diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..4554801
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,42 @@
+{
+ // Use IntelliSense to learn about possible attributes.
+ // Hover to view descriptions of existing attributes.
+ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+ "version": "0.2.0",
+ "configurations": [
+ {
+ "name": "test",
+ "type": "python",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/main.py",
+ "console": "integratedTerminal",
+ "justMyCode": false,
+ "autoReload": {"enable": true,},
+ "env": {"WANDB_MODE":"disabled"},
+ "args": [
+ // "'wandb.mode=disabled",
+ // "env.train.id=BreakoutNoFrameskip-v4",
+ "env.train.id=CrafterReward-v1",
+ // # make it start early
+ "training.tokenizer.start_after_epochs=1",
+ "training.world_model.start_after_epochs=2",
+ "training.actor_critic.start_after_epochs=3",
+ "training.tokenizer.steps_per_epoch=10",
+ "training.world_model.steps_per_epoch=10",
+ "training.actor_critic.steps_per_epoch=10",
+ ]
+ },
+ {
+ "name": "main",
+ "type": "python",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/main.py",
+ "console": "integratedTerminal",
+ "justMyCode": false,
+ "autoReload": {"enable": true,},
+ "args": [
+ "env.train.id=CrafterReward-v1",
+ ]
+ }
+ ]
+}
diff --git a/README.md b/README.md
index 7ce5350..a39d2ea 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,19 @@ See also:
- [AdaVAE](https://github.com/ImKeTT/AdaVAE)
- [bigvae](https://github.com/JD-P/minihf/blob/adavae-moe/vae_infer.py)
+
+A fork of IRIS where I use a pretrained LLM as the tranformer (with LoRa).
+
+My hypothesis: Pretrained LLM's make good world models by including a lot of world information!
+
+details:
+- for speed and cost I use a small 1.5B model. But it would be interesting to try a 7B one
+- for speed I use a smaller actor critic than in IRIS
+- max_blocks 20->10
+- batch smaller because of my small machine
+- actor_critic.steps_per_epoch 200->20
+- world_model.batch_num_sampler; 64->8 because the forzen transformer uses lots of gpu ram
+
# Transformers are Sample-Efficient World Models (IRIS)
[Transformers are Sample-Efficient World Models](https://openreview.net/forum?id=vhFu1Acb0xb)
diff --git a/config/actor_critic/default.yaml b/config/actor_critic/default.yaml
index e4e2eee..69b1ea8 100644
--- a/config/actor_critic/default.yaml
+++ b/config/actor_critic/default.yaml
@@ -1 +1,2 @@
use_original_obs: False
+lstm_dim: 512
diff --git a/config/datasets/default.yaml b/config/datasets/default.yaml
index c959c98..de8d15a 100644
--- a/config/datasets/default.yaml
+++ b/config/datasets/default.yaml
@@ -1,8 +1,8 @@
train:
- _target_: dataset.EpisodesDatasetRamMonitoring
+ _target_: src.dataset.EpisodesDatasetRamMonitoring
max_ram_usage: 30G
name: train_dataset
test:
- _target_: dataset.EpisodesDataset
+ _target_: src.dataset.EpisodesDataset
max_num_episodes: null
name: test_dataset
diff --git a/config/env/default.yaml b/config/env/default.yaml
index f6b3b89..dc885ba 100644
--- a/config/env/default.yaml
+++ b/config/env/default.yaml
@@ -1,5 +1,5 @@
train:
- _target_: envs.make_atari
+ _target_: src.envs.make_env
id: null
size: 64
max_episode_steps: 20000
@@ -18,4 +18,4 @@ test:
done_on_life_loss: False
clip_reward: False
-keymap: atari/${.train.id}
\ No newline at end of file
+keymap: atari/${.train.id}
diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index 54a4dd7..9029ef4 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -1,14 +1,14 @@
-_target_: models.tokenizer.Tokenizer
+_target_: src.models.tokenizer.Tokenizer
-vocab_size: 512
-embed_dim: 512
+vocab_size: ${..world_model.vocab_size}
+embed_dim: ${..world_model.embed_dim}
encoder:
- _target_: models.tokenizer.Encoder
+ _target_: src.models.tokenizer.Encoder
config:
- _target_: models.tokenizer.EncoderDecoderConfig
+ _target_: src.models.tokenizer.EncoderDecoderConfig
resolution: 64
in_channels: 3
- z_channels: 512
+ z_channels: ${...vocab_size}
ch: 64
ch_mult: [1, 1, 1, 1, 1]
num_res_blocks: 2
@@ -16,5 +16,5 @@ encoder:
out_ch: 3
dropout: 0.0
decoder:
- _target_: models.tokenizer.Decoder
- config: ${..encoder.config}
\ No newline at end of file
+ _target_: src.models.tokenizer.Decoder
+ config: ${..encoder.config}
diff --git a/config/trainer.yaml b/config/trainer.yaml
index 9e14d17..3539b2e 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -54,24 +54,24 @@ training:
should: True
learning_rate: 0.0001
tokenizer:
- batch_num_samples: 256
+ batch_num_samples: 128
grad_acc_steps: 1
max_grad_norm: 10.0
start_after_epochs: 5
steps_per_epoch: 200
world_model:
- batch_num_samples: 64
- grad_acc_steps: 1
+ batch_num_samples: 8 # pretrained models use lots of ram
+ grad_acc_steps: 2
max_grad_norm: 10.0
weight_decay: 0.01
start_after_epochs: 25
steps_per_epoch: 200
actor_critic:
- batch_num_samples: 64
+ batch_num_samples: 16
grad_acc_steps: 1
max_grad_norm: 10.0
- start_after_epochs: 50
- steps_per_epoch: 200
+ start_after_epochs: 300
+ steps_per_epoch: 40
imagine_horizon: ${common.sequence_length}
burn_in: 20
gamma: 0.995
@@ -92,3 +92,7 @@ evaluation:
num_episodes_to_save: ${training.actor_critic.batch_num_samples}
horizon: ${training.actor_critic.imagine_horizon}
start_after_epochs: ${training.actor_critic.start_after_epochs}
+
+hydra:
+ job:
+ chdir: True
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index 7b70c6a..2b631a9 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -1,2 +1,8 @@
-_target_: models.BigVAEConfig
-tokens_per_block: 17
+_target_: src.models.TransformerConfig
+max_blocks: 10 # this is the rollout length when training policy
+tokens_per_block: 17 # how much info we can encode
+dropout: 0.1
+rank: 32 # lora rank
+model_name: "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
+vocab_size: 32000 # change to llm vocab dim
+embed_dim: 2048 # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
diff --git a/img/2023-11-16-13-01-11.png b/img/2023-11-16-13-01-11.png
new file mode 100644
index 0000000..00f3a57
Binary files /dev/null and b/img/2023-11-16-13-01-11.png differ
diff --git a/justfile b/justfile
new file mode 100644
index 0000000..5f2f184
--- /dev/null
+++ b/justfile
@@ -0,0 +1,29 @@
+set shell := ["zsh", "-cu"]
+
+breakout:
+ python src/main.py env.train.id=BreakoutNoFrameskip-v4
+
+crafter:
+ python src/main.py env.train.id=CrafterReward-v1
+
+# minihack:
+# python src/main.py env.train.id=MiniHack-River-v0
+
+# watch the latest runs
+watch_latest:
+ . ./.venv/bin/activate
+ cd ./outputs && \
+ cd *([-1]) && \
+ cd *([-1]) && \
+ scripts/play.sh -e -r -h
+
+
+resume_latest:
+ . ./.venv/bin/activate
+ cd ./outputs && \
+ cd *([-1]) && \
+ cd *([-1]) && \
+ scripts/resume.sh
+
+default:
+ just --list
diff --git a/notebooks/01_debug_models.ipynb b/notebooks/01_debug_models.ipynb
new file mode 100644
index 0000000..8c14e02
--- /dev/null
+++ b/notebooks/01_debug_models.ipynb
@@ -0,0 +1,747 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# autoreload import your package\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import gym\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.style.use('ggplot')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Debug model components\n",
+ "\n",
+ "### Using trainer? :poop:\n",
+ "\n",
+ "Hyrda is really annoying\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n",
+ "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'wandb': {'mode': 'disabled', 'project': 'iris', 'entity': None, 'name': None, 'group': None, 'tags': None, 'notes': None}, 'initialization': {'path_to_checkpoint': None, 'load_tokenizer': False, 'load_world_model': False, 'load_actor_critic': False}, 'common': {'epochs': 600, 'device': 'cuda:0', 'do_checkpoint': False, 'seed': 0, 'sequence_length': '${world_model.max_blocks}', 'resume': True}, 'collection': {'train': {'num_envs': 1, 'stop_after_epochs': 500, 'num_episodes_to_save': 10, 'config': {'epsilon': 0.01, 'should_sample': True, 'temperature': 1.0, 'num_steps': 200, 'burn_in': '${training.actor_critic.burn_in}'}}, 'test': {'num_envs': 8, 'num_episodes_to_save': '${collection.train.num_episodes_to_save}', 'config': {'epsilon': 0.0, 'should_sample': True, 'temperature': 0.5, 'num_episodes': 16, 'burn_in': '${training.actor_critic.burn_in}'}}}, 'training': {'should': True, 'learning_rate': 0.0001, 'tokenizer': {'batch_num_samples': 128, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'start_after_epochs': 1, 'steps_per_epoch': 10}, 'world_model': {'batch_num_samples': 4, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'weight_decay': 0.01, 'start_after_epochs': 1, 'steps_per_epoch': 10}, 'actor_critic': {'batch_num_samples': 4, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'start_after_epochs': 1, 'steps_per_epoch': 10, 'imagine_horizon': '${common.sequence_length}', 'burn_in': 20, 'gamma': 0.995, 'lambda_': 0.95, 'entropy_weight': 0.001}}, 'evaluation': {'should': True, 'every': 5, 'tokenizer': {'batch_num_samples': '${training.tokenizer.batch_num_samples}', 'start_after_epochs': '${training.tokenizer.start_after_epochs}', 'save_reconstructions': True}, 'world_model': {'batch_num_samples': '${training.world_model.batch_num_samples}', 'start_after_epochs': '${training.world_model.start_after_epochs}'}, 'actor_critic': {'num_episodes_to_save': '${training.actor_critic.batch_num_samples}', 'horizon': '${training.actor_critic.imagine_horizon}', 'start_after_epochs': '${training.actor_critic.start_after_epochs}'}}, 'tokenizer': {'_target_': 'src.models.tokenizer.Tokenizer', 'vocab_size': 2048, 'embed_dim': 2048, 'encoder': {'_target_': 'src.models.tokenizer.Encoder', 'config': {'_target_': 'src.models.tokenizer.EncoderDecoderConfig', 'resolution': 64, 'in_channels': 3, 'z_channels': 2048, 'ch': 64, 'ch_mult': [1, 1, 1, 1, 1], 'num_res_blocks': 2, 'attn_resolutions': [8, 16], 'out_ch': 3, 'dropout': 0.0}}, 'decoder': {'_target_': 'src.models.tokenizer.Decoder', 'config': '${..encoder.config}'}}, 'world_model': {'_target_': 'src.models.TransformerConfig', 'max_blocks': 10, 'num_layers': 1, 'num_heads': 1, 'embed_dim': 2048, 'dropout': 0.1, 'model_name': 'PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T', 'rank': 32, 'tokens_per_block': 17}, 'actor_critic': {'use_original_obs': False, 'lstm_dim': 512}, 'env': {'train': {'_target_': 'src.envs.make_env', 'id': 'CrafterReward-v1', 'size': 64, 'max_episode_steps': 20000, 'noop_max': 30, 'frame_skip': 4, 'done_on_life_loss': True, 'clip_reward': False}, 'test': {'_target_': '${..train._target_}', 'id': '${..train.id}', 'size': '${..train.size}', 'max_episode_steps': 108000, 'noop_max': 1, 'frame_skip': '${..train.frame_skip}', 'done_on_life_loss': False, 'clip_reward': False}, 'keymap': 'atari/${.train.id}'}, 'datasets': {'train': {'_target_': 'src.dataset.EpisodesDatasetRamMonitoring', 'max_ram_usage': '30G', 'name': 'train_dataset'}, 'test': {'_target_': 'src.dataset.EpisodesDataset', 'max_num_episodes': None, 'name': 'test_dataset'}}}\n",
+ "Tokenizer : shape of latent is (2048, 4, 4).\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+ " warnings.warn(\n",
+ "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n",
+ "Using pad_token, but it is not set yet.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "trainable params: 50,462,720 || all params: 1,150,511,104 || trainable%: 4.386113252149889\n",
+ "None\n",
+ "32314243 parameters in agent.tokenizer\n",
+ "752979973 parameters in agent.world_model\n",
+ "3224626 parameters in agent.actor_critic\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import os\n",
+ "os.environ['WANDB_MODE'] = 'disabled'\n",
+ "\n",
+ "import hydra\n",
+ "from hydra import initialize, initialize_config_module, initialize_config_dir, compose\n",
+ "from omegaconf import OmegaConf\n",
+ "\n",
+ "from pathlib import Path\n",
+ "from datetime import datetime\n",
+ "\n",
+ "from src.trainer import Trainer\n",
+ "\n",
+ "\n",
+ "class Trainer2(Trainer):\n",
+ " \n",
+ " def load_checkpoint(self, *args, **kwargs):\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "\n",
+ "ts = datetime.now().strftime(\"%Y-%m-%d/%H-%M-%S\")\n",
+ "run_dir = Path(f\"..outputs/{ts}\").absolute()\n",
+ "run_dir.mkdir(parents=True, exist_ok=True)\n",
+ "abs_config_dir=os.path.abspath(\"../config\")\n",
+ "os.chdir(run_dir)\n",
+ "# with initialize_config_dir(version_base=None, config_dir=abs_config_dir):\n",
+ "with initialize(version_base=None, config_path=\"../config\"):\n",
+ " cfg = compose(config_name='trainer', overrides=[\n",
+ " f'hydra.run.dir={run_dir}',\n",
+ " # f\"initialization.path_to_checkpoint={str(path_to_checkpoint.absolute())}\",\n",
+ " 'wandb.mode=disabled',\n",
+ " \"env.train.id=CrafterReward-v1\",\n",
+ " \"training.tokenizer.start_after_epochs=1\",\n",
+ " \"training.world_model.start_after_epochs=1\",\n",
+ " \"training.actor_critic.start_after_epochs=1\",\n",
+ " \"training.tokenizer.steps_per_epoch=10\",\n",
+ " \"training.world_model.steps_per_epoch=10\",\n",
+ " \"training.actor_critic.steps_per_epoch=10\",\n",
+ " \"common.do_checkpoint=False\",\n",
+ " \"common.resume=True\",\n",
+ " \"training.world_model.batch_num_samples=4\",\n",
+ " \"training.actor_critic.batch_num_samples=4\",\n",
+ " ])\n",
+ " print(cfg)\n",
+ "\n",
+ " with run_dir:\n",
+ " Path('media/episodes/train').mkdir(parents=True, exist_ok=True)\n",
+ " trainer = Trainer2(cfg)\n",
+ " trainer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Trainer train_agent\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Experience collection (train_dataset): 100%|██████████| 200/200 [00:03<00:00, 58.00it/s]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'train_dataset/episode_length': 183,\n",
+ " 'train_dataset/episode_return': tensor(0.1000),\n",
+ " 'train_dataset/episode_num': 0,\n",
+ " 'train_dataset/action_histogram': },\n",
+ " {'train_dataset/#episodes': 2,\n",
+ " 'train_dataset/#steps': 200,\n",
+ " 'train_dataset/return': 0.100000024}]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "self=trainer\n",
+ "epoch = 52\n",
+ "\n",
+ "# get out first exp\n",
+ "self.train_collector.collect(self.agent, epoch, **self.cfg.collection.train.config)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "self.agent.train()\n",
+ "self.agent.zero_grad()\n",
+ "\n",
+ "metrics_tokenizer, metrics_world_model, metrics_actor_critic = {}, {}, {}\n",
+ "\n",
+ "cfg_tokenizer = self.cfg.training.tokenizer\n",
+ "cfg_world_model = self.cfg.training.world_model\n",
+ "cfg_actor_critic = self.cfg.training.actor_critic\n",
+ "\n",
+ "# if epoch > cfg_tokenizer.start_after_epochs:\n",
+ "# metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)\n",
+ "# self.agent.tokenizer.eval()\n",
+ "\n",
+ "# if epoch > cfg_world_model.start_after_epochs:\n",
+ "# metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)\n",
+ "# self.agent.world_model.eval()\n",
+ "\n",
+ "# if epoch > cfg_actor_critic.start_after_epochs:\n",
+ "# metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training.actor_critic.burn_in, sample_from_start=False, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)\n",
+ "# self.agent.actor_critic.eval()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from torchinfo import summary\n",
+ "import torch\n",
+ "from einops import rearrange\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Directly benchmark models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer = self.agent.tokenizer\n",
+ "world_model = self.agent.world_model\n",
+ "actor_critic = self.agent.actor_critic\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch_num_samples = cfg.training.world_model.batch_num_samples\n",
+ "sequence_length = cfg.common.sequence_length\n",
+ "sample_from_start = False\n",
+ "# train_dataset = instantiate(cfg.datasets.train)\n",
+ "batch_num_samples\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch = self.train_dataset.sample_batch(batch_num_samples, sequence_length, sample_from_start)\n",
+ "batch = {k: v.to(self.device) for k, v in batch.items()}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 190 ms, sys: 4.87 ms, total: 194 ms\n",
+ "Wall time: 195 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "self.agent.world_model.compute_loss(batch, tokenizer=self.agent.tokenizer)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 9.35 s, sys: 17.3 ms, total: 9.37 s\n",
+ "Wall time: 9.37 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# TODO: why is this so slow?\n",
+ "cfg_actor_critic = self.cfg.training.actor_critic\n",
+ "self.agent.actor_critic.compute_loss(batch, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 9.11 s, sys: 15.5 ms, total: 9.13 s\n",
+ "Wall time: 9.13 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# is this the slow part... yes. damn\n",
+ "actor_critic.imagine(batch, tokenizer, world_model, horizon=10);\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # takes 0.1 s, fast\n",
+ "# wm_env = WorldModelEnv(tokenizer, world_model, device)\n",
+ "# wm_env\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([4, 3, 64, 64])\n",
+ "CPU times: user 105 ms, sys: 207 µs, total: 105 ms\n",
+ "Wall time: 105 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# this takes 0.1 seconds and is run 10+ time. So 1 second. Hmm\n",
+ "from src.envs.world_model_env import WorldModelEnv, Categorical\n",
+ "initial_observations = batch['observations']\n",
+ "\n",
+ "# get the right obs\n",
+ "wm_env = WorldModelEnv(self.agent.tokenizer, self.agent.world_model, self.device)\n",
+ "obs = wm_env.reset_from_initial_observations(initial_observations[:, -1])\n",
+ "print(obs.shape)\n",
+ "\n",
+ "\n",
+ "# make sure hidden states are right\n",
+ "self.agent.actor_critic.reset(obs.shape[0])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gc\n",
+ "gc.collect()\n",
+ "torch.cuda.empty_cache()\n",
+ "# obs\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.6 ms, sys: 309 µs, total: 1.9 ms\n",
+ "Wall time: 1.72 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# 700us\n",
+ "# fast, executed 10+ times\n",
+ "outputs_ac = actor_critic(obs)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4, 1, 17])"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outputs_ac.logits_actions.shape\n",
+ "# action_token.shape\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %%timeit\n",
+ "# slow! takes 1s, executed 10+ times this is the culprit, not the lstm. hmm\n",
+ "k=3\n",
+ "horizon = 6\n",
+ "action_token = Categorical(logits=outputs_ac.logits_actions).sample()\n",
+ "obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "66.5 ms ± 1.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "# 62ms\n",
+ "# this is the slow part again. no grad and eval don't hepl\n",
+ "outputs_wm = world_model(action_token, past_keys_values=wm_env.keys_values_wm)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "num_steps=1\n",
+ "prev_steps=0\n",
+ "sequences = world_model.embedder(action_token, num_steps, prev_steps) + world_model.pos_emb(prev_steps + torch.arange(num_steps, device=action_token.device))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/notebooks/01_debug_models.ipynb Cell 24\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m get_ipython()\u001b[39m.\u001b[39;49mrun_cell_magic(\u001b[39m'\u001b[39;49m\u001b[39mtimeit\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m# ofc it\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the transformer that\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms slow. I guess we just call it was more than during training\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mpast_keys_values = wm_env.keys_values_wm\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mx = world_model.transformer(sequences, past_keys_values)\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m\"\u001b[39;49m)\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2515\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2513\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2514\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2515\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2517\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2518\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2519\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/magics/execution.py:1189\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39mif\u001b[39;00m time_number \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m0.2\u001b[39m:\n\u001b[1;32m 1187\u001b[0m \u001b[39mbreak\u001b[39;00m\n\u001b[0;32m-> 1189\u001b[0m all_runs \u001b[39m=\u001b[39m timer\u001b[39m.\u001b[39;49mrepeat(repeat, number)\n\u001b[1;32m 1190\u001b[0m best \u001b[39m=\u001b[39m \u001b[39mmin\u001b[39m(all_runs) \u001b[39m/\u001b[39m number\n\u001b[1;32m 1191\u001b[0m worst \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(all_runs) \u001b[39m/\u001b[39m number\n",
+ "File \u001b[0;32m~/miniforge3/lib/python3.9/timeit.py:205\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m 203\u001b[0m r \u001b[39m=\u001b[39m []\n\u001b[1;32m 204\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(repeat):\n\u001b[0;32m--> 205\u001b[0m t \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeit(number)\n\u001b[1;32m 206\u001b[0m r\u001b[39m.\u001b[39mappend(t)\n\u001b[1;32m 207\u001b[0m \u001b[39mreturn\u001b[39;00m r\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/magics/execution.py:173\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m 171\u001b[0m gc\u001b[39m.\u001b[39mdisable()\n\u001b[1;32m 172\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 173\u001b[0m timing \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minner(it, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimer)\n\u001b[1;32m 174\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 175\u001b[0m \u001b[39mif\u001b[39;00m gcold:\n",
+ "File \u001b[0;32m:3\u001b[0m, in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/transformer.py:69\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[0;34m(self, sequences, past_keys_values)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[39m# k_size = (x.shape[0], x.shape[1], x.shape[1], 1)\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[39m# v_size = past_keys_values[0]._v_cache._cache.size()\u001b[39;00m\n\u001b[1;32m 68\u001b[0m v_size \u001b[39m=\u001b[39m (k_size[\u001b[39m0\u001b[39m], k_size[\u001b[39m1\u001b[39m], x\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], k_size[\u001b[39m3\u001b[39m])\n\u001b[0;32m---> 69\u001b[0m past_keys_values[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mupdate(torch\u001b[39m.\u001b[39;49mrand(v_size), torch\u001b[39m.\u001b[39;49mrand(v_size))\n\u001b[1;32m 70\u001b[0m \u001b[39mreturn\u001b[39;00m x\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/kv_caching.py:59\u001b[0m, in \u001b[0;36mKVCache.update\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, k: torch\u001b[39m.\u001b[39mTensor, v: torch\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m---> 59\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_k_cache\u001b[39m.\u001b[39;49mupdate(k)\n\u001b[1;32m 60\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_v_cache\u001b[39m.\u001b[39mupdate(v)\n",
+ "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/kv_caching.py:33\u001b[0m, in \u001b[0;36mCache.update\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, x: torch\u001b[39m.\u001b[39mTensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 32\u001b[0m \u001b[39massert\u001b[39;00m (x\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39mndim) \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m([x\u001b[39m.\u001b[39msize(i) \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39msize(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m (\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m3\u001b[39m)])\n\u001b[0;32m---> 33\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n\u001b[1;32m 34\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache \u001b[39m=\u001b[39m AssignWithoutInplaceCheck\u001b[39m.\u001b[39mapply(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache, x, \u001b[39m2\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m))\n\u001b[1;32m 35\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m)\n",
+ "\u001b[0;31mAssertionError\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "# ofc it's the transformer that's slow. I guess we just call it was more than during training\n",
+ "past_keys_values = wm_env.keys_values_wm\n",
+ "x = world_model.transformer(sequences, past_keys_values)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# past_keys_values = wm_env.keys_values_wm\n",
+ "# x = world_model.transformer(sequences, past_keys_values)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%timeit\n",
+ "# ofc it's the transformer that's slow. I guess we just call it was more than during training\n",
+ "past_keys_values = wm_env.keys_values_wm\n",
+ "x = world_model.transformer(sequences, past_keys_values)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logits_observations = world_model.head_observations(x, num_steps=num_steps, prev_steps=prev_steps)\n",
+ "logits_rewards = world_model.head_rewards(x, num_steps=num_steps, prev_steps=prev_steps)\n",
+ "logits_ends = world_model.head_ends(x, num_steps=num_steps, prev_steps=prev_steps)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Torchinfo model sizes\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "observations = self.agent.tokenizer.preprocess_input(rearrange(batch['observations'], 'b t c h w -> (b t) c h w'))\n",
+ "# z, z_quantized, reconstructions = self.agent.tokenizer(observations, should_preprocess=False, should_postprocess=False)\n",
+ "summary(self.agent.tokenizer, input_data=observations)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " obs_tokens = self.agent.tokenizer.encode(batch['observations'], should_preprocess=True).tokens # (BL, K)\n",
+ "\n",
+ "act_tokens = rearrange(batch['actions'], 'b l -> b l 1')\n",
+ "tokens = rearrange(torch.cat((obs_tokens, act_tokens), dim=2), 'b l k1 -> b (l k1)') # \n",
+ "\n",
+ "summary(self.agent.world_model, input_data=tokens)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from src.envs.world_model_env import WorldModelEnv\n",
+ "initial_observations = batch['observations']\n",
+ "\n",
+ "# get the right obs\n",
+ "wm_env = WorldModelEnv(self.agent.tokenizer, self.agent.world_model, self.device)\n",
+ "obs = wm_env.reset_from_initial_observations(initial_observations[:, -1])\n",
+ "obs.shape\n",
+ "\n",
+ "\n",
+ "# make sure hidden states are right\n",
+ "self.agent.actor_critic.reset(obs.shape[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "from torchinfo import summary\n",
+ "summary(self.agent.actor_critic, input_data=obs)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Debug env\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import minihack\n",
+ "env = gym.make(\"MiniHack-River-v0\", observation_keys=(\"pixel_crop\", \"pixel\", 'blstats', 'message'))\n",
+ "env.reset() # each reset generates a new environment instance\n",
+ "obs, reward, end, info = env.step(1) # move agent '@' north\n",
+ "print(obs['pixel_crop'].shape)\n",
+ "plt.imshow(obs['pixel_crop'])\n",
+ "plt.show()\n",
+ "\n",
+ "print(obs['pixel'].shape)\n",
+ "plt.imshow(obs['pixel'])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # plt.imshow(obs['glyphs_crop'])\n",
+ "# obs['glyphs_crop'].shape\n",
+ "# obs['blstats']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import minihack\n",
+ "env = gym.make(\"MiniHack-Room-5x5-v0\", observation_keys=(\"pixel_crop\", \"pixel\", 'blstats', 'message'))\n",
+ "env.reset() # each reset generates a new environment instance\n",
+ "obs, reward, end, info = env.step(1) # move agent '@' north\n",
+ "print(obs['pixel_crop'].shape)\n",
+ "plt.imshow(obs['pixel_crop'])\n",
+ "plt.show()\n",
+ "\n",
+ "print(obs['pixel'].shape)\n",
+ "plt.imshow(obs['pixel'])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import minihack\n",
+ "import crafter\n",
+ "env = gym.make(\"CrafterReward-v1\")\n",
+ "env.reset() # each reset generates a new environment instance\n",
+ "obs, reward, end, info = env.step(1) # move agent '@' north\n",
+ "print(obs.shape)\n",
+ "plt.imshow(obs)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index ade2c0f..c67ea9c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -29,163 +29,48 @@ test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
test-trackers = ["comet-ml", "tensorboard", "wandb"]
testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
-[[package]]
-name = "aiohttp"
-version = "3.8.6"
-description = "Async http client/server framework (asyncio)"
-optional = false
-python-versions = ">=3.6"
-files = [
- {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"},
- {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"},
- {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:253bf92b744b3170eb4c4ca2fa58f9c4b87aeb1df42f71d4e78815e6e8b73c9e"},
- {file = "aiohttp-3.8.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3fd194939b1f764d6bb05490987bfe104287bbf51b8d862261ccf66f48fb4096"},
- {file = "aiohttp-3.8.6-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6c5f938d199a6fdbdc10bbb9447496561c3a9a565b43be564648d81e1102ac22"},
- {file = "aiohttp-3.8.6-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2817b2f66ca82ee699acd90e05c95e79bbf1dc986abb62b61ec8aaf851e81c93"},
- {file = "aiohttp-3.8.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fa375b3d34e71ccccf172cab401cd94a72de7a8cc01847a7b3386204093bb47"},
- {file = "aiohttp-3.8.6-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9de50a199b7710fa2904be5a4a9b51af587ab24c8e540a7243ab737b45844543"},
- {file = "aiohttp-3.8.6-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e1d8cb0b56b3587c5c01de3bf2f600f186da7e7b5f7353d1bf26a8ddca57f965"},
- {file = "aiohttp-3.8.6-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:8e31e9db1bee8b4f407b77fd2507337a0a80665ad7b6c749d08df595d88f1cf5"},
- {file = "aiohttp-3.8.6-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:7bc88fc494b1f0311d67f29fee6fd636606f4697e8cc793a2d912ac5b19aa38d"},
- {file = "aiohttp-3.8.6-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ec00c3305788e04bf6d29d42e504560e159ccaf0be30c09203b468a6c1ccd3b2"},
- {file = "aiohttp-3.8.6-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:ad1407db8f2f49329729564f71685557157bfa42b48f4b93e53721a16eb813ed"},
- {file = "aiohttp-3.8.6-cp310-cp310-win32.whl", hash = "sha256:ccc360e87341ad47c777f5723f68adbb52b37ab450c8bc3ca9ca1f3e849e5fe2"},
- {file = "aiohttp-3.8.6-cp310-cp310-win_amd64.whl", hash = "sha256:93c15c8e48e5e7b89d5cb4613479d144fda8344e2d886cf694fd36db4cc86865"},
- {file = "aiohttp-3.8.6-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6e2f9cc8e5328f829f6e1fb74a0a3a939b14e67e80832975e01929e320386b34"},
- {file = "aiohttp-3.8.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e6a00ffcc173e765e200ceefb06399ba09c06db97f401f920513a10c803604ca"},
- {file = "aiohttp-3.8.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:41bdc2ba359032e36c0e9de5a3bd00d6fb7ea558a6ce6b70acedf0da86458321"},
- {file = "aiohttp-3.8.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:14cd52ccf40006c7a6cd34a0f8663734e5363fd981807173faf3a017e202fec9"},
- {file = "aiohttp-3.8.6-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2d5b785c792802e7b275c420d84f3397668e9d49ab1cb52bd916b3b3ffcf09ad"},
- {file = "aiohttp-3.8.6-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1bed815f3dc3d915c5c1e556c397c8667826fbc1b935d95b0ad680787896a358"},
- {file = "aiohttp-3.8.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96603a562b546632441926cd1293cfcb5b69f0b4159e6077f7c7dbdfb686af4d"},
- {file = "aiohttp-3.8.6-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d76e8b13161a202d14c9584590c4df4d068c9567c99506497bdd67eaedf36403"},
- {file = "aiohttp-3.8.6-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e3f1e3f1a1751bb62b4a1b7f4e435afcdade6c17a4fd9b9d43607cebd242924a"},
- {file = "aiohttp-3.8.6-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:76b36b3124f0223903609944a3c8bf28a599b2cc0ce0be60b45211c8e9be97f8"},
- {file = "aiohttp-3.8.6-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:a2ece4af1f3c967a4390c284797ab595a9f1bc1130ef8b01828915a05a6ae684"},
- {file = "aiohttp-3.8.6-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:16d330b3b9db87c3883e565340d292638a878236418b23cc8b9b11a054aaa887"},
- {file = "aiohttp-3.8.6-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:42c89579f82e49db436b69c938ab3e1559e5a4409eb8639eb4143989bc390f2f"},
- {file = "aiohttp-3.8.6-cp311-cp311-win32.whl", hash = "sha256:efd2fcf7e7b9d7ab16e6b7d54205beded0a9c8566cb30f09c1abe42b4e22bdcb"},
- {file = "aiohttp-3.8.6-cp311-cp311-win_amd64.whl", hash = "sha256:3b2ab182fc28e7a81f6c70bfbd829045d9480063f5ab06f6e601a3eddbbd49a0"},
- {file = "aiohttp-3.8.6-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:fdee8405931b0615220e5ddf8cd7edd8592c606a8e4ca2a00704883c396e4479"},
- {file = "aiohttp-3.8.6-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d25036d161c4fe2225d1abff2bd52c34ed0b1099f02c208cd34d8c05729882f0"},
- {file = "aiohttp-3.8.6-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5d791245a894be071d5ab04bbb4850534261a7d4fd363b094a7b9963e8cdbd31"},
- {file = "aiohttp-3.8.6-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0cccd1de239afa866e4ce5c789b3032442f19c261c7d8a01183fd956b1935349"},
- {file = "aiohttp-3.8.6-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f13f60d78224f0dace220d8ab4ef1dbc37115eeeab8c06804fec11bec2bbd07"},
- {file = "aiohttp-3.8.6-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8a9b5a0606faca4f6cc0d338359d6fa137104c337f489cd135bb7fbdbccb1e39"},
- {file = "aiohttp-3.8.6-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:13da35c9ceb847732bf5c6c5781dcf4780e14392e5d3b3c689f6d22f8e15ae31"},
- {file = "aiohttp-3.8.6-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:4d4cbe4ffa9d05f46a28252efc5941e0462792930caa370a6efaf491f412bc66"},
- {file = "aiohttp-3.8.6-cp36-cp36m-musllinux_1_1_ppc64le.whl", hash = "sha256:229852e147f44da0241954fc6cb910ba074e597f06789c867cb7fb0621e0ba7a"},
- {file = "aiohttp-3.8.6-cp36-cp36m-musllinux_1_1_s390x.whl", hash = "sha256:713103a8bdde61d13490adf47171a1039fd880113981e55401a0f7b42c37d071"},
- {file = "aiohttp-3.8.6-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:45ad816b2c8e3b60b510f30dbd37fe74fd4a772248a52bb021f6fd65dff809b6"},
- {file = "aiohttp-3.8.6-cp36-cp36m-win32.whl", hash = "sha256:2b8d4e166e600dcfbff51919c7a3789ff6ca8b3ecce16e1d9c96d95dd569eb4c"},
- {file = "aiohttp-3.8.6-cp36-cp36m-win_amd64.whl", hash = "sha256:0912ed87fee967940aacc5306d3aa8ba3a459fcd12add0b407081fbefc931e53"},
- {file = "aiohttp-3.8.6-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e2a988a0c673c2e12084f5e6ba3392d76c75ddb8ebc6c7e9ead68248101cd446"},
- {file = "aiohttp-3.8.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ebf3fd9f141700b510d4b190094db0ce37ac6361a6806c153c161dc6c041ccda"},
- {file = "aiohttp-3.8.6-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3161ce82ab85acd267c8f4b14aa226047a6bee1e4e6adb74b798bd42c6ae1f80"},
- {file = "aiohttp-3.8.6-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d95fc1bf33a9a81469aa760617b5971331cdd74370d1214f0b3109272c0e1e3c"},
- {file = "aiohttp-3.8.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c43ecfef7deaf0617cee936836518e7424ee12cb709883f2c9a1adda63cc460"},
- {file = "aiohttp-3.8.6-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca80e1b90a05a4f476547f904992ae81eda5c2c85c66ee4195bb8f9c5fb47f28"},
- {file = "aiohttp-3.8.6-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:90c72ebb7cb3a08a7f40061079817133f502a160561d0675b0a6adf231382c92"},
- {file = "aiohttp-3.8.6-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:bb54c54510e47a8c7c8e63454a6acc817519337b2b78606c4e840871a3e15349"},
- {file = "aiohttp-3.8.6-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:de6a1c9f6803b90e20869e6b99c2c18cef5cc691363954c93cb9adeb26d9f3ae"},
- {file = "aiohttp-3.8.6-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:a3628b6c7b880b181a3ae0a0683698513874df63783fd89de99b7b7539e3e8a8"},
- {file = "aiohttp-3.8.6-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:fc37e9aef10a696a5a4474802930079ccfc14d9f9c10b4662169671ff034b7df"},
- {file = "aiohttp-3.8.6-cp37-cp37m-win32.whl", hash = "sha256:f8ef51e459eb2ad8e7a66c1d6440c808485840ad55ecc3cafefadea47d1b1ba2"},
- {file = "aiohttp-3.8.6-cp37-cp37m-win_amd64.whl", hash = "sha256:b2fe42e523be344124c6c8ef32a011444e869dc5f883c591ed87f84339de5976"},
- {file = "aiohttp-3.8.6-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:9e2ee0ac5a1f5c7dd3197de309adfb99ac4617ff02b0603fd1e65b07dc772e4b"},
- {file = "aiohttp-3.8.6-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:01770d8c04bd8db568abb636c1fdd4f7140b284b8b3e0b4584f070180c1e5c62"},
- {file = "aiohttp-3.8.6-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3c68330a59506254b556b99a91857428cab98b2f84061260a67865f7f52899f5"},
- {file = "aiohttp-3.8.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89341b2c19fb5eac30c341133ae2cc3544d40d9b1892749cdd25892bbc6ac951"},
- {file = "aiohttp-3.8.6-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:71783b0b6455ac8f34b5ec99d83e686892c50498d5d00b8e56d47f41b38fbe04"},
- {file = "aiohttp-3.8.6-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f628dbf3c91e12f4d6c8b3f092069567d8eb17814aebba3d7d60c149391aee3a"},
- {file = "aiohttp-3.8.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b04691bc6601ef47c88f0255043df6f570ada1a9ebef99c34bd0b72866c217ae"},
- {file = "aiohttp-3.8.6-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ee912f7e78287516df155f69da575a0ba33b02dd7c1d6614dbc9463f43066e3"},
- {file = "aiohttp-3.8.6-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:9c19b26acdd08dd239e0d3669a3dddafd600902e37881f13fbd8a53943079dbc"},
- {file = "aiohttp-3.8.6-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:99c5ac4ad492b4a19fc132306cd57075c28446ec2ed970973bbf036bcda1bcc6"},
- {file = "aiohttp-3.8.6-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:f0f03211fd14a6a0aed2997d4b1c013d49fb7b50eeb9ffdf5e51f23cfe2c77fa"},
- {file = "aiohttp-3.8.6-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:8d399dade330c53b4106160f75f55407e9ae7505263ea86f2ccca6bfcbdb4921"},
- {file = "aiohttp-3.8.6-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ec4fd86658c6a8964d75426517dc01cbf840bbf32d055ce64a9e63a40fd7b771"},
- {file = "aiohttp-3.8.6-cp38-cp38-win32.whl", hash = "sha256:33164093be11fcef3ce2571a0dccd9041c9a93fa3bde86569d7b03120d276c6f"},
- {file = "aiohttp-3.8.6-cp38-cp38-win_amd64.whl", hash = "sha256:bdf70bfe5a1414ba9afb9d49f0c912dc524cf60141102f3a11143ba3d291870f"},
- {file = "aiohttp-3.8.6-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:d52d5dc7c6682b720280f9d9db41d36ebe4791622c842e258c9206232251ab2b"},
- {file = "aiohttp-3.8.6-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4ac39027011414dbd3d87f7edb31680e1f430834c8cef029f11c66dad0670aa5"},
- {file = "aiohttp-3.8.6-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3f5c7ce535a1d2429a634310e308fb7d718905487257060e5d4598e29dc17f0b"},
- {file = "aiohttp-3.8.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b30e963f9e0d52c28f284d554a9469af073030030cef8693106d918b2ca92f54"},
- {file = "aiohttp-3.8.6-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:918810ef188f84152af6b938254911055a72e0f935b5fbc4c1a4ed0b0584aed1"},
- {file = "aiohttp-3.8.6-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:002f23e6ea8d3dd8d149e569fd580c999232b5fbc601c48d55398fbc2e582e8c"},
- {file = "aiohttp-3.8.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4fcf3eabd3fd1a5e6092d1242295fa37d0354b2eb2077e6eb670accad78e40e1"},
- {file = "aiohttp-3.8.6-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:255ba9d6d5ff1a382bb9a578cd563605aa69bec845680e21c44afc2670607a95"},
- {file = "aiohttp-3.8.6-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d67f8baed00870aa390ea2590798766256f31dc5ed3ecc737debb6e97e2ede78"},
- {file = "aiohttp-3.8.6-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:86f20cee0f0a317c76573b627b954c412ea766d6ada1a9fcf1b805763ae7feeb"},
- {file = "aiohttp-3.8.6-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:39a312d0e991690ccc1a61f1e9e42daa519dcc34ad03eb6f826d94c1190190dd"},
- {file = "aiohttp-3.8.6-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:e827d48cf802de06d9c935088c2924e3c7e7533377d66b6f31ed175c1620e05e"},
- {file = "aiohttp-3.8.6-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:bd111d7fc5591ddf377a408ed9067045259ff2770f37e2d94e6478d0f3fc0c17"},
- {file = "aiohttp-3.8.6-cp39-cp39-win32.whl", hash = "sha256:caf486ac1e689dda3502567eb89ffe02876546599bbf915ec94b1fa424eeffd4"},
- {file = "aiohttp-3.8.6-cp39-cp39-win_amd64.whl", hash = "sha256:3f0e27e5b733803333bb2371249f41cf42bae8884863e8e8965ec69bebe53132"},
- {file = "aiohttp-3.8.6.tar.gz", hash = "sha256:b0cf2a4501bff9330a8a5248b4ce951851e415bdcce9dc158e76cfd55e15085c"},
-]
-
-[package.dependencies]
-aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
-attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
-frozenlist = ">=1.1.1"
-multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
-
-[package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
-
-[[package]]
-name = "aiosignal"
-version = "1.3.1"
-description = "aiosignal: a list of registered asynchronous callbacks"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
- {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
-]
-
-[package.dependencies]
-frozenlist = ">=1.1.0"
-
[[package]]
name = "ale-py"
-version = "0.8.1"
+version = "0.7.5"
description = "The Arcade Learning Environment (ALE) - a platform for AI research."
optional = false
python-versions = ">=3.7"
files = [
- {file = "ale_py-0.8.1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:b2aa2f69a4169742800615970efe6914fa856e33eaf7fa9133c0e06a617a80e2"},
- {file = "ale_py-0.8.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f2f6b92c8fd6189654979bbf0b305dbe0ecf82176c47f244d8c1cbc36286b89"},
- {file = "ale_py-0.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9b168eb88c87d0f3e2a778e6c5cdde4ad951d1ca8a6dc3d3679fd45398df7d1"},
- {file = "ale_py-0.8.1-cp310-cp310-win_amd64.whl", hash = "sha256:5fcc31f495de79ee1d6bfc0f4b7c4619948851e679bbf010035e25f23146a687"},
- {file = "ale_py-0.8.1-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:0856ca777473ec4ae8a59f3af9580259adb0fd4a47d586a125a440c62e82fc10"},
- {file = "ale_py-0.8.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f10b1df8774bbe3b00365748b5e0e07cf35f6a703bbaff991bc7b3b2247dccc9"},
- {file = "ale_py-0.8.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0006d80dfe7745eb5a93444492337203c8bc7eb594a2c24c6a651c5c5b0eaf09"},
- {file = "ale_py-0.8.1-cp311-cp311-win_amd64.whl", hash = "sha256:9773eea7505484e024beb2fff0f3bfd363db151bdb9799d70995448e196b1ded"},
- {file = "ale_py-0.8.1-cp37-cp37m-macosx_10_15_x86_64.whl", hash = "sha256:87557db05be0e04130e2ec1bf909d3bb0b0bc034645d4f664e6baa573fe32191"},
- {file = "ale_py-0.8.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ae2ba24557e0ce541ea3be13b148db2a9cfa730d83537b4cbed5e10449826e51"},
- {file = "ale_py-0.8.1-cp37-cp37m-win_amd64.whl", hash = "sha256:ade5c32af567629164a6b49378978c728a15dc4db07ad6b679e8832d4fd3ea1f"},
- {file = "ale_py-0.8.1-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:0ffecb5c956749596030e464827642945162170a132d093c3d4fa2d7e5725c18"},
- {file = "ale_py-0.8.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7cd74b7ee0248ef11a086c9764e142e71defd40ec8989a99232bfd2d9e8023be"},
- {file = "ale_py-0.8.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eadf9f3990b4ff2f9e5ca35889f5e2e95cddd6a353d9d857d9b4601a6e1c4e7c"},
- {file = "ale_py-0.8.1-cp38-cp38-win_amd64.whl", hash = "sha256:817adf9a3a82c4923c731e634520a5ecf296aca0367f5c69959a96b32119d831"},
- {file = "ale_py-0.8.1-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:2d9fcfa06c74a613c5419e942ef4d3e0959533f52e94d2d4bda61d07fbfffeee"},
- {file = "ale_py-0.8.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f278036f9b6066062abcdf0987a0ec5a8e0f22a2c7cfac925e39378d4343d490"},
- {file = "ale_py-0.8.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b00f74e27815131c1a2791f3d48114363fa2708e19f09ce6b7b614cb14c9d469"},
- {file = "ale_py-0.8.1-cp39-cp39-win_amd64.whl", hash = "sha256:d49b550a2d9c25b63c343aa680fd81f253a3714cdc0e1835640933ebff1798ff"},
+ {file = "ale_py-0.7.5-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:c68d3960f0e14738362069798ff5baa85ecb337038168cb61bd4855125deb894"},
+ {file = "ale_py-0.7.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:003725a8b6f02353066564ab417f4228adc886434aa4c8d314489a7f79fe9dfd"},
+ {file = "ale_py-0.7.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1a30270d4aec820527be7a643aad381d0d1c932dc476740f63cc649dd200e065"},
+ {file = "ale_py-0.7.5-cp310-cp310-win_amd64.whl", hash = "sha256:1a72f8a311887fe3800964976c7ce3567dd701e7d9b77209d3a05c5cdf9c6af0"},
+ {file = "ale_py-0.7.5-cp37-cp37m-macosx_10_15_x86_64.whl", hash = "sha256:910c5443a836bf0bc6b03c45fefeeb9096254f807d24e247753e214062c12cae"},
+ {file = "ale_py-0.7.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f97301b89a8b49b9d2cc034f8a7df55eb6b1930126fb6cb6c6d6239bab8a89d"},
+ {file = "ale_py-0.7.5-cp37-cp37m-win_amd64.whl", hash = "sha256:38e9f6c2b1cca246253f41028b8ad92d725beec8d4672c0fe247a217a4c7dc4f"},
+ {file = "ale_py-0.7.5-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:48c5abf2c59bed0b46f17ac60ec6c940e6e7d4abc6ff7e5296246286f80e4a23"},
+ {file = "ale_py-0.7.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b2680b9a3c577865572dd9b8f24303189bb22c67b1b9668dcad3c20f59285e78"},
+ {file = "ale_py-0.7.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dec87873c6fe65c71129e20c52ce35dd4507ca409fa1233f4ae9c511144a9c99"},
+ {file = "ale_py-0.7.5-cp38-cp38-win_amd64.whl", hash = "sha256:1f8694a091e13be45c8704a089462b1519f7112a85e1c1b61c842c69a2608a98"},
+ {file = "ale_py-0.7.5-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:4c7e7fed3ce2b305d2db9ca2f87106e3df6da2c4fc2363103782ffd5975b4549"},
+ {file = "ale_py-0.7.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:da5be311285a00e587b886805ea56762d44bf4f1da672229c1fbaff209bfcdc2"},
+ {file = "ale_py-0.7.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e513799fbb5128fa045cfb9a31cffa7f7c188dbe885665188b6c4cb50e9d983c"},
+ {file = "ale_py-0.7.5-cp39-cp39-win_amd64.whl", hash = "sha256:9976ceea0251ccad7e562e2021c337d5e9a9b24b5f9eecfe82573c2279337016"},
]
[package.dependencies]
+importlib-metadata = {version = ">=4.10.0", markers = "python_version < \"3.10\""}
importlib-resources = "*"
numpy = "*"
[package.extras]
test = ["gym (>=0.23,<1.0)", "pytest (>=7.0)"]
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+description = "ANTLR 4.9.3 runtime for Python 3.7"
+optional = false
+python-versions = "*"
+files = [
+ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
+]
+
[[package]]
name = "appnope"
version = "0.1.3"
@@ -216,33 +101,41 @@ astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
[[package]]
-name = "async-timeout"
-version = "4.0.3"
-description = "Timeout context manager for asyncio programs"
+name = "autorom"
+version = "0.4.2"
+description = "Automated installation of Atari ROMs for Gym/ALE-Py"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
files = [
- {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
- {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
+ {file = "AutoROM-0.4.2-py3-none-any.whl", hash = "sha256:719c9d363ef08391fdb7003d70df235b68f36de628d289a946c4a59a3adefa13"},
+ {file = "AutoROM-0.4.2.tar.gz", hash = "sha256:b426a39bc0ee3781c7791f28963a9b2e4385b6421eeaf2f368edc00c761d428a"},
]
-[[package]]
-name = "attrs"
-version = "23.1.0"
-description = "Classes Without Boilerplate"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
- {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
-]
+[package.dependencies]
+"AutoROM.accept-rom-license" = {version = "*", optional = true, markers = "extra == \"accept-rom-license\""}
+click = "*"
+requests = "*"
+tqdm = "*"
[package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+accept-rom-license = ["AutoROM.accept-rom-license"]
+
+[[package]]
+name = "autorom-accept-rom-license"
+version = "0.6.1"
+description = "Automated installation of Atari ROMs for Gym/ALE-Py"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "AutoROM.accept-rom-license-0.6.1.tar.gz", hash = "sha256:0c905a708d634a076f686802f672817d3585259ce3be0bde8713a4fb59e3159e"},
+]
+
+[package.dependencies]
+click = "*"
+requests = "*"
+
+[package.extras]
+tests = ["ale_py", "multi_agent_ale_py"]
[[package]]
name = "bitsandbytes"
@@ -432,6 +325,31 @@ files = [
{file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
]
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+ {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "cloudpickle"
+version = "3.0.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
+ {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
+]
+
[[package]]
name = "colorama"
version = "0.4.6"
@@ -523,6 +441,26 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pill
test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+[[package]]
+name = "crafter"
+version = "1.8.2"
+description = "Open world survival game for reinforcement learning."
+optional = false
+python-versions = "*"
+files = [
+ {file = "crafter-1.8.2.tar.gz", hash = "sha256:4a142c291aa0b137c0808890381b38803876a5811b362801b8cc669faa350def"},
+]
+
+[package.dependencies]
+imageio = "*"
+numpy = "*"
+opensimplex = "*"
+pillow = "*"
+"ruamel.yaml" = "*"
+
+[package.extras]
+gui = ["pygame"]
+
[[package]]
name = "cycler"
version = "0.12.1"
@@ -538,48 +476,6 @@ files = [
docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
tests = ["pytest", "pytest-cov", "pytest-xdist"]
-[[package]]
-name = "datasets"
-version = "2.14.6"
-description = "HuggingFace community-driven open-source library of datasets"
-optional = false
-python-versions = ">=3.8.0"
-files = [
- {file = "datasets-2.14.6-py3-none-any.whl", hash = "sha256:4de857ffce21cfc847236745c69f102e33cd1f0fa8398e7be9964525fd4cd5db"},
- {file = "datasets-2.14.6.tar.gz", hash = "sha256:97ebbace8ec7af11434a87d1215379927f8fee2beab2c4a674003756ecfe920c"},
-]
-
-[package.dependencies]
-aiohttp = "*"
-dill = ">=0.3.0,<0.3.8"
-fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
-huggingface-hub = ">=0.14.0,<1.0.0"
-multiprocess = "*"
-numpy = ">=1.17"
-packaging = "*"
-pandas = "*"
-pyarrow = ">=8.0.0"
-pyyaml = ">=5.1"
-requests = ">=2.19.0"
-tqdm = ">=4.62.1"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)"]
-benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
-jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
-metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
-s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
-
[[package]]
name = "debugpy"
version = "1.8.0"
@@ -619,41 +515,44 @@ files = [
]
[[package]]
-name = "dill"
-version = "0.3.7"
-description = "serialize all of Python"
+name = "docker-pycreds"
+version = "0.4.0"
+description = "Python bindings for the docker credentials store API"
optional = false
-python-versions = ">=3.7"
+python-versions = "*"
files = [
- {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
- {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
+ {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"},
+ {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"},
]
-[package.extras]
-graph = ["objgraph (>=1.7.2)"]
-
-[[package]]
-name = "docstring-parser"
-version = "0.15"
-description = "Parse Python docstrings in reST, Google and Numpydoc format"
-optional = false
-python-versions = ">=3.6,<4.0"
-files = [
- {file = "docstring_parser-0.15-py3-none-any.whl", hash = "sha256:d1679b86250d269d06a99670924d6bce45adc00b08069dae8c47d98e89b667a9"},
- {file = "docstring_parser-0.15.tar.gz", hash = "sha256:48ddc093e8b1865899956fcc03b03e66bb7240c310fac5af81814580c55bf682"},
-]
+[package.dependencies]
+six = ">=1.4.0"
[[package]]
name = "einops"
-version = "0.7.0"
+version = "0.3.2"
description = "A new flavour of deep learning operations"
optional = false
-python-versions = ">=3.8"
+python-versions = "*"
files = [
- {file = "einops-0.7.0-py3-none-any.whl", hash = "sha256:0f3096f26b914f465f6ff3c66f5478f9a5e380bb367ffc6493a68143fbbf1fd1"},
- {file = "einops-0.7.0.tar.gz", hash = "sha256:b2b04ad6081a3b227080c9bf5e3ace7160357ff03043cd66cc5b2319eb7031d1"},
+ {file = "einops-0.3.2-py3-none-any.whl", hash = "sha256:285f3c75620897acb8b5580170c88121f010c77ce130bc7b9f220179009dafe0"},
+ {file = "einops-0.3.2.tar.gz", hash = "sha256:5200e413539f0377f4177ef00dc019968f4177c49b1db3e836c7883df2a5fe2e"},
]
+[[package]]
+name = "exceptiongroup"
+version = "1.1.3"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
+ {file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
[[package]]
name = "executing"
version = "2.0.1"
@@ -749,76 +648,6 @@ ufo = ["fs (>=2.2.0,<3)"]
unicode = ["unicodedata2 (>=15.1.0)"]
woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
-[[package]]
-name = "frozenlist"
-version = "1.4.0"
-description = "A list-like structure which implements collections.abc.MutableSequence"
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "frozenlist-1.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:764226ceef3125e53ea2cb275000e309c0aa5464d43bd72abd661e27fffc26ab"},
- {file = "frozenlist-1.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d6484756b12f40003c6128bfcc3fa9f0d49a687e171186c2d85ec82e3758c559"},
- {file = "frozenlist-1.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9ac08e601308e41eb533f232dbf6b7e4cea762f9f84f6357136eed926c15d12c"},
- {file = "frozenlist-1.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d081f13b095d74b67d550de04df1c756831f3b83dc9881c38985834387487f1b"},
- {file = "frozenlist-1.4.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:71932b597f9895f011f47f17d6428252fc728ba2ae6024e13c3398a087c2cdea"},
- {file = "frozenlist-1.4.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:981b9ab5a0a3178ff413bca62526bb784249421c24ad7381e39d67981be2c326"},
- {file = "frozenlist-1.4.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e41f3de4df3e80de75845d3e743b3f1c4c8613c3997a912dbf0229fc61a8b963"},
- {file = "frozenlist-1.4.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6918d49b1f90821e93069682c06ffde41829c346c66b721e65a5c62b4bab0300"},
- {file = "frozenlist-1.4.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0e5c8764c7829343d919cc2dfc587a8db01c4f70a4ebbc49abde5d4b158b007b"},
- {file = "frozenlist-1.4.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:8d0edd6b1c7fb94922bf569c9b092ee187a83f03fb1a63076e7774b60f9481a8"},
- {file = "frozenlist-1.4.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e29cda763f752553fa14c68fb2195150bfab22b352572cb36c43c47bedba70eb"},
- {file = "frozenlist-1.4.0-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:0c7c1b47859ee2cac3846fde1c1dc0f15da6cec5a0e5c72d101e0f83dcb67ff9"},
- {file = "frozenlist-1.4.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:901289d524fdd571be1c7be054f48b1f88ce8dddcbdf1ec698b27d4b8b9e5d62"},
- {file = "frozenlist-1.4.0-cp310-cp310-win32.whl", hash = "sha256:1a0848b52815006ea6596c395f87449f693dc419061cc21e970f139d466dc0a0"},
- {file = "frozenlist-1.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:b206646d176a007466358aa21d85cd8600a415c67c9bd15403336c331a10d956"},
- {file = "frozenlist-1.4.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:de343e75f40e972bae1ef6090267f8260c1446a1695e77096db6cfa25e759a95"},
- {file = "frozenlist-1.4.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ad2a9eb6d9839ae241701d0918f54c51365a51407fd80f6b8289e2dfca977cc3"},
- {file = "frozenlist-1.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:bd7bd3b3830247580de99c99ea2a01416dfc3c34471ca1298bccabf86d0ff4dc"},
- {file = "frozenlist-1.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bdf1847068c362f16b353163391210269e4f0569a3c166bc6a9f74ccbfc7e839"},
- {file = "frozenlist-1.4.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:38461d02d66de17455072c9ba981d35f1d2a73024bee7790ac2f9e361ef1cd0c"},
- {file = "frozenlist-1.4.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5a32087d720c608f42caed0ef36d2b3ea61a9d09ee59a5142d6070da9041b8f"},
- {file = "frozenlist-1.4.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dd65632acaf0d47608190a71bfe46b209719bf2beb59507db08ccdbe712f969b"},
- {file = "frozenlist-1.4.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:261b9f5d17cac914531331ff1b1d452125bf5daa05faf73b71d935485b0c510b"},
- {file = "frozenlist-1.4.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:b89ac9768b82205936771f8d2eb3ce88503b1556324c9f903e7156669f521472"},
- {file = "frozenlist-1.4.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:008eb8b31b3ea6896da16c38c1b136cb9fec9e249e77f6211d479db79a4eaf01"},
- {file = "frozenlist-1.4.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:e74b0506fa5aa5598ac6a975a12aa8928cbb58e1f5ac8360792ef15de1aa848f"},
- {file = "frozenlist-1.4.0-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:490132667476f6781b4c9458298b0c1cddf237488abd228b0b3650e5ecba7467"},
- {file = "frozenlist-1.4.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:76d4711f6f6d08551a7e9ef28c722f4a50dd0fc204c56b4bcd95c6cc05ce6fbb"},
- {file = "frozenlist-1.4.0-cp311-cp311-win32.whl", hash = "sha256:a02eb8ab2b8f200179b5f62b59757685ae9987996ae549ccf30f983f40602431"},
- {file = "frozenlist-1.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:515e1abc578dd3b275d6a5114030b1330ba044ffba03f94091842852f806f1c1"},
- {file = "frozenlist-1.4.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:f0ed05f5079c708fe74bf9027e95125334b6978bf07fd5ab923e9e55e5fbb9d3"},
- {file = "frozenlist-1.4.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ca265542ca427bf97aed183c1676e2a9c66942e822b14dc6e5f42e038f92a503"},
- {file = "frozenlist-1.4.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:491e014f5c43656da08958808588cc6c016847b4360e327a62cb308c791bd2d9"},
- {file = "frozenlist-1.4.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17ae5cd0f333f94f2e03aaf140bb762c64783935cc764ff9c82dff626089bebf"},
- {file = "frozenlist-1.4.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1e78fb68cf9c1a6aa4a9a12e960a5c9dfbdb89b3695197aa7064705662515de2"},
- {file = "frozenlist-1.4.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5655a942f5f5d2c9ed93d72148226d75369b4f6952680211972a33e59b1dfdc"},
- {file = "frozenlist-1.4.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c11b0746f5d946fecf750428a95f3e9ebe792c1ee3b1e96eeba145dc631a9672"},
- {file = "frozenlist-1.4.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e66d2a64d44d50d2543405fb183a21f76b3b5fd16f130f5c99187c3fb4e64919"},
- {file = "frozenlist-1.4.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:88f7bc0fcca81f985f78dd0fa68d2c75abf8272b1f5c323ea4a01a4d7a614efc"},
- {file = "frozenlist-1.4.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:5833593c25ac59ede40ed4de6d67eb42928cca97f26feea219f21d0ed0959b79"},
- {file = "frozenlist-1.4.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:fec520865f42e5c7f050c2a79038897b1c7d1595e907a9e08e3353293ffc948e"},
- {file = "frozenlist-1.4.0-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:b826d97e4276750beca7c8f0f1a4938892697a6bcd8ec8217b3312dad6982781"},
- {file = "frozenlist-1.4.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ceb6ec0a10c65540421e20ebd29083c50e6d1143278746a4ef6bcf6153171eb8"},
- {file = "frozenlist-1.4.0-cp38-cp38-win32.whl", hash = "sha256:2b8bcf994563466db019fab287ff390fffbfdb4f905fc77bc1c1d604b1c689cc"},
- {file = "frozenlist-1.4.0-cp38-cp38-win_amd64.whl", hash = "sha256:a6c8097e01886188e5be3e6b14e94ab365f384736aa1fca6a0b9e35bd4a30bc7"},
- {file = "frozenlist-1.4.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:6c38721585f285203e4b4132a352eb3daa19121a035f3182e08e437cface44bf"},
- {file = "frozenlist-1.4.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a0c6da9aee33ff0b1a451e867da0c1f47408112b3391dd43133838339e410963"},
- {file = "frozenlist-1.4.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:93ea75c050c5bb3d98016b4ba2497851eadf0ac154d88a67d7a6816206f6fa7f"},
- {file = "frozenlist-1.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f61e2dc5ad442c52b4887f1fdc112f97caeff4d9e6ebe78879364ac59f1663e1"},
- {file = "frozenlist-1.4.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa384489fefeb62321b238e64c07ef48398fe80f9e1e6afeff22e140e0850eef"},
- {file = "frozenlist-1.4.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:10ff5faaa22786315ef57097a279b833ecab1a0bfb07d604c9cbb1c4cdc2ed87"},
- {file = "frozenlist-1.4.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:007df07a6e3eb3e33e9a1fe6a9db7af152bbd8a185f9aaa6ece10a3529e3e1c6"},
- {file = "frozenlist-1.4.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f4f399d28478d1f604c2ff9119907af9726aed73680e5ed1ca634d377abb087"},
- {file = "frozenlist-1.4.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:c5374b80521d3d3f2ec5572e05adc94601985cc526fb276d0c8574a6d749f1b3"},
- {file = "frozenlist-1.4.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:ce31ae3e19f3c902de379cf1323d90c649425b86de7bbdf82871b8a2a0615f3d"},
- {file = "frozenlist-1.4.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7211ef110a9194b6042449431e08c4d80c0481e5891e58d429df5899690511c2"},
- {file = "frozenlist-1.4.0-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:556de4430ce324c836789fa4560ca62d1591d2538b8ceb0b4f68fb7b2384a27a"},
- {file = "frozenlist-1.4.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:7645a8e814a3ee34a89c4a372011dcd817964ce8cb273c8ed6119d706e9613e3"},
- {file = "frozenlist-1.4.0-cp39-cp39-win32.whl", hash = "sha256:19488c57c12d4e8095a922f328df3f179c820c212940a498623ed39160bc3c2f"},
- {file = "frozenlist-1.4.0-cp39-cp39-win_amd64.whl", hash = "sha256:6221d84d463fb110bdd7619b69cb43878a11d51cbb9394ae3105d082d5199167"},
- {file = "frozenlist-1.4.0.tar.gz", hash = "sha256:09163bdf0b2907454042edb19f887c6d33806adc71fbd54afc14908bfdc22251"},
-]
-
[[package]]
name = "fsspec"
version = "2023.10.0"
@@ -830,10 +659,6 @@ files = [
{file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
]
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
-
[package.extras]
abfs = ["adlfs"]
adl = ["adlfs"]
@@ -858,6 +683,77 @@ smb = ["smbprotocol"]
ssh = ["paramiko"]
tqdm = ["tqdm"]
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"},
+ {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.40"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
+ {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
+
+[[package]]
+name = "gym"
+version = "0.22.0"
+description = "Gym: A universal API for reinforcement learning environments."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "gym-0.22.0.tar.gz", hash = "sha256:339144c89951758187c378111919bc0e2f1695f9e9d9699e3f19279a6398148d"},
+]
+
+[package.dependencies]
+ale-py = {version = ">=0.7.4,<0.8.0", optional = true, markers = "extra == \"atari\""}
+autorom = {version = ">=0.4.2,<0.5.0", extras = ["accept-rom-license"], optional = true, markers = "extra == \"accept-rom-license\""}
+cloudpickle = ">=1.2.0"
+gym_notices = ">=0.0.4"
+importlib_metadata = {version = ">=4.10.0", markers = "python_version < \"3.10\""}
+numpy = ">=1.18.0"
+
+[package.extras]
+accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
+all = ["ale-py (>=0.7.4,<0.8.0)", "box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "mujoco_py (>=1.50,<2.0)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)", "scipy (>=1.4.1)"]
+atari = ["ale-py (>=0.7.4,<0.8.0)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)"]
+classic-control = ["pygame (==2.1.0)"]
+mujoco = ["mujoco_py (>=1.50,<2.0)"]
+nomujoco = ["box2d-py (==2.3.5)", "lz4 (>=3.1.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)"]
+other = ["lz4 (>=3.1.0)", "opencv-python (>=3.0)"]
+toy-text = ["pygame (==2.1.0)", "scipy (>=1.4.1)"]
+
+[[package]]
+name = "gym-notices"
+version = "0.0.8"
+description = "Notices for gym"
+optional = false
+python-versions = "*"
+files = [
+ {file = "gym-notices-0.0.8.tar.gz", hash = "sha256:ad25e200487cafa369728625fe064e88ada1346618526102659b4640f2b4b911"},
+ {file = "gym_notices-0.0.8-py3-none-any.whl", hash = "sha256:e5f82e00823a166747b4c2a07de63b6560b1acb880638547e0cabf825a01e463"},
+]
+
[[package]]
name = "huggingface-hub"
version = "0.17.3"
@@ -891,6 +787,22 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jed
torch = ["torch"]
typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+[[package]]
+name = "hydra-core"
+version = "1.3.2"
+description = "A framework for elegantly configuring complex applications"
+optional = false
+python-versions = "*"
+files = [
+ {file = "hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824"},
+ {file = "hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b"},
+]
+
+[package.dependencies]
+antlr4-python3-runtime = "==4.9.*"
+omegaconf = ">=2.2,<2.4"
+packaging = "*"
+
[[package]]
name = "idna"
version = "3.4"
@@ -902,6 +814,56 @@ files = [
{file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
]
+[[package]]
+name = "imageio"
+version = "2.31.5"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "imageio-2.31.5-py3-none-any.whl", hash = "sha256:97f68e12ba676f2f4b541684ed81f7f3370dc347e8321bc68ee34d37b2dbac9f"},
+ {file = "imageio-2.31.5.tar.gz", hash = "sha256:d8e53f9cd4054880276a3dac0a28c85ba7874084856a55a0294a8ae6ed7f3a8e"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
+[[package]]
+name = "importlib-metadata"
+version = "6.8.0"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
+ {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"},
+]
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+
[[package]]
name = "importlib-resources"
version = "6.1.1"
@@ -913,6 +875,9 @@ files = [
{file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
]
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
@@ -965,6 +930,7 @@ files = [
appnope = {version = "*", markers = "sys_platform == \"darwin\""}
colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
jedi = ">=0.16"
matplotlib-inline = "*"
pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
@@ -972,6 +938,7 @@ prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
pygments = ">=2.4.0"
stack-data = "*"
traitlets = ">=5"
+typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
[package.extras]
all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
@@ -1022,17 +989,6 @@ MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
-[[package]]
-name = "joblib"
-version = "1.3.2"
-description = "Lightweight pipelining with Python functions"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
- {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
-]
-
[[package]]
name = "jupyter-client"
version = "8.6.0"
@@ -1045,6 +1001,7 @@ files = [
]
[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
python-dateutil = ">=2.8.2"
pyzmq = ">=23.0"
@@ -1188,85 +1145,6 @@ files = [
{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
]
-[[package]]
-name = "lightning"
-version = "2.1.1"
-description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "lightning-2.1.1-py3-none-any.whl", hash = "sha256:c1ea10fd112600eda5210bf3dc6d01d82cec6d70a6187e44b800cbb50be7346e"},
- {file = "lightning-2.1.1.tar.gz", hash = "sha256:865491940d20a9754eac7494aa18cab893e0c2b31e83743349eeeaf31dfb52db"},
-]
-
-[package.dependencies]
-fsspec = {version = ">2021.06.0,<2025.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0,<2.0"
-numpy = ">=1.17.2,<3.0"
-packaging = ">=20.0,<25.0"
-pytorch-lightning = "*"
-PyYAML = ">=5.4,<8.0"
-torch = ">=1.12.0,<4.0"
-torchmetrics = ">=0.7.0,<3.0"
-tqdm = ">=4.57.0,<6.0"
-typing-extensions = ">=4.0.0,<6.0"
-
-[package.extras]
-all = ["Jinja2 (<4.0)", "Pillow (>=9.5.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (<1.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "deepspeed (>=0.8.2,<=0.9.3)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "fsspec[http] (>2021.06.0,<2024.0)", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "tensorboardX (>=2.2,<3.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)", "torchvision (>=0.15.2,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.50)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app-all = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app-cloud = ["docker (>=5.0.0,<7.0)", "redis (>=4.0.1,<6.0)", "s3fs (>=2022.5.0,<2024.0)"]
-app-components = ["aiohttp (>=3.8.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-fabric (>=1.9.0)", "pytorch-lightning (>=1.9.0)"]
-app-dev = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "coverage (==7.3.1)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "httpx (==0.25.0)", "inquirer (>=2.10.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "pandas", "playwright (==1.38.0)", "psutil (<6.0)", "pydantic (>=1.7.4)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==0.9.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "requests-mock (==1.11.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "setuptools (<69.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "trio (<0.22.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app-extra = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app-test = ["coverage (==7.3.1)", "httpx (==0.25.0)", "pandas", "playwright (==1.38.0)", "psutil (<6.0)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==0.9.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "requests-mock (==1.11.0)", "setuptools (<69.0)", "trio (<0.22.0)"]
-cloud = ["docker (>=5.0.0,<7.0)", "fsspec[http] (>2021.06.0,<2024.0)", "redis (>=4.0.1,<6.0)", "s3fs (>=2022.5.0,<2024.0)"]
-components = ["aiohttp (>=3.8.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-fabric (>=1.9.0)", "pytorch-lightning (>=1.9.0)"]
-data = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.50)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-data-all = ["Jinja2 (<4.0)", "Pillow (>=9.5.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "fsspec[http] (>2021.06.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.50)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "torchvision (>=0.15.2,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-data-cloud = ["fsspec[http] (>2021.06.0,<2024.0)", "s3fs (>=2022.5.0,<2024.0)"]
-data-dev = ["Jinja2 (<4.0)", "Pillow (>=9.5.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "coverage (==7.3.1)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "fsspec[http] (>2021.06.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.50)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "torchvision (>=0.15.2,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-data-examples = ["Pillow (>=9.5.0)", "torchvision (>=0.15.2,<1.0)"]
-data-test = ["coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)"]
-dev = ["Jinja2 (<4.0)", "Pillow (>=9.5.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (<1.0)", "click (<9.0)", "click (==8.1.7)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "deepspeed (>=0.8.2,<=0.9.3)", "docker (>=5.0.0,<7.0)", "fastapi", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "fsspec[http] (>2021.06.0,<2024.0)", "gym[classic-control] (>=0.17.0,<1.0)", "httpx (==0.25.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "packaging", "pandas", "pandas (>1.0,<3.0)", "playwright (==1.38.0)", "psutil (<6.0)", "pydantic (>=1.7.4)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==0.9.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "requests-mock (==1.11.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "scikit-learn (>0.22.1,<2.0)", "setuptools (<69.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchmetrics (>=0.7.0,<2.0)", "torchvision (>=0.13.0,<1.0)", "torchvision (>=0.15.2,<1.0)", "traitlets (>=5.3.0,<6.0)", "trio (<0.22.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-examples = ["Pillow (>=9.5.0)", "gym[classic-control] (>=0.17.0,<1.0)", "ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)", "torchvision (>=0.15.2,<1.0)"]
-extra = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (<1.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.50)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "tensorboardX (>=2.2,<3.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-fabric-all = ["deepspeed (>=0.8.2,<=0.9.3)", "lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)"]
-fabric-dev = ["click (==8.1.7)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "lightning-utilities (>=0.8.0,<1.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchmetrics (>=0.7.0,<2.0)", "torchvision (>=0.13.0,<1.0)"]
-fabric-examples = ["lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)"]
-fabric-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
-fabric-test = ["click (==8.1.7)", "coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)"]
-pytorch-all = ["bitsandbytes (<1.0)", "deepspeed (>=0.8.2,<=0.9.3)", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)"]
-pytorch-dev = ["bitsandbytes (<1.0)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "fastapi", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "rich (>=12.3.0,<14.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)", "uvicorn"]
-pytorch-examples = ["gym[classic-control] (>=0.17.0,<1.0)", "ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)"]
-pytorch-extra = ["bitsandbytes (<1.0)", "hydra-core (>=1.0.5,<2.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)"]
-pytorch-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
-pytorch-test = ["cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "uvicorn"]
-store = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.50)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-store-test = ["coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)"]
-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
-test = ["click (==8.1.7)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "httpx (==0.25.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas", "pandas (>1.0,<3.0)", "playwright (==1.38.0)", "psutil (<6.0)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==0.9.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "requests-mock (==1.11.0)", "scikit-learn (>0.22.1,<2.0)", "setuptools (<69.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)", "trio (<0.22.0)", "uvicorn"]
-
-[[package]]
-name = "lightning-utilities"
-version = "0.9.0"
-description = "PyTorch Lightning Sample project."
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "lightning-utilities-0.9.0.tar.gz", hash = "sha256:efbf2c488c257f942abdfd06cf646fb84ca215a9663b60081811e22a15ee033b"},
- {file = "lightning_utilities-0.9.0-py3-none-any.whl", hash = "sha256:918dd90c775719e3855631db6282ad75c14da4c5727c4cebdd1589d865fad03d"},
-]
-
-[package.dependencies]
-packaging = ">=17.1"
-typing-extensions = "*"
-
-[package.extras]
-cli = ["fire"]
-docs = ["requests (>=2.0.0)"]
-typing = ["mypy (>=1.0.0)"]
-
[[package]]
name = "loguru"
version = "0.7.2"
@@ -1395,6 +1273,7 @@ files = [
contourpy = ">=1.0.1"
cycler = ">=0.10"
fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
kiwisolver = ">=1.3.1"
numpy = ">=1.21,<2"
packaging = ">=20.0"
@@ -1416,6 +1295,28 @@ files = [
[package.dependencies]
traitlets = "*"
+[[package]]
+name = "minihack"
+version = "0.1.5"
+description = "MiniHack The Planet: A Sandbox for Open-Ended Reinforcement Learning Research"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "minihack-0.1.5.tar.gz", hash = "sha256:b732a05eed9b70cc48735d63fd0c34498611be1342f4bd45a3ad128367d8c97a"},
+]
+
+[package.dependencies]
+gym = ">=0.15,<=0.23"
+nle = "0.9.0"
+numpy = ">=1.16"
+
+[package.extras]
+all = ["black (>=19.10b0)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "hydra-colorlog (>=1.0.0)", "hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "hydra-submitit-launcher (>=1.1.1)", "inflect", "myst-parser (==0.15.1)", "nbsphinx (==0.8.6)", "pre-commit (>=2.0.1)", "pytest (>=5.3)", "pytest-benchmark (>=3.1.0)", "pyyaml", "ray[default] (==1.3.0)", "ray[rllib] (==1.3.0)", "sphinx (==4.0.2)", "sphinx-rtd-theme (==1.0.0)", "stanza", "torch (>=1.3.1)", "torch (>=1.3.1)", "wandb (>=0.10.31)", "wandb (>=0.10.31)"]
+dev = ["black (>=19.10b0)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "myst-parser (==0.15.1)", "nbsphinx (==0.8.6)", "pre-commit (>=2.0.1)", "pytest (>=5.3)", "pytest-benchmark (>=3.1.0)", "sphinx (==4.0.2)", "sphinx-rtd-theme (==1.0.0)"]
+polybeast = ["hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "pyyaml", "torch (>=1.3.1)", "wandb (>=0.10.31)"]
+rllib = ["hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "ray[default] (==1.3.0)", "ray[rllib] (==1.3.0)", "torch (>=1.3.1)", "wandb (>=0.10.31)"]
+wiki = ["inflect", "stanza"]
+
[[package]]
name = "mpmath"
version = "1.3.0"
@@ -1433,117 +1334,6 @@ docs = ["sphinx"]
gmpy = ["gmpy2 (>=2.1.0a4)"]
tests = ["pytest (>=4.6)"]
-[[package]]
-name = "multidict"
-version = "6.0.4"
-description = "multidict implementation"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "multidict-6.0.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0b1a97283e0c85772d613878028fec909f003993e1007eafa715b24b377cb9b8"},
- {file = "multidict-6.0.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eeb6dcc05e911516ae3d1f207d4b0520d07f54484c49dfc294d6e7d63b734171"},
- {file = "multidict-6.0.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d6d635d5209b82a3492508cf5b365f3446afb65ae7ebd755e70e18f287b0adf7"},
- {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c048099e4c9e9d615545e2001d3d8a4380bd403e1a0578734e0d31703d1b0c0b"},
- {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ea20853c6dbbb53ed34cb4d080382169b6f4554d394015f1bef35e881bf83547"},
- {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:16d232d4e5396c2efbbf4f6d4df89bfa905eb0d4dc5b3549d872ab898451f569"},
- {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36c63aaa167f6c6b04ef2c85704e93af16c11d20de1d133e39de6a0e84582a93"},
- {file = "multidict-6.0.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:64bdf1086b6043bf519869678f5f2757f473dee970d7abf6da91ec00acb9cb98"},
- {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:43644e38f42e3af682690876cff722d301ac585c5b9e1eacc013b7a3f7b696a0"},
- {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:7582a1d1030e15422262de9f58711774e02fa80df0d1578995c76214f6954988"},
- {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ddff9c4e225a63a5afab9dd15590432c22e8057e1a9a13d28ed128ecf047bbdc"},
- {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ee2a1ece51b9b9e7752e742cfb661d2a29e7bcdba2d27e66e28a99f1890e4fa0"},
- {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a2e4369eb3d47d2034032a26c7a80fcb21a2cb22e1173d761a162f11e562caa5"},
- {file = "multidict-6.0.4-cp310-cp310-win32.whl", hash = "sha256:574b7eae1ab267e5f8285f0fe881f17efe4b98c39a40858247720935b893bba8"},
- {file = "multidict-6.0.4-cp310-cp310-win_amd64.whl", hash = "sha256:4dcbb0906e38440fa3e325df2359ac6cb043df8e58c965bb45f4e406ecb162cc"},
- {file = "multidict-6.0.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:0dfad7a5a1e39c53ed00d2dd0c2e36aed4650936dc18fd9a1826a5ae1cad6f03"},
- {file = "multidict-6.0.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:64da238a09d6039e3bd39bb3aee9c21a5e34f28bfa5aa22518581f910ff94af3"},
- {file = "multidict-6.0.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ff959bee35038c4624250473988b24f846cbeb2c6639de3602c073f10410ceba"},
- {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:01a3a55bd90018c9c080fbb0b9f4891db37d148a0a18722b42f94694f8b6d4c9"},
- {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c5cb09abb18c1ea940fb99360ea0396f34d46566f157122c92dfa069d3e0e982"},
- {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:666daae833559deb2d609afa4490b85830ab0dfca811a98b70a205621a6109fe"},
- {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11bdf3f5e1518b24530b8241529d2050014c884cf18b6fc69c0c2b30ca248710"},
- {file = "multidict-6.0.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d18748f2d30f94f498e852c67d61261c643b349b9d2a581131725595c45ec6c"},
- {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:458f37be2d9e4c95e2d8866a851663cbc76e865b78395090786f6cd9b3bbf4f4"},
- {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:b1a2eeedcead3a41694130495593a559a668f382eee0727352b9a41e1c45759a"},
- {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7d6ae9d593ef8641544d6263c7fa6408cc90370c8cb2bbb65f8d43e5b0351d9c"},
- {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:5979b5632c3e3534e42ca6ff856bb24b2e3071b37861c2c727ce220d80eee9ed"},
- {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:dcfe792765fab89c365123c81046ad4103fcabbc4f56d1c1997e6715e8015461"},
- {file = "multidict-6.0.4-cp311-cp311-win32.whl", hash = "sha256:3601a3cece3819534b11d4efc1eb76047488fddd0c85a3948099d5da4d504636"},
- {file = "multidict-6.0.4-cp311-cp311-win_amd64.whl", hash = "sha256:81a4f0b34bd92df3da93315c6a59034df95866014ac08535fc819f043bfd51f0"},
- {file = "multidict-6.0.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:67040058f37a2a51ed8ea8f6b0e6ee5bd78ca67f169ce6122f3e2ec80dfe9b78"},
- {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:853888594621e6604c978ce2a0444a1e6e70c8d253ab65ba11657659dcc9100f"},
- {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:39ff62e7d0f26c248b15e364517a72932a611a9b75f35b45be078d81bdb86603"},
- {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af048912e045a2dc732847d33821a9d84ba553f5c5f028adbd364dd4765092ac"},
- {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1e8b901e607795ec06c9e42530788c45ac21ef3aaa11dbd0c69de543bfb79a9"},
- {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:62501642008a8b9871ddfccbf83e4222cf8ac0d5aeedf73da36153ef2ec222d2"},
- {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:99b76c052e9f1bc0721f7541e5e8c05db3941eb9ebe7b8553c625ef88d6eefde"},
- {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:509eac6cf09c794aa27bcacfd4d62c885cce62bef7b2c3e8b2e49d365b5003fe"},
- {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:21a12c4eb6ddc9952c415f24eef97e3e55ba3af61f67c7bc388dcdec1404a067"},
- {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:5cad9430ab3e2e4fa4a2ef4450f548768400a2ac635841bc2a56a2052cdbeb87"},
- {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ab55edc2e84460694295f401215f4a58597f8f7c9466faec545093045476327d"},
- {file = "multidict-6.0.4-cp37-cp37m-win32.whl", hash = "sha256:5a4dcf02b908c3b8b17a45fb0f15b695bf117a67b76b7ad18b73cf8e92608775"},
- {file = "multidict-6.0.4-cp37-cp37m-win_amd64.whl", hash = "sha256:6ed5f161328b7df384d71b07317f4d8656434e34591f20552c7bcef27b0ab88e"},
- {file = "multidict-6.0.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5fc1b16f586f049820c5c5b17bb4ee7583092fa0d1c4e28b5239181ff9532e0c"},
- {file = "multidict-6.0.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1502e24330eb681bdaa3eb70d6358e818e8e8f908a22a1851dfd4e15bc2f8161"},
- {file = "multidict-6.0.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b692f419760c0e65d060959df05f2a531945af31fda0c8a3b3195d4efd06de11"},
- {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45e1ecb0379bfaab5eef059f50115b54571acfbe422a14f668fc8c27ba410e7e"},
- {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ddd3915998d93fbcd2566ddf9cf62cdb35c9e093075f862935573d265cf8f65d"},
- {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:59d43b61c59d82f2effb39a93c48b845efe23a3852d201ed2d24ba830d0b4cf2"},
- {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc8e1d0c705233c5dd0c5e6460fbad7827d5d36f310a0fadfd45cc3029762258"},
- {file = "multidict-6.0.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6aa0418fcc838522256761b3415822626f866758ee0bc6632c9486b179d0b52"},
- {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6748717bb10339c4760c1e63da040f5f29f5ed6e59d76daee30305894069a660"},
- {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:4d1a3d7ef5e96b1c9e92f973e43aa5e5b96c659c9bc3124acbbd81b0b9c8a951"},
- {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4372381634485bec7e46718edc71528024fcdc6f835baefe517b34a33c731d60"},
- {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:fc35cb4676846ef752816d5be2193a1e8367b4c1397b74a565a9d0389c433a1d"},
- {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:4b9d9e4e2b37daddb5c23ea33a3417901fa7c7b3dee2d855f63ee67a0b21e5b1"},
- {file = "multidict-6.0.4-cp38-cp38-win32.whl", hash = "sha256:e41b7e2b59679edfa309e8db64fdf22399eec4b0b24694e1b2104fb789207779"},
- {file = "multidict-6.0.4-cp38-cp38-win_amd64.whl", hash = "sha256:d6c254ba6e45d8e72739281ebc46ea5eb5f101234f3ce171f0e9f5cc86991480"},
- {file = "multidict-6.0.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:16ab77bbeb596e14212e7bab8429f24c1579234a3a462105cda4a66904998664"},
- {file = "multidict-6.0.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc779e9e6f7fda81b3f9aa58e3a6091d49ad528b11ed19f6621408806204ad35"},
- {file = "multidict-6.0.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4ceef517eca3e03c1cceb22030a3e39cb399ac86bff4e426d4fc6ae49052cc60"},
- {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:281af09f488903fde97923c7744bb001a9b23b039a909460d0f14edc7bf59706"},
- {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:52f2dffc8acaba9a2f27174c41c9e57f60b907bb9f096b36b1a1f3be71c6284d"},
- {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b41156839806aecb3641f3208c0dafd3ac7775b9c4c422d82ee2a45c34ba81ca"},
- {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d5e3fc56f88cc98ef8139255cf8cd63eb2c586531e43310ff859d6bb3a6b51f1"},
- {file = "multidict-6.0.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8316a77808c501004802f9beebde51c9f857054a0c871bd6da8280e718444449"},
- {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f70b98cd94886b49d91170ef23ec5c0e8ebb6f242d734ed7ed677b24d50c82cf"},
- {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bf6774e60d67a9efe02b3616fee22441d86fab4c6d335f9d2051d19d90a40063"},
- {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:e69924bfcdda39b722ef4d9aa762b2dd38e4632b3641b1d9a57ca9cd18f2f83a"},
- {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:6b181d8c23da913d4ff585afd1155a0e1194c0b50c54fcfe286f70cdaf2b7176"},
- {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:52509b5be062d9eafc8170e53026fbc54cf3b32759a23d07fd935fb04fc22d95"},
- {file = "multidict-6.0.4-cp39-cp39-win32.whl", hash = "sha256:27c523fbfbdfd19c6867af7346332b62b586eed663887392cff78d614f9ec313"},
- {file = "multidict-6.0.4-cp39-cp39-win_amd64.whl", hash = "sha256:33029f5734336aa0d4c0384525da0387ef89148dc7191aae00ca5fb23d7aafc2"},
- {file = "multidict-6.0.4.tar.gz", hash = "sha256:3666906492efb76453c0e7b97f2cf459b0682e7402c0489a95484965dbc1da49"},
-]
-
-[[package]]
-name = "multiprocess"
-version = "0.70.15"
-description = "better multiprocessing and multithreading in Python"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "multiprocess-0.70.15-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:aa36c7ed16f508091438687fe9baa393a7a8e206731d321e443745e743a0d4e5"},
- {file = "multiprocess-0.70.15-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:20e024018c46d0d1602024c613007ac948f9754659e3853b0aa705e83f6931d8"},
- {file = "multiprocess-0.70.15-pp37-pypy37_pp73-manylinux_2_24_i686.whl", hash = "sha256:e576062981c91f0fe8a463c3d52506e598dfc51320a8dd8d78b987dfca91c5db"},
- {file = "multiprocess-0.70.15-pp37-pypy37_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:e73f497e6696a0f5433ada2b3d599ae733b87a6e8b008e387c62ac9127add177"},
- {file = "multiprocess-0.70.15-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:73db2e7b32dcc7f9b0f075c2ffa45c90b6729d3f1805f27e88534c8d321a1be5"},
- {file = "multiprocess-0.70.15-pp38-pypy38_pp73-manylinux_2_24_i686.whl", hash = "sha256:4271647bd8a49c28ecd6eb56a7fdbd3c212c45529ad5303b40b3c65fc6928e5f"},
- {file = "multiprocess-0.70.15-pp38-pypy38_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:cf981fb998d6ec3208cb14f0cf2e9e80216e834f5d51fd09ebc937c32b960902"},
- {file = "multiprocess-0.70.15-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:18f9f2c7063346d1617bd1684fdcae8d33380ae96b99427260f562e1a1228b67"},
- {file = "multiprocess-0.70.15-pp39-pypy39_pp73-manylinux_2_24_i686.whl", hash = "sha256:0eac53214d664c49a34695e5824872db4006b1a465edd7459a251809c3773370"},
- {file = "multiprocess-0.70.15-pp39-pypy39_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:1a51dd34096db47fb21fa2b839e615b051d51b97af9a67afbcdaa67186b44883"},
- {file = "multiprocess-0.70.15-py310-none-any.whl", hash = "sha256:7dd58e33235e83cf09d625e55cffd7b0f0eede7ee9223cdd666a87624f60c21a"},
- {file = "multiprocess-0.70.15-py311-none-any.whl", hash = "sha256:134f89053d82c9ed3b73edd3a2531eb791e602d4f4156fc92a79259590bd9670"},
- {file = "multiprocess-0.70.15-py37-none-any.whl", hash = "sha256:f7d4a1629bccb433114c3b4885f69eccc200994323c80f6feee73b0edc9199c5"},
- {file = "multiprocess-0.70.15-py38-none-any.whl", hash = "sha256:bee9afba476c91f9ebee7beeee0601face9eff67d822e893f9a893725fbd6316"},
- {file = "multiprocess-0.70.15-py39-none-any.whl", hash = "sha256:3e0953f5d52b4c76f1c973eaf8214554d146f2be5decb48e928e55c7a2d19338"},
- {file = "multiprocess-0.70.15.tar.gz", hash = "sha256:f20eed3036c0ef477b07a4177cf7c1ba520d9a2677870a4f47fe026f0cd6787e"},
-]
-
-[package.dependencies]
-dill = ">=0.3.7"
-
[[package]]
name = "nest-asyncio"
version = "1.5.8"
@@ -1573,6 +1363,26 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+[[package]]
+name = "nle"
+version = "0.9.0"
+description = "The NetHack Learning Environment (NLE): a reinforcement learning environment based on NetHack"
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "nle-0.9.0.tar.gz", hash = "sha256:a98644bdd547017cdde9fdf589b245da98ff8753327285e949d506d9006f10d4"},
+]
+
+[package.dependencies]
+gym = ">=0.15,<=0.23"
+numpy = ">=1.16"
+pybind11 = ">=2.2"
+
+[package.extras]
+agent = ["torch (>=1.3.1)"]
+all = ["black (>=19.10b0)", "cmake_format (>=0.6.10)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "memory-profiler (>=0.60.0)", "pre-commit (>=2.0.1)", "pytest (>=6.2.5)", "pytest-benchmark (>=3.4.1)", "sphinx (>=2.4.4)", "sphinx-rtd-theme (==0.4.3)", "torch (>=1.3.1)"]
+dev = ["black (>=19.10b0)", "cmake_format (>=0.6.10)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "memory-profiler (>=0.60.0)", "pre-commit (>=2.0.1)", "pytest (>=6.2.5)", "pytest-benchmark (>=3.4.1)", "sphinx (>=2.4.4)", "sphinx-rtd-theme (==0.4.3)"]
+
[[package]]
name = "numpy"
version = "1.26.1"
@@ -1614,6 +1424,60 @@ files = [
{file = "numpy-1.26.1.tar.gz", hash = "sha256:c8c6c72d4a9f831f328efb1312642a1cafafaa88981d9ab76368d50d07d93cbe"},
]
+[[package]]
+name = "omegaconf"
+version = "2.3.0"
+description = "A flexible configuration library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"},
+ {file = "omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7"},
+]
+
+[package.dependencies]
+antlr4-python3-runtime = "==4.9.*"
+PyYAML = ">=5.1.0"
+
+[[package]]
+name = "opencv-python"
+version = "4.8.1.78"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "opencv-python-4.8.1.78.tar.gz", hash = "sha256:cc7adbbcd1112877a39274106cb2752e04984bc01a031162952e97450d6117f6"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:91d5f6f5209dc2635d496f6b8ca6573ecdad051a09e6b5de4c399b8e673c60da"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:bc31f47e05447da8b3089faa0a07ffe80e114c91ce0b171e6424f9badbd1c5cd"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9814beca408d3a0eca1bae7e3e5be68b07c17ecceb392b94170881216e09b319"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c4c406bdb41eb21ea51b4e90dfbc989c002786c3f601c236a99c59a54670a394"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-win32.whl", hash = "sha256:a7aac3900fbacf55b551e7b53626c3dad4c71ce85643645c43e91fcb19045e47"},
+ {file = "opencv_python-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:b983197f97cfa6fcb74e1da1802c7497a6f94ed561aba6980f1f33123f904956"},
+]
+
+[package.dependencies]
+numpy = [
+ {version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
+ {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+ {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
+ {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+ {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
+]
+
+[[package]]
+name = "opensimplex"
+version = "0.4.5"
+description = "OpenSimplex is a noise generation function like Perlin or Simplex noise, but better."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "opensimplex-0.4.5-py3-none-any.whl", hash = "sha256:5e34f2b6f7e2d3e798d7f060b45cb1f92c02b68b6fc2626ac2a36bd53ec6c773"},
+ {file = "opensimplex-0.4.5.tar.gz", hash = "sha256:c390cf70dea97b32bd1a49ba6781e84f48dc93b1cc6c06f36d9d44c548299f90"},
+]
+
+[package.dependencies]
+numpy = ">=1.22"
+
[[package]]
name = "packaging"
version = "23.2"
@@ -1625,73 +1489,6 @@ files = [
{file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
]
-[[package]]
-name = "pandas"
-version = "2.1.3"
-description = "Powerful data structures for data analysis, time series, and statistics"
-optional = false
-python-versions = ">=3.9"
-files = [
- {file = "pandas-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:acf08a73b5022b479c1be155d4988b72f3020f308f7a87c527702c5f8966d34f"},
- {file = "pandas-2.1.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3cc4469ff0cf9aa3a005870cb49ab8969942b7156e0a46cc3f5abd6b11051dfb"},
- {file = "pandas-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:35172bff95f598cc5866c047f43c7f4df2c893acd8e10e6653a4b792ed7f19bb"},
- {file = "pandas-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:59dfe0e65a2f3988e940224e2a70932edc964df79f3356e5f2997c7d63e758b4"},
- {file = "pandas-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:0296a66200dee556850d99b24c54c7dfa53a3264b1ca6f440e42bad424caea03"},
- {file = "pandas-2.1.3-cp310-cp310-win_amd64.whl", hash = "sha256:465571472267a2d6e00657900afadbe6097c8e1dc43746917db4dfc862e8863e"},
- {file = "pandas-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:04d4c58e1f112a74689da707be31cf689db086949c71828ef5da86727cfe3f82"},
- {file = "pandas-2.1.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7fa2ad4ff196768ae63a33f8062e6838efed3a319cf938fdf8b95e956c813042"},
- {file = "pandas-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4441ac94a2a2613e3982e502ccec3bdedefe871e8cea54b8775992485c5660ef"},
- {file = "pandas-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d5ded6ff28abbf0ea7689f251754d3789e1edb0c4d0d91028f0b980598418a58"},
- {file = "pandas-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fca5680368a5139d4920ae3dc993eb5106d49f814ff24018b64d8850a52c6ed2"},
- {file = "pandas-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:de21e12bf1511190fc1e9ebc067f14ca09fccfb189a813b38d63211d54832f5f"},
- {file = "pandas-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:a5d53c725832e5f1645e7674989f4c106e4b7249c1d57549023ed5462d73b140"},
- {file = "pandas-2.1.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7cf4cf26042476e39394f1f86868d25b265ff787c9b2f0d367280f11afbdee6d"},
- {file = "pandas-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72c84ec1b1d8e5efcbff5312abe92bfb9d5b558f11e0cf077f5496c4f4a3c99e"},
- {file = "pandas-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f539e113739a3e0cc15176bf1231a553db0239bfa47a2c870283fd93ba4f683"},
- {file = "pandas-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fc77309da3b55732059e484a1efc0897f6149183c522390772d3561f9bf96c00"},
- {file = "pandas-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:08637041279b8981a062899da0ef47828df52a1838204d2b3761fbd3e9fcb549"},
- {file = "pandas-2.1.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b99c4e51ef2ed98f69099c72c75ec904dd610eb41a32847c4fcbc1a975f2d2b8"},
- {file = "pandas-2.1.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f7ea8ae8004de0381a2376662c0505bb0a4f679f4c61fbfd122aa3d1b0e5f09d"},
- {file = "pandas-2.1.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fcd76d67ca2d48f56e2db45833cf9d58f548f97f61eecd3fdc74268417632b8a"},
- {file = "pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1329dbe93a880a3d7893149979caa82d6ba64a25e471682637f846d9dbc10dd2"},
- {file = "pandas-2.1.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:321ecdb117bf0f16c339cc6d5c9a06063854f12d4d9bc422a84bb2ed3207380a"},
- {file = "pandas-2.1.3-cp39-cp39-win_amd64.whl", hash = "sha256:11a771450f36cebf2a4c9dbd3a19dfa8c46c4b905a3ea09dc8e556626060fe71"},
- {file = "pandas-2.1.3.tar.gz", hash = "sha256:22929f84bca106921917eb73c1521317ddd0a4c71b395bcf767a106e3494209f"},
-]
-
-[package.dependencies]
-numpy = [
- {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
- {version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
-]
-python-dateutil = ">=2.8.2"
-pytz = ">=2020.1"
-tzdata = ">=2022.1"
-
-[package.extras]
-all = ["PyQt5 (>=5.15.6)", "SQLAlchemy (>=1.4.36)", "beautifulsoup4 (>=4.11.1)", "bottleneck (>=1.3.4)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=0.8.1)", "fsspec (>=2022.05.0)", "gcsfs (>=2022.05.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.8.0)", "matplotlib (>=3.6.1)", "numba (>=0.55.2)", "numexpr (>=2.8.0)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pandas-gbq (>=0.17.5)", "psycopg2 (>=2.9.3)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.5)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "pyxlsb (>=1.0.9)", "qtpy (>=2.2.0)", "s3fs (>=2022.05.0)", "scipy (>=1.8.1)", "tables (>=3.7.0)", "tabulate (>=0.8.10)", "xarray (>=2022.03.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)", "zstandard (>=0.17.0)"]
-aws = ["s3fs (>=2022.05.0)"]
-clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
-compression = ["zstandard (>=0.17.0)"]
-computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
-consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
-excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pyxlsb (>=1.0.9)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)"]
-feather = ["pyarrow (>=7.0.0)"]
-fss = ["fsspec (>=2022.05.0)"]
-gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
-hdf5 = ["tables (>=3.7.0)"]
-html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
-mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
-output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
-parquet = ["pyarrow (>=7.0.0)"]
-performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
-plot = ["matplotlib (>=3.6.1)"]
-postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
-spss = ["pyreadstat (>=1.1.5)"]
-sql-other = ["SQLAlchemy (>=1.4.36)"]
-test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
-xml = ["lxml (>=4.8.0)"]
-
[[package]]
name = "parso"
version = "0.8.3"
@@ -1707,6 +1504,16 @@ files = [
qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
testing = ["docopt", "pytest (<6.0.0)"]
+[[package]]
+name = "pathtools"
+version = "0.1.2"
+description = "File system general utilities"
+optional = false
+python-versions = "*"
+files = [
+ {file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"},
+]
+
[[package]]
name = "peft"
version = "0.5.0"
@@ -1831,6 +1638,22 @@ files = [
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+[[package]]
+name = "promise"
+version = "2.3"
+description = "Promises/A+ implementation for Python"
+optional = false
+python-versions = "*"
+files = [
+ {file = "promise-2.3.tar.gz", hash = "sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0"},
+]
+
+[package.dependencies]
+six = "*"
+
+[package.extras]
+test = ["coveralls", "futures", "mock", "pytest (>=2.7.3)", "pytest-benchmark", "pytest-cov"]
+
[[package]]
name = "prompt-toolkit"
version = "3.0.40"
@@ -1845,6 +1668,37 @@ files = [
[package.dependencies]
wcwidth = "*"
+[[package]]
+name = "protobuf"
+version = "3.20.3"
+description = "Protocol Buffers"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "protobuf-3.20.3-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:f4bd856d702e5b0d96a00ec6b307b0f51c1982c2bf9c0052cf9019e9a544ba99"},
+ {file = "protobuf-3.20.3-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:9aae4406ea63d825636cc11ffb34ad3379335803216ee3a856787bcf5ccc751e"},
+ {file = "protobuf-3.20.3-cp310-cp310-win32.whl", hash = "sha256:28545383d61f55b57cf4df63eebd9827754fd2dc25f80c5253f9184235db242c"},
+ {file = "protobuf-3.20.3-cp310-cp310-win_amd64.whl", hash = "sha256:67a3598f0a2dcbc58d02dd1928544e7d88f764b47d4a286202913f0b2801c2e7"},
+ {file = "protobuf-3.20.3-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:899dc660cd599d7352d6f10d83c95df430a38b410c1b66b407a6b29265d66469"},
+ {file = "protobuf-3.20.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e64857f395505ebf3d2569935506ae0dfc4a15cb80dc25261176c784662cdcc4"},
+ {file = "protobuf-3.20.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:d9e4432ff660d67d775c66ac42a67cf2453c27cb4d738fc22cb53b5d84c135d4"},
+ {file = "protobuf-3.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:74480f79a023f90dc6e18febbf7b8bac7508420f2006fabd512013c0c238f454"},
+ {file = "protobuf-3.20.3-cp37-cp37m-win32.whl", hash = "sha256:b6cc7ba72a8850621bfec987cb72623e703b7fe2b9127a161ce61e61558ad905"},
+ {file = "protobuf-3.20.3-cp37-cp37m-win_amd64.whl", hash = "sha256:8c0c984a1b8fef4086329ff8dd19ac77576b384079247c770f29cc8ce3afa06c"},
+ {file = "protobuf-3.20.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:de78575669dddf6099a8a0f46a27e82a1783c557ccc38ee620ed8cc96d3be7d7"},
+ {file = "protobuf-3.20.3-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:f4c42102bc82a51108e449cbb32b19b180022941c727bac0cfd50170341f16ee"},
+ {file = "protobuf-3.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:44246bab5dd4b7fbd3c0c80b6f16686808fab0e4aca819ade6e8d294a29c7050"},
+ {file = "protobuf-3.20.3-cp38-cp38-win32.whl", hash = "sha256:c02ce36ec760252242a33967d51c289fd0e1c0e6e5cc9397e2279177716add86"},
+ {file = "protobuf-3.20.3-cp38-cp38-win_amd64.whl", hash = "sha256:447d43819997825d4e71bf5769d869b968ce96848b6479397e29fc24c4a5dfe9"},
+ {file = "protobuf-3.20.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:398a9e0c3eaceb34ec1aee71894ca3299605fa8e761544934378bbc6c97de23b"},
+ {file = "protobuf-3.20.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:bf01b5720be110540be4286e791db73f84a2b721072a3711efff6c324cdf074b"},
+ {file = "protobuf-3.20.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:daa564862dd0d39c00f8086f88700fdbe8bc717e993a21e90711acfed02f2402"},
+ {file = "protobuf-3.20.3-cp39-cp39-win32.whl", hash = "sha256:819559cafa1a373b7096a482b504ae8a857c89593cf3a25af743ac9ecbd23480"},
+ {file = "protobuf-3.20.3-cp39-cp39-win_amd64.whl", hash = "sha256:03038ac1cfbc41aa21f6afcbcd357281d7521b4157926f30ebecc8d4ea59dcb7"},
+ {file = "protobuf-3.20.3-py2.py3-none-any.whl", hash = "sha256:a7ca6d488aa8ff7f329d4c545b2dbad8ac31464f1d8b1c87ad1346717731e4db"},
+ {file = "protobuf-3.20.3.tar.gz", hash = "sha256:2e3427429c9cffebf259491be0af70189607f365c2f41c7c3764af6f337105f2"},
+]
+
[[package]]
name = "psutil"
version = "5.9.6"
@@ -1899,52 +1753,18 @@ files = [
tests = ["pytest"]
[[package]]
-name = "pyarrow"
-version = "14.0.1"
-description = "Python library for Apache Arrow"
+name = "pybind11"
+version = "2.11.1"
+description = "Seamless operability between C++11 and Python"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.6"
files = [
- {file = "pyarrow-14.0.1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:96d64e5ba7dceb519a955e5eeb5c9adcfd63f73a56aea4722e2cc81364fc567a"},
- {file = "pyarrow-14.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1a8ae88c0038d1bc362a682320112ee6774f006134cd5afc291591ee4bc06505"},
- {file = "pyarrow-14.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f6f053cb66dc24091f5511e5920e45c83107f954a21032feadc7b9e3a8e7851"},
- {file = "pyarrow-14.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:906b0dc25f2be12e95975722f1e60e162437023f490dbd80d0deb7375baf3171"},
- {file = "pyarrow-14.0.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:78d4a77a46a7de9388b653af1c4ce539350726cd9af62e0831e4f2bd0c95a2f4"},
- {file = "pyarrow-14.0.1-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:06ca79080ef89d6529bb8e5074d4b4f6086143b2520494fcb7cf8a99079cde93"},
- {file = "pyarrow-14.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:32542164d905002c42dff896efdac79b3bdd7291b1b74aa292fac8450d0e4dcd"},
- {file = "pyarrow-14.0.1-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:c7331b4ed3401b7ee56f22c980608cf273f0380f77d0f73dd3c185f78f5a6220"},
- {file = "pyarrow-14.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:922e8b49b88da8633d6cac0e1b5a690311b6758d6f5d7c2be71acb0f1e14cd61"},
- {file = "pyarrow-14.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:58c889851ca33f992ea916b48b8540735055201b177cb0dcf0596a495a667b00"},
- {file = "pyarrow-14.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:30d8494870d9916bb53b2a4384948491444741cb9a38253c590e21f836b01222"},
- {file = "pyarrow-14.0.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:be28e1a07f20391bb0b15ea03dcac3aade29fc773c5eb4bee2838e9b2cdde0cb"},
- {file = "pyarrow-14.0.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:981670b4ce0110d8dcb3246410a4aabf5714db5d8ea63b15686bce1c914b1f83"},
- {file = "pyarrow-14.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:4756a2b373a28f6166c42711240643fb8bd6322467e9aacabd26b488fa41ec23"},
- {file = "pyarrow-14.0.1-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:cf87e2cec65dd5cf1aa4aba918d523ef56ef95597b545bbaad01e6433851aa10"},
- {file = "pyarrow-14.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:470ae0194fbfdfbf4a6b65b4f9e0f6e1fa0ea5b90c1ee6b65b38aecee53508c8"},
- {file = "pyarrow-14.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6263cffd0c3721c1e348062997babdf0151301f7353010c9c9a8ed47448f82ab"},
- {file = "pyarrow-14.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a8089d7e77d1455d529dbd7cff08898bbb2666ee48bc4085203af1d826a33cc"},
- {file = "pyarrow-14.0.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:fada8396bc739d958d0b81d291cfd201126ed5e7913cb73de6bc606befc30226"},
- {file = "pyarrow-14.0.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:2a145dab9ed7849fc1101bf03bcdc69913547f10513fdf70fc3ab6c0a50c7eee"},
- {file = "pyarrow-14.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:05fe7994745b634c5fb16ce5717e39a1ac1fac3e2b0795232841660aa76647cd"},
- {file = "pyarrow-14.0.1-cp38-cp38-macosx_10_14_x86_64.whl", hash = "sha256:a8eeef015ae69d104c4c3117a6011e7e3ecd1abec79dc87fd2fac6e442f666ee"},
- {file = "pyarrow-14.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3c76807540989fe8fcd02285dd15e4f2a3da0b09d27781abec3adc265ddbeba1"},
- {file = "pyarrow-14.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:450e4605e3c20e558485f9161a79280a61c55efe585d51513c014de9ae8d393f"},
- {file = "pyarrow-14.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:323cbe60210173ffd7db78bfd50b80bdd792c4c9daca8843ef3cd70b186649db"},
- {file = "pyarrow-14.0.1-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:0140c7e2b740e08c5a459439d87acd26b747fc408bde0a8806096ee0baaa0c15"},
- {file = "pyarrow-14.0.1-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:e592e482edd9f1ab32f18cd6a716c45b2c0f2403dc2af782f4e9674952e6dd27"},
- {file = "pyarrow-14.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:d264ad13605b61959f2ae7c1d25b1a5b8505b112715c961418c8396433f213ad"},
- {file = "pyarrow-14.0.1-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:01e44de9749cddc486169cb632f3c99962318e9dacac7778315a110f4bf8a450"},
- {file = "pyarrow-14.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d0351fecf0e26e152542bc164c22ea2a8e8c682726fce160ce4d459ea802d69c"},
- {file = "pyarrow-14.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:33c1f6110c386464fd2e5e4ea3624466055bbe681ff185fd6c9daa98f30a3f9a"},
- {file = "pyarrow-14.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11e045dfa09855b6d3e7705a37c42e2dc2c71d608fab34d3c23df2e02df9aec3"},
- {file = "pyarrow-14.0.1-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:097828b55321897db0e1dbfc606e3ff8101ae5725673498cbfa7754ee0da80e4"},
- {file = "pyarrow-14.0.1-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:1daab52050a1c48506c029e6fa0944a7b2436334d7e44221c16f6f1b2cc9c510"},
- {file = "pyarrow-14.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:3f6d5faf4f1b0d5a7f97be987cf9e9f8cd39902611e818fe134588ee99bf0283"},
- {file = "pyarrow-14.0.1.tar.gz", hash = "sha256:b8b3f4fe8d4ec15e1ef9b599b94683c5216adaed78d5cb4c606180546d1e2ee1"},
+ {file = "pybind11-2.11.1-py3-none-any.whl", hash = "sha256:33cdd02a6453380dd71cc70357ce388ad1ee8d32bd0e38fc22b273d050aa29b3"},
+ {file = "pybind11-2.11.1.tar.gz", hash = "sha256:00cd59116a6e8155aecd9174f37ba299d1d397ed4a6b86ac1dfe01b3e40f2cc4"},
]
-[package.dependencies]
-numpy = ">=1.16.6"
+[package.extras]
+global = ["pybind11-global (==2.11.1)"]
[[package]]
name = "pycparser"
@@ -1957,6 +1777,72 @@ files = [
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
]
+[[package]]
+name = "pygame"
+version = "2.5.2"
+description = "Python Game Development"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "pygame-2.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a0769eb628c818761755eb0a0ca8216b95270ea8cbcbc82227e39ac9644643da"},
+ {file = "pygame-2.5.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed9a3d98adafa0805ccbaaff5d2996a2b5795381285d8437a4a5d248dbd12b4a"},
+ {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f30d1618672a55e8c6669281ba264464b3ab563158e40d89e8c8b3faa0febebd"},
+ {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:39690e9be9baf58b7359d1f3b2336e1fd6f92fedbbce42987be5df27f8d30718"},
+ {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03879ec299c9f4ba23901b2649a96b2143f0a5d787f0b6c39469989e2320caf1"},
+ {file = "pygame-2.5.2-cp310-cp310-win32.whl", hash = "sha256:74e1d6284100e294f445832e6f6343be4fe4748decc4f8a51131ae197dae8584"},
+ {file = "pygame-2.5.2-cp310-cp310-win_amd64.whl", hash = "sha256:485239c7d32265fd35b76ae8f64f34b0637ae11e69d76de15710c4b9edcc7c8d"},
+ {file = "pygame-2.5.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:34646ca20e163dc6f6cf8170f1e12a2e41726780112594ac061fa448cf7ccd75"},
+ {file = "pygame-2.5.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3b8a6e351665ed26ea791f0e1fd649d3f483e8681892caef9d471f488f9ea5ee"},
+ {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc346965847aef00013fa2364f41a64f068cd096dcc7778fc306ca3735f0eedf"},
+ {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35632035fd81261f2d797fa810ea8c46111bd78ceb6089d52b61ed7dc3c5d05f"},
+ {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0e24d05184e4195fe5ebcdce8b18ecb086f00182b9ae460a86682d312ce8d31f"},
+ {file = "pygame-2.5.2-cp311-cp311-win32.whl", hash = "sha256:f02c1c7505af18d426d355ac9872bd5c916b27f7b0fe224749930662bea47a50"},
+ {file = "pygame-2.5.2-cp311-cp311-win_amd64.whl", hash = "sha256:6d58c8cf937815d3b7cdc0fa9590c5129cb2c9658b72d00e8a4568dea2ff1d42"},
+ {file = "pygame-2.5.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1a2a43802bb5e89ce2b3b775744e78db4f9a201bf8d059b946c61722840ceea8"},
+ {file = "pygame-2.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1c289f2613c44fe70a1e40769de4a49c5ab5a29b9376f1692bb1a15c9c1c9bfa"},
+ {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:074aa6c6e110c925f7f27f00c7733c6303407edc61d738882985091d1eb2ef17"},
+ {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fe0228501ec616779a0b9c4299e837877783e18df294dd690b9ab0eed3d8aaab"},
+ {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:31648d38ecdc2335ffc0e38fb18a84b3339730521505dac68514f83a1092e3f4"},
+ {file = "pygame-2.5.2-cp312-cp312-win32.whl", hash = "sha256:224c308856334bc792f696e9278e50d099a87c116f7fc314cd6aa3ff99d21592"},
+ {file = "pygame-2.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:dd2d2650faf54f9a0f5bd0db8409f79609319725f8f08af6507a0609deadcad4"},
+ {file = "pygame-2.5.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:9b30bc1220c457169571aac998e54b013aaeb732d2fd8744966cb1cfab1f61d1"},
+ {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78fcd7643358b886a44127ff7dec9041c056c212b3a98977674f83f99e9b12d3"},
+ {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35cf093a51cb294ede56c29d4acf41538c00f297fcf78a9b186fb7d23c0577b6"},
+ {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fe323acbf53a0195c8c98b1b941eba7ac24e3e2b28ae48e8cda566f15fc4945"},
+ {file = "pygame-2.5.2-cp36-cp36m-win32.whl", hash = "sha256:5697528266b4716d9cdd44a5a1d210f4d86ef801d0f64ca5da5d0816704009d9"},
+ {file = "pygame-2.5.2-cp36-cp36m-win_amd64.whl", hash = "sha256:edda1f7cff4806a4fa39e0e8ccd75f38d1d340fa5fc52d8582ade87aca247d92"},
+ {file = "pygame-2.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9bd738fd4ecc224769d0b4a719f96900a86578e26e0105193658a32966df2aae"},
+ {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30a8d7cf12363b4140bf2f93b5eec4028376ca1d0fe4b550588f836279485308"},
+ {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bc12e4dea3e88ea8a553de6d56a37b704dbe2aed95105889f6afeb4b96e62097"},
+ {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b34c73cb328024f8db3cb6487a37e54000148988275d8d6e5adf99d9323c937"},
+ {file = "pygame-2.5.2-cp37-cp37m-win32.whl", hash = "sha256:7d0a2794649defa57ef50b096a99f7113d3d0c2e32d1426cafa7d618eadce4c7"},
+ {file = "pygame-2.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:41f8779f52e0f6e6e6ccb8f0b5536e432bf386ee29c721a1c22cada7767b0cef"},
+ {file = "pygame-2.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:677e37bc0ea7afd89dde5a88ced4458aa8656159c70a576eea68b5622ee1997b"},
+ {file = "pygame-2.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:47a8415d2bd60e6909823b5643a1d4ef5cc29417d817f2a214b255f6fa3a1e4c"},
+ {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ff21201df6278b8ca2e948fb148ffe88f5481fd03760f381dd61e45954c7dff"},
+ {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d29a84b2e02814b9ba925357fd2e1df78efe5e1aa64dc3051eaed95d2b96eafd"},
+ {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d78485c4d21133d6b2fbb504cd544ca655e50b6eb551d2995b3aa6035928adda"},
+ {file = "pygame-2.5.2-cp38-cp38-win32.whl", hash = "sha256:d851247239548aa357c4a6840fb67adc2d570ce7cb56988d036a723d26b48bff"},
+ {file = "pygame-2.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:88d1cdacc2d3471eceab98bf0c93c14d3a8461f93e58e3d926f20d4de3a75554"},
+ {file = "pygame-2.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4f1559e7efe4efb9dc19d2d811d702f325d9605f9f6f9ececa39ee6890c798f5"},
+ {file = "pygame-2.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cf2191b756ceb0e8458a761d0c665b0c70b538570449e0d39b75a5ba94ac5cf0"},
+ {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6cf2257447ce7f2d6de37e5fb019d2bbe32ed05a5721ace8bc78c2d9beaf3aee"},
+ {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d75cbbfaba2b81434d62631d0b08b85fab16cf4a36e40b80298d3868927e1299"},
+ {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daca456d5b9f52e088e06a127dec182b3638a775684fb2260f25d664351cf1ae"},
+ {file = "pygame-2.5.2-cp39-cp39-win32.whl", hash = "sha256:3b3e619e33d11c297d7a57a82db40681f9c2c3ae1d5bf06003520b4fe30c435d"},
+ {file = "pygame-2.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:1822d534bb7fe756804647b6da2c9ea5d7a62d8796b2e15d172d3be085de28c6"},
+ {file = "pygame-2.5.2-pp36-pypy36_pp73-win32.whl", hash = "sha256:e708fc8f709a0fe1d1876489345f2e443d47f3976d33455e2e1e937f972f8677"},
+ {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c13edebc43c240fb0532969e914f0ccefff5ae7e50b0b788d08ad2c15ef793e4"},
+ {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:263b4a7cbfc9fe2055abc21b0251cc17dea6dff750f0e1c598919ff350cdbffe"},
+ {file = "pygame-2.5.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e58e2b0c791041e4bccafa5bd7650623ba1592b8fe62ae0a276b7d0ecb314b6c"},
+ {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a0bd67426c02ffe6c9827fc4bcbda9442fbc451d29b17c83a3c088c56fef2c90"},
+ {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9dcff6cbba1584cf7732ce1dbdd044406cd4f6e296d13bcb7fba963fb4aeefc9"},
+ {file = "pygame-2.5.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:ce4b6c0bfe44d00bb0998a6517bd0cf9455f642f30f91bc671ad41c05bf6f6ae"},
+ {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:68c4e8e60b725ffc7a6c6ecd9bb5fcc5ed2d6e0e2a2c4a29a8454856ef16ad63"},
+ {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f3849f97372a3381c66955f99a0d58485ccd513c3d00c030b869094ce6997a6"},
+ {file = "pygame-2.5.2.tar.gz", hash = "sha256:c1b89eb5d539e7ac5cf75513125fb5f2f0a2d918b1fd6e981f23bf0ac1b1c24a"},
+]
+
[[package]]
name = "pygments"
version = "2.16.1"
@@ -1999,66 +1885,6 @@ files = [
[package.dependencies]
six = ">=1.5"
-[[package]]
-name = "pytorch-lightning"
-version = "2.1.1"
-description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "pytorch-lightning-2.1.1.tar.gz", hash = "sha256:b55b53fa0a57b8c072ec4c446219911eff7cf19432eeaa63639bf1d89c0436d5"},
- {file = "pytorch_lightning-2.1.1-py3-none-any.whl", hash = "sha256:62969e065fda39a54aba2bd1e2314285e581003ebe37158130754c0111178d06"},
-]
-
-[package.dependencies]
-fsspec = {version = ">2021.06.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0"
-numpy = ">=1.17.2"
-packaging = ">=20.0"
-PyYAML = ">=5.4"
-torch = ">=1.12.0"
-torchmetrics = ">=0.7.0"
-tqdm = ">=4.57.0"
-typing-extensions = ">=4.0.0"
-
-[package.extras]
-all = ["bitsandbytes (<=0.41.1)", "deepspeed (>=0.8.2,<=0.9.3)", "gym[classic-control] (>=0.17.0)", "hydra-core (>=1.0.5)", "ipython[all] (<8.15.0)", "jsonargparse[signatures] (>=4.18.0)", "lightning-utilities (>=0.8.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "rich (>=12.3.0)", "tensorboardX (>=2.2)", "torchmetrics (>=0.10.0)", "torchvision (>=0.13.0)"]
-deepspeed = ["deepspeed (>=0.8.2,<=0.9.3)"]
-dev = ["bitsandbytes (<=0.41.1)", "cloudpickle (>=1.3)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "fastapi", "gym[classic-control] (>=0.17.0)", "hydra-core (>=1.0.5)", "ipython[all] (<8.15.0)", "jsonargparse[signatures] (>=4.18.0)", "lightning-utilities (>=0.8.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "rich (>=12.3.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "tensorboardX (>=2.2)", "torchmetrics (>=0.10.0)", "torchvision (>=0.13.0)", "uvicorn"]
-examples = ["gym[classic-control] (>=0.17.0)", "ipython[all] (<8.15.0)", "lightning-utilities (>=0.8.0)", "torchmetrics (>=0.10.0)", "torchvision (>=0.13.0)"]
-extra = ["bitsandbytes (<=0.41.1)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.18.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "rich (>=12.3.0)", "tensorboardX (>=2.2)"]
-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
-test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"]
-
-[[package]]
-name = "pytorch-optimizer"
-version = "2.12.0"
-description = "optimizer & lr scheduler & objective function collections in PyTorch"
-optional = false
-python-versions = ">=3.7.2,<4.0.0"
-files = [
- {file = "pytorch_optimizer-2.12.0-py3-none-any.whl", hash = "sha256:1683f305265c9428b486365fc1286d792e6cacac97eb10395a725d460d441152"},
- {file = "pytorch_optimizer-2.12.0.tar.gz", hash = "sha256:81bd70988b564853f8a0e5ec3f787e385b727d205782a95d209145939dddec79"},
-]
-
-[package.dependencies]
-numpy = {version = "*", markers = "python_version >= \"3.8\""}
-torch = {version = ">=1.10", markers = "python_version >= \"3.8\""}
-
-[package.extras]
-bitsandbytes = ["bitsandbytes (>=0.41,<0.42)"]
-
-[[package]]
-name = "pytz"
-version = "2023.3.post1"
-description = "World timezone definitions, modern and historical"
-optional = false
-python-versions = "*"
-files = [
- {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
- {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
-]
-
[[package]]
name = "pywin32"
version = "306"
@@ -2364,6 +2190,83 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+[[package]]
+name = "ruamel-yaml"
+version = "0.18.5"
+description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "ruamel.yaml-0.18.5-py3-none-any.whl", hash = "sha256:a013ac02f99a69cdd6277d9664689eb1acba07069f912823177c5eced21a6ada"},
+ {file = "ruamel.yaml-0.18.5.tar.gz", hash = "sha256:61917e3a35a569c1133a8f772e1226961bf5a1198bea7e23f06a0841dea1ab0e"},
+]
+
+[package.dependencies]
+"ruamel.yaml.clib" = {version = ">=0.2.7", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.13\""}
+
+[package.extras]
+docs = ["mercurial (>5.7)", "ryd"]
+jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
+
+[[package]]
+name = "ruamel-yaml-clib"
+version = "0.2.8"
+description = "C version of reader, parser and emitter for ruamel.yaml derived from libyaml"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b42169467c42b692c19cf539c38d4602069d8c1505e97b86387fcf7afb766e1d"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:07238db9cbdf8fc1e9de2489a4f68474e70dffcb32232db7c08fa61ca0c7c462"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:fff3573c2db359f091e1589c3d7c5fc2f86f5bdb6f24252c2d8e539d4e45f412"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_24_aarch64.whl", hash = "sha256:aa2267c6a303eb483de8d02db2871afb5c5fc15618d894300b88958f729ad74f"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:840f0c7f194986a63d2c2465ca63af8ccbbc90ab1c6001b1978f05119b5e7334"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:024cfe1fc7c7f4e1aff4a81e718109e13409767e4f871443cbff3dba3578203d"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-win32.whl", hash = "sha256:c69212f63169ec1cfc9bb44723bf2917cbbd8f6191a00ef3410f5a7fe300722d"},
+ {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-win_amd64.whl", hash = "sha256:cabddb8d8ead485e255fe80429f833172b4cadf99274db39abc080e068cbcc31"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:bef08cd86169d9eafb3ccb0a39edb11d8e25f3dae2b28f5c52fd997521133069"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-macosx_13_0_arm64.whl", hash = "sha256:b16420e621d26fdfa949a8b4b47ade8810c56002f5389970db4ddda51dbff248"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:25c515e350e5b739842fc3228d662413ef28f295791af5e5110b543cf0b57d9b"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-manylinux_2_24_aarch64.whl", hash = "sha256:1707814f0d9791df063f8c19bb51b0d1278b8e9a2353abbb676c2f685dee6afe"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:46d378daaac94f454b3a0e3d8d78cafd78a026b1d71443f4966c696b48a6d899"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:09b055c05697b38ecacb7ac50bdab2240bfca1a0c4872b0fd309bb07dc9aa3a9"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-win32.whl", hash = "sha256:53a300ed9cea38cf5a2a9b069058137c2ca1ce658a874b79baceb8f892f915a7"},
+ {file = "ruamel.yaml.clib-0.2.8-cp311-cp311-win_amd64.whl", hash = "sha256:c2a72e9109ea74e511e29032f3b670835f8a59bbdc9ce692c5b4ed91ccf1eedb"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:ebc06178e8821efc9692ea7544aa5644217358490145629914d8020042c24aa1"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-macosx_13_0_arm64.whl", hash = "sha256:edaef1c1200c4b4cb914583150dcaa3bc30e592e907c01117c08b13a07255ec2"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d176b57452ab5b7028ac47e7b3cf644bcfdc8cacfecf7e71759f7f51a59e5c92"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-manylinux_2_24_aarch64.whl", hash = "sha256:1dc67314e7e1086c9fdf2680b7b6c2be1c0d8e3a8279f2e993ca2a7545fecf62"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3213ece08ea033eb159ac52ae052a4899b56ecc124bb80020d9bbceeb50258e9"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:aab7fd643f71d7946f2ee58cc88c9b7bfc97debd71dcc93e03e2d174628e7e2d"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-win32.whl", hash = "sha256:5c365d91c88390c8d0a8545df0b5857172824b1c604e867161e6b3d59a827eaa"},
+ {file = "ruamel.yaml.clib-0.2.8-cp312-cp312-win_amd64.whl", hash = "sha256:1758ce7d8e1a29d23de54a16ae867abd370f01b5a69e1a3ba75223eaa3ca1a1b"},
+ {file = "ruamel.yaml.clib-0.2.8-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:a5aa27bad2bb83670b71683aae140a1f52b0857a2deff56ad3f6c13a017a26ed"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c58ecd827313af6864893e7af0a3bb85fd529f862b6adbefe14643947cfe2942"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-macosx_12_0_arm64.whl", hash = "sha256:f481f16baec5290e45aebdc2a5168ebc6d35189ae6fea7a58787613a25f6e875"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-manylinux_2_24_aarch64.whl", hash = "sha256:77159f5d5b5c14f7c34073862a6b7d34944075d9f93e681638f6d753606c6ce6"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:7f67a1ee819dc4562d444bbafb135832b0b909f81cc90f7aa00260968c9ca1b3"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:4ecbf9c3e19f9562c7fdd462e8d18dd902a47ca046a2e64dba80699f0b6c09b7"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:87ea5ff66d8064301a154b3933ae406b0863402a799b16e4a1d24d9fbbcbe0d3"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-win32.whl", hash = "sha256:75e1ed13e1f9de23c5607fe6bd1aeaae21e523b32d83bb33918245361e9cc51b"},
+ {file = "ruamel.yaml.clib-0.2.8-cp37-cp37m-win_amd64.whl", hash = "sha256:3f215c5daf6a9d7bbed4a0a4f760f3113b10e82ff4c5c44bec20a68c8014f675"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1b617618914cb00bf5c34d4357c37aa15183fa229b24767259657746c9077615"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:a6a9ffd280b71ad062eae53ac1659ad86a17f59a0fdc7699fd9be40525153337"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-manylinux_2_24_aarch64.whl", hash = "sha256:305889baa4043a09e5b76f8e2a51d4ffba44259f6b4c72dec8ca56207d9c6fe1"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:700e4ebb569e59e16a976857c8798aee258dceac7c7d6b50cab63e080058df91"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:e2b4c44b60eadec492926a7270abb100ef9f72798e18743939bdbf037aab8c28"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:e79e5db08739731b0ce4850bed599235d601701d5694c36570a99a0c5ca41a9d"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-win32.whl", hash = "sha256:955eae71ac26c1ab35924203fda6220f84dce57d6d7884f189743e2abe3a9fbe"},
+ {file = "ruamel.yaml.clib-0.2.8-cp38-cp38-win_amd64.whl", hash = "sha256:56f4252222c067b4ce51ae12cbac231bce32aee1d33fbfc9d17e5b8d6966c312"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:03d1162b6d1df1caa3a4bd27aa51ce17c9afc2046c31b0ad60a0a96ec22f8001"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba64af9fa9cebe325a62fa398760f5c7206b215201b0ec825005f1b18b9bccf"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-manylinux_2_24_aarch64.whl", hash = "sha256:a1a45e0bb052edf6a1d3a93baef85319733a888363938e1fc9924cb00c8df24c"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:da09ad1c359a728e112d60116f626cc9f29730ff3e0e7db72b9a2dbc2e4beed5"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:184565012b60405d93838167f425713180b949e9d8dd0bbc7b49f074407c5a8b"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a75879bacf2c987c003368cf14bed0ffe99e8e85acfa6c0bfffc21a090f16880"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-win32.whl", hash = "sha256:84b554931e932c46f94ab306913ad7e11bba988104c5cff26d90d03f68258cd5"},
+ {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-win_amd64.whl", hash = "sha256:25ac8c08322002b06fa1d49d1646181f0b2c72f5cbc15a85e80b4c30a544bb15"},
+ {file = "ruamel.yaml.clib-0.2.8.tar.gz", hash = "sha256:beb2e0404003de9a4cab9753a8805a8fe9320ee6673136ed7f04255fe60bb512"},
+]
+
[[package]]
name = "ruff"
version = "0.1.5"
@@ -2509,53 +2412,6 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
torch = ["safetensors[numpy]", "torch (>=1.10)"]
-[[package]]
-name = "scikit-learn"
-version = "1.3.2"
-description = "A set of python modules for machine learning and data mining"
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "scikit-learn-1.3.2.tar.gz", hash = "sha256:a2f54c76accc15a34bfb9066e6c7a56c1e7235dda5762b990792330b52ccfb05"},
- {file = "scikit_learn-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e326c0eb5cf4d6ba40f93776a20e9a7a69524c4db0757e7ce24ba222471ee8a1"},
- {file = "scikit_learn-1.3.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:535805c2a01ccb40ca4ab7d081d771aea67e535153e35a1fd99418fcedd1648a"},
- {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1215e5e58e9880b554b01187b8c9390bf4dc4692eedeaf542d3273f4785e342c"},
- {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ee107923a623b9f517754ea2f69ea3b62fc898a3641766cb7deb2f2ce450161"},
- {file = "scikit_learn-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:35a22e8015048c628ad099da9df5ab3004cdbf81edc75b396fd0cff8699ac58c"},
- {file = "scikit_learn-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6fb6bc98f234fda43163ddbe36df8bcde1d13ee176c6dc9b92bb7d3fc842eb66"},
- {file = "scikit_learn-1.3.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:18424efee518a1cde7b0b53a422cde2f6625197de6af36da0b57ec502f126157"},
- {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3271552a5eb16f208a6f7f617b8cc6d1f137b52c8a1ef8edf547db0259b2c9fb"},
- {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc4144a5004a676d5022b798d9e573b05139e77f271253a4703eed295bde0433"},
- {file = "scikit_learn-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:67f37d708f042a9b8d59551cf94d30431e01374e00dc2645fa186059c6c5d78b"},
- {file = "scikit_learn-1.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8db94cd8a2e038b37a80a04df8783e09caac77cbe052146432e67800e430c028"},
- {file = "scikit_learn-1.3.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:61a6efd384258789aa89415a410dcdb39a50e19d3d8410bd29be365bcdd512d5"},
- {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb06f8dce3f5ddc5dee1715a9b9f19f20d295bed8e3cd4fa51e1d050347de525"},
- {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b2de18d86f630d68fe1f87af690d451388bb186480afc719e5f770590c2ef6c"},
- {file = "scikit_learn-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107"},
- {file = "scikit_learn-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:a19f90f95ba93c1a7f7924906d0576a84da7f3b2282ac3bfb7a08a32801add93"},
- {file = "scikit_learn-1.3.2-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:b8692e395a03a60cd927125eef3a8e3424d86dde9b2370d544f0ea35f78a8073"},
- {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15e1e94cc23d04d39da797ee34236ce2375ddea158b10bee3c343647d615581d"},
- {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:785a2213086b7b1abf037aeadbbd6d67159feb3e30263434139c98425e3dcfcf"},
- {file = "scikit_learn-1.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:64381066f8aa63c2710e6b56edc9f0894cc7bf59bd71b8ce5613a4559b6145e0"},
- {file = "scikit_learn-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6c43290337f7a4b969d207e620658372ba3c1ffb611f8bc2b6f031dc5c6d1d03"},
- {file = "scikit_learn-1.3.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:dc9002fc200bed597d5d34e90c752b74df516d592db162f756cc52836b38fe0e"},
- {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d08ada33e955c54355d909b9c06a4789a729977f165b8bae6f225ff0a60ec4a"},
- {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:763f0ae4b79b0ff9cca0bf3716bcc9915bdacff3cebea15ec79652d1cc4fa5c9"},
- {file = "scikit_learn-1.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:ed932ea780517b00dae7431e031faae6b49b20eb6950918eb83bd043237950e0"},
-]
-
-[package.dependencies]
-joblib = ">=1.1.1"
-numpy = ">=1.17.3,<2.0"
-scipy = ">=1.5.0"
-threadpoolctl = ">=2.0.0"
-
-[package.extras]
-benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.10.1)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
-examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
-tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"]
-
[[package]]
name = "scipy"
version = "1.11.3"
@@ -2599,24 +2455,176 @@ doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sp
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
[[package]]
-name = "simple-parsing"
-version = "0.1.4"
-description = "A small utility for simplifying and cleaning up argument parsing scripts."
+name = "sentry-sdk"
+version = "1.34.0"
+description = "Python client for Sentry (https://sentry.io)"
optional = false
-python-versions = ">=3.7"
+python-versions = "*"
files = [
- {file = "simple_parsing-0.1.4-py3-none-any.whl", hash = "sha256:1fb042499d8872090ed3aad1f60f4d53026d2062942196a600933bc83787328e"},
- {file = "simple_parsing-0.1.4.tar.gz", hash = "sha256:312300327d8f0beb61b6419500423bd335be8b83da66f733e9c67c6c18341ae8"},
+ {file = "sentry-sdk-1.34.0.tar.gz", hash = "sha256:e5d0d2b25931d88fa10986da59d941ac6037f742ab6ff2fce4143a27981d60c3"},
+ {file = "sentry_sdk-1.34.0-py2.py3-none-any.whl", hash = "sha256:76dd087f38062ac6c1e30ed6feb533ee0037ff9e709974802db7b5dbf2e5db21"},
]
[package.dependencies]
-docstring-parser = ">=0.15,<1.0"
-typing-extensions = ">=4.5.0"
+certifi = "*"
+urllib3 = {version = ">=1.26.11", markers = "python_version >= \"3.6\""}
[package.extras]
-all = ["numpy", "pytest", "pytest-benchmark", "pytest-regressions", "pytest-xdist", "pyyaml"]
-test = ["numpy", "pytest", "pytest-benchmark", "pytest-regressions", "pytest-xdist"]
-yaml = ["pyyaml"]
+aiohttp = ["aiohttp (>=3.5)"]
+arq = ["arq (>=0.23)"]
+asyncpg = ["asyncpg (>=0.23)"]
+beam = ["apache-beam (>=2.12)"]
+bottle = ["bottle (>=0.12.13)"]
+celery = ["celery (>=3)"]
+chalice = ["chalice (>=1.16.0)"]
+clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
+django = ["django (>=1.8)"]
+falcon = ["falcon (>=1.4)"]
+fastapi = ["fastapi (>=0.79.0)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+grpcio = ["grpcio (>=1.21.1)"]
+httpx = ["httpx (>=0.16.0)"]
+huey = ["huey (>=2)"]
+loguru = ["loguru (>=0.5)"]
+opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
+opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
+pure-eval = ["asttokens", "executing", "pure-eval"]
+pymongo = ["pymongo (>=3.1)"]
+pyspark = ["pyspark (>=2.4.4)"]
+quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
+rq = ["rq (>=0.6)"]
+sanic = ["sanic (>=0.8)"]
+sqlalchemy = ["sqlalchemy (>=1.2)"]
+starlette = ["starlette (>=0.19.1)"]
+starlite = ["starlite (>=1.48)"]
+tornado = ["tornado (>=5)"]
+
+[[package]]
+name = "setproctitle"
+version = "1.3.3"
+description = "A Python module to customize the process title"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:897a73208da48db41e687225f355ce993167079eda1260ba5e13c4e53be7f754"},
+ {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8c331e91a14ba4076f88c29c777ad6b58639530ed5b24b5564b5ed2fd7a95452"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbbd6c7de0771c84b4aa30e70b409565eb1fc13627a723ca6be774ed6b9d9fa3"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c05ac48ef16ee013b8a326c63e4610e2430dbec037ec5c5b58fcced550382b74"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1342f4fdb37f89d3e3c1c0a59d6ddbedbde838fff5c51178a7982993d238fe4f"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc74e84fdfa96821580fb5e9c0b0777c1c4779434ce16d3d62a9c4d8c710df39"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9617b676b95adb412bb69645d5b077d664b6882bb0d37bfdafbbb1b999568d85"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6a249415f5bb88b5e9e8c4db47f609e0bf0e20a75e8d744ea787f3092ba1f2d0"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:38da436a0aaace9add67b999eb6abe4b84397edf4a78ec28f264e5b4c9d53cd5"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:da0d57edd4c95bf221b2ebbaa061e65b1788f1544977288bdf95831b6e44e44d"},
+ {file = "setproctitle-1.3.3-cp310-cp310-win32.whl", hash = "sha256:a1fcac43918b836ace25f69b1dca8c9395253ad8152b625064415b1d2f9be4fb"},
+ {file = "setproctitle-1.3.3-cp310-cp310-win_amd64.whl", hash = "sha256:200620c3b15388d7f3f97e0ae26599c0c378fdf07ae9ac5a13616e933cbd2086"},
+ {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:334f7ed39895d692f753a443102dd5fed180c571eb6a48b2a5b7f5b3564908c8"},
+ {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:950f6476d56ff7817a8fed4ab207727fc5260af83481b2a4b125f32844df513a"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:195c961f54a09eb2acabbfc90c413955cf16c6e2f8caa2adbf2237d1019c7dd8"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f05e66746bf9fe6a3397ec246fe481096664a9c97eb3fea6004735a4daf867fd"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b5901a31012a40ec913265b64e48c2a4059278d9f4e6be628441482dd13fb8b5"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64286f8a995f2cd934082b398fc63fca7d5ffe31f0e27e75b3ca6b4efda4e353"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:184239903bbc6b813b1a8fc86394dc6ca7d20e2ebe6f69f716bec301e4b0199d"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:664698ae0013f986118064b6676d7dcd28fefd0d7d5a5ae9497cbc10cba48fa5"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:e5119a211c2e98ff18b9908ba62a3bd0e3fabb02a29277a7232a6fb4b2560aa0"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:417de6b2e214e837827067048f61841f5d7fc27926f2e43954567094051aff18"},
+ {file = "setproctitle-1.3.3-cp311-cp311-win32.whl", hash = "sha256:6a143b31d758296dc2f440175f6c8e0b5301ced3b0f477b84ca43cdcf7f2f476"},
+ {file = "setproctitle-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:a680d62c399fa4b44899094027ec9a1bdaf6f31c650e44183b50d4c4d0ccc085"},
+ {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:d4460795a8a7a391e3567b902ec5bdf6c60a47d791c3b1d27080fc203d11c9dc"},
+ {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bdfd7254745bb737ca1384dee57e6523651892f0ea2a7344490e9caefcc35e64"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:477d3da48e216d7fc04bddab67b0dcde633e19f484a146fd2a34bb0e9dbb4a1e"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ab2900d111e93aff5df9fddc64cf51ca4ef2c9f98702ce26524f1acc5a786ae7"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:088b9efc62d5aa5d6edf6cba1cf0c81f4488b5ce1c0342a8b67ae39d64001120"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6d50252377db62d6a0bb82cc898089916457f2db2041e1d03ce7fadd4a07381"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:87e668f9561fd3a457ba189edfc9e37709261287b52293c115ae3487a24b92f6"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:287490eb90e7a0ddd22e74c89a92cc922389daa95babc833c08cf80c84c4df0a"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:4fe1c49486109f72d502f8be569972e27f385fe632bd8895f4730df3c87d5ac8"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4a6ba2494a6449b1f477bd3e67935c2b7b0274f2f6dcd0f7c6aceae10c6c6ba3"},
+ {file = "setproctitle-1.3.3-cp312-cp312-win32.whl", hash = "sha256:2df2b67e4b1d7498632e18c56722851ba4db5d6a0c91aaf0fd395111e51cdcf4"},
+ {file = "setproctitle-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:f38d48abc121263f3b62943f84cbaede05749047e428409c2c199664feb6abc7"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:816330675e3504ae4d9a2185c46b573105d2310c20b19ea2b4596a9460a4f674"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68f960bc22d8d8e4ac886d1e2e21ccbd283adcf3c43136161c1ba0fa509088e0"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:00e6e7adff74796ef12753ff399491b8827f84f6c77659d71bd0b35870a17d8f"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:53bc0d2358507596c22b02db079618451f3bd720755d88e3cccd840bafb4c41c"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad6d20f9541f5f6ac63df553b6d7a04f313947f550eab6a61aa758b45f0d5657"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c1c84beab776b0becaa368254801e57692ed749d935469ac10e2b9b825dbdd8e"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:507e8dc2891021350eaea40a44ddd887c9f006e6b599af8d64a505c0f718f170"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b1067647ac7aba0b44b591936118a22847bda3c507b0a42d74272256a7a798e9"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:2e71f6365744bf53714e8bd2522b3c9c1d83f52ffa6324bd7cbb4da707312cd8"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-win32.whl", hash = "sha256:7f1d36a1e15a46e8ede4e953abb104fdbc0845a266ec0e99cc0492a4364f8c44"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-win_amd64.whl", hash = "sha256:c9a402881ec269d0cc9c354b149fc29f9ec1a1939a777f1c858cdb09c7a261df"},
+ {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ff814dea1e5c492a4980e3e7d094286077054e7ea116cbeda138819db194b2cd"},
+ {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:accb66d7b3ccb00d5cd11d8c6e07055a4568a24c95cf86109894dcc0c134cc89"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:554eae5a5b28f02705b83a230e9d163d645c9a08914c0ad921df363a07cf39b1"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a911b26264dbe9e8066c7531c0591cfab27b464459c74385b276fe487ca91c12"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2982efe7640c4835f7355fdb4da313ad37fb3b40f5c69069912f8048f77b28c8"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df3f4274b80709d8bcab2f9a862973d453b308b97a0b423a501bcd93582852e3"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:af2c67ae4c795d1674a8d3ac1988676fa306bcfa1e23fddb5e0bd5f5635309ca"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:af4061f67fd7ec01624c5e3c21f6b7af2ef0e6bab7fbb43f209e6506c9ce0092"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:37a62cbe16d4c6294e84670b59cf7adcc73faafe6af07f8cb9adaf1f0e775b19"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:a83ca086fbb017f0d87f240a8f9bbcf0809f3b754ee01cec928fff926542c450"},
+ {file = "setproctitle-1.3.3-cp38-cp38-win32.whl", hash = "sha256:059f4ce86f8cc92e5860abfc43a1dceb21137b26a02373618d88f6b4b86ba9b2"},
+ {file = "setproctitle-1.3.3-cp38-cp38-win_amd64.whl", hash = "sha256:ab92e51cd4a218208efee4c6d37db7368fdf182f6e7ff148fb295ecddf264287"},
+ {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c7951820b77abe03d88b114b998867c0f99da03859e5ab2623d94690848d3e45"},
+ {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5bc94cf128676e8fac6503b37763adb378e2b6be1249d207630f83fc325d9b11"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f5d9027eeda64d353cf21a3ceb74bb1760bd534526c9214e19f052424b37e42"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e4a8104db15d3462e29d9946f26bed817a5b1d7a47eabca2d9dc2b995991503"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c32c41ace41f344d317399efff4cffb133e709cec2ef09c99e7a13e9f3b9483c"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbf16381c7bf7f963b58fb4daaa65684e10966ee14d26f5cc90f07049bfd8c1e"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e18b7bd0898398cc97ce2dfc83bb192a13a087ef6b2d5a8a36460311cb09e775"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:69d565d20efe527bd8a9b92e7f299ae5e73b6c0470f3719bd66f3cd821e0d5bd"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:ddedd300cd690a3b06e7eac90ed4452348b1348635777ce23d460d913b5b63c3"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:415bfcfd01d1fbf5cbd75004599ef167a533395955305f42220a585f64036081"},
+ {file = "setproctitle-1.3.3-cp39-cp39-win32.whl", hash = "sha256:21112fcd2195d48f25760f0eafa7a76510871bbb3b750219310cf88b04456ae3"},
+ {file = "setproctitle-1.3.3-cp39-cp39-win_amd64.whl", hash = "sha256:5a740f05d0968a5a17da3d676ce6afefebeeeb5ce137510901bf6306ba8ee002"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6b9e62ddb3db4b5205c0321dd69a406d8af9ee1693529d144e86bd43bcb4b6c0"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9e3b99b338598de0bd6b2643bf8c343cf5ff70db3627af3ca427a5e1a1a90dd9"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38ae9a02766dad331deb06855fb7a6ca15daea333b3967e214de12cfae8f0ef5"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:200ede6fd11233085ba9b764eb055a2a191fb4ffb950c68675ac53c874c22e20"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0d3a953c50776751e80fe755a380a64cb14d61e8762bd43041ab3f8cc436092f"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5e08e232b78ba3ac6bc0d23ce9e2bee8fad2be391b7e2da834fc9a45129eb87"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1da82c3e11284da4fcbf54957dafbf0655d2389cd3d54e4eaba636faf6d117a"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:aeaa71fb9568ebe9b911ddb490c644fbd2006e8c940f21cb9a1e9425bd709574"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:59335d000c6250c35989394661eb6287187854e94ac79ea22315469ee4f4c244"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c3ba57029c9c50ecaf0c92bb127224cc2ea9fda057b5d99d3f348c9ec2855ad3"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d876d355c53d975c2ef9c4f2487c8f83dad6aeaaee1b6571453cb0ee992f55f6"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:224602f0939e6fb9d5dd881be1229d485f3257b540f8a900d4271a2c2aa4e5f4"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d7f27e0268af2d7503386e0e6be87fb9b6657afd96f5726b733837121146750d"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f5e7266498cd31a4572378c61920af9f6b4676a73c299fce8ba93afd694f8ae7"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33c5609ad51cd99d388e55651b19148ea99727516132fb44680e1f28dd0d1de9"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:eae8988e78192fd1a3245a6f4f382390b61bce6cfcc93f3809726e4c885fa68d"},
+ {file = "setproctitle-1.3.3.tar.gz", hash = "sha256:c913e151e7ea01567837ff037a23ca8740192880198b7fbb90b16d181607caae"},
+]
+
+[package.extras]
+test = ["pytest"]
+
+[[package]]
+name = "setuptools"
+version = "68.2.2"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "setuptools-68.2.2-py3-none-any.whl", hash = "sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"},
+ {file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "shortuuid"
+version = "1.0.11"
+description = "A generator library for concise, unambiguous and URL-safe UUIDs."
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "shortuuid-1.0.11-py3-none-any.whl", hash = "sha256:27ea8f28b1bd0bf8f15057a3ece57275d2059d2b0bb02854f02189962c13b6aa"},
+ {file = "shortuuid-1.0.11.tar.gz", hash = "sha256:fc75f2615914815a8e4cb1501b3a513745cb66ef0fd5fc6fb9f8c3fa3481f789"},
+]
[[package]]
name = "six"
@@ -2629,6 +2637,17 @@ files = [
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
+ {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
[[package]]
name = "stack-data"
version = "0.6.3"
@@ -2662,17 +2681,6 @@ files = [
[package.dependencies]
mpmath = ">=0.19"
-[[package]]
-name = "threadpoolctl"
-version = "3.2.0"
-description = "threadpoolctl"
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
- {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
-]
-
[[package]]
name = "tokenizers"
version = "0.14.1"
@@ -2835,32 +2843,42 @@ files = [
]
[[package]]
-name = "torchmetrics"
-version = "1.2.0"
-description = "PyTorch native Metrics"
+name = "torchvision"
+version = "0.16.0"
+description = "image and video datasets and models for torch deep learning"
optional = false
python-versions = ">=3.8"
files = [
- {file = "torchmetrics-1.2.0-py3-none-any.whl", hash = "sha256:da2cb18822b285786d082c40efb9e1d861aac425f58230234fe6ce233cf002f8"},
- {file = "torchmetrics-1.2.0.tar.gz", hash = "sha256:7eb28340bde45e13187a9ad54a4a7010a50417815d8181a5df6131f116ffe1b7"},
+ {file = "torchvision-0.16.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:16c300fdbbe91469f5e9feef8d24c6acabd8849db502a06160dd76ba68e897a0"},
+ {file = "torchvision-0.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ef5dec6c48b715353781b83749efcdea03835720a71b377684453ee117aab3c7"},
+ {file = "torchvision-0.16.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:9e3a2012e463f498de21f6598cc7a266b9a8c6fe15788472fdc419233ea6f3f2"},
+ {file = "torchvision-0.16.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:e4327e082b703921ae52caeee4f7839f7e6c73cfc5eedea468ecb5c1487ecdbf"},
+ {file = "torchvision-0.16.0-cp310-cp310-win_amd64.whl", hash = "sha256:62f01513687cce3480df8928fcc6c09b4aa0433d05ac75e82877acc773f6a568"},
+ {file = "torchvision-0.16.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:31fdf289bdfb2976f65a14f79f6ddd1ee60113db34622674918e61521c2dc41f"},
+ {file = "torchvision-0.16.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2294a6514a31a6fda562288b28cf6db57877237f4b56ff693262f237a7ed4035"},
+ {file = "torchvision-0.16.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:6a24a1e83e4bc7a31b39ef05d2ca4cd2182e95ff10f525edffe1473f7ce16ca1"},
+ {file = "torchvision-0.16.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:9ed5f21e5a56e466667c6f9f6f93dba2a75e29921108bd70043eaf8e9ba0a7cc"},
+ {file = "torchvision-0.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:9ee3d4df7d4a84f883f8ad11fb6510549f40f68dd5469eae601d7e02fb4809b2"},
+ {file = "torchvision-0.16.0-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:0c6f36d00b9ce412e367ad6f42e9054cbc890cd9ddd0d200ed9b3b52dd9c225b"},
+ {file = "torchvision-0.16.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:597f60cb03e6f758a00b36b38506f6f38b6c3f1fdfd3921bb9abd60b72d522fd"},
+ {file = "torchvision-0.16.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:eddd91da4603f1dbb340d9aca82344df64605a0897b17014ac8e0b54dd6e5716"},
+ {file = "torchvision-0.16.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:79875f5247337723ec363762c2716bcfc13b78b3045e4e58847c696f03d9ed4d"},
+ {file = "torchvision-0.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:550c9793637c5369fbcb4e4b6b0e6d53a4f6cc22389f0563ad60ab90e4f1c8ba"},
+ {file = "torchvision-0.16.0-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:de7c7302fa2f67a2a151e595a8e7dc3865a445d952e99d5c682ba78f312fedc3"},
+ {file = "torchvision-0.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f044cffd252fd293b6df46f38d7eeb2fd4fe931e0114c5263735e3b8c9c60a4f"},
+ {file = "torchvision-0.16.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:8cb501061f6654da494dd975acc1fa301c4b8aacf96bdbcf1553f51a53ebfd1f"},
+ {file = "torchvision-0.16.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5a47108ae6a8effdf09fe35fd0c4d5414e69ca8d2334e87339de497b7b64b0c9"},
+ {file = "torchvision-0.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:9b8f06e6a2f80576007b88846f74b680a1ad3b59d2e22b075587b430180e9cfa"},
]
[package.dependencies]
-lightning-utilities = ">=0.8.0"
-numpy = ">1.20.0"
-torch = ">=1.8.1"
+numpy = "*"
+pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
+requests = "*"
+torch = "2.1.0"
[package.extras]
-all = ["SciencePlots (>=2.0.0)", "lpips (<=0.1.4)", "matplotlib (>=3.2.0)", "mypy (==1.5.1)", "nltk (>=3.6)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "regex (>=2021.9.24)", "scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
-audio = ["pystoi (>=0.3.0)", "torchaudio (>=0.10.0)"]
-detection = ["pycocotools (>2.0.0)", "torchvision (>=0.8)"]
-dev = ["SciencePlots (>=2.0.0)", "bert-score (==0.3.13)", "cloudpickle (>1.3)", "coverage (==7.3.1)", "dython (<=0.7.4)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.3.3)", "fire (<=0.5.0)", "huggingface-hub (<0.18)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "lpips (<=0.1.4)", "matplotlib (>=3.2.0)", "mir-eval (>=0.6)", "mypy (==1.5.1)", "netcal (>1.0.0)", "nltk (>=3.6)", "numpy (<1.25.0)", "pandas (>1.0.0)", "pandas (>=1.4.0)", "phmdoctest (==1.4.0)", "piq (<=0.8.0)", "psutil (<=5.9.5)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "requests (<=2.31.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.0.0)", "scikit-image (>=0.19.0)", "scikit-learn (>=1.1.1)", "scipy (>1.0.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch-complex (<=0.4.3)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
-image = ["lpips (<=0.1.4)", "scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.8)"]
-multimodal = ["piq (<=0.8.0)", "transformers (>=4.10.0)"]
-test = ["bert-score (==0.3.13)", "cloudpickle (>1.3)", "coverage (==7.3.1)", "dython (<=0.7.4)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.3.3)", "fire (<=0.5.0)", "huggingface-hub (<0.18)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "mir-eval (>=0.6)", "netcal (>1.0.0)", "numpy (<1.25.0)", "pandas (>1.0.0)", "pandas (>=1.4.0)", "phmdoctest (==1.4.0)", "psutil (<=5.9.5)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytorch-msssim (==1.0.0)", "requests (<=2.31.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.0.0)", "scikit-image (>=0.19.0)", "scikit-learn (>=1.1.1)", "scipy (>1.0.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch-complex (<=0.4.3)"]
-text = ["nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)", "transformers (>4.4.0)"]
-typing = ["mypy (==1.5.1)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
-visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.2.0)"]
+scipy = ["scipy"]
[[package]]
name = "tornado"
@@ -3022,17 +3040,6 @@ files = [
{file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
]
-[[package]]
-name = "tzdata"
-version = "2023.3"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-files = [
- {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
- {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
-]
-
[[package]]
name = "urllib3"
version = "2.0.7"
@@ -3050,6 +3057,44 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
+[[package]]
+name = "wandb"
+version = "0.12.21"
+description = "A CLI and library for interacting with the Weights and Biases API."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "wandb-0.12.21-py2.py3-none-any.whl", hash = "sha256:150842447d355d90dc7f368b824951a625e5b2d1be355a00e99b11b73728bc1f"},
+ {file = "wandb-0.12.21.tar.gz", hash = "sha256:1975ff88c5024923c3321c93cfefb8d9b871543c0b009f34001bf0f31e444b04"},
+]
+
+[package.dependencies]
+Click = ">=7.0,<8.0.0 || >8.0.0"
+docker-pycreds = ">=0.4.0"
+GitPython = ">=1.0.0"
+pathtools = "*"
+promise = ">=2.0,<3"
+protobuf = ">=3.12.0,<4.0dev"
+psutil = ">=5.0.0"
+PyYAML = "*"
+requests = ">=2.0.0,<3"
+sentry-sdk = ">=1.0.0"
+setproctitle = "*"
+setuptools = "*"
+shortuuid = ">=0.5.0"
+six = ">=1.13.0"
+
+[package.extras]
+aws = ["boto3"]
+azure = ["azure-storage-blob"]
+gcp = ["google-cloud-storage"]
+grpc = ["grpcio (>=1.27.2)"]
+kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
+launch = ["boto3", "chardet", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
+models = ["cloudpickle"]
+sweeps = ["sweeps (>=0.1.0)"]
+
[[package]]
name = "wcwidth"
version = "0.2.9"
@@ -3076,210 +3121,21 @@ files = [
dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
[[package]]
-name = "xxhash"
-version = "3.4.1"
-description = "Python binding for xxHash"
+name = "zipp"
+version = "3.17.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "xxhash-3.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:91dbfa55346ad3e18e738742236554531a621042e419b70ad8f3c1d9c7a16e7f"},
- {file = "xxhash-3.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:665a65c2a48a72068fcc4d21721510df5f51f1142541c890491afc80451636d2"},
- {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb11628470a6004dc71a09fe90c2f459ff03d611376c1debeec2d648f44cb693"},
- {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5bef2a7dc7b4f4beb45a1edbba9b9194c60a43a89598a87f1a0226d183764189"},
- {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c0f7b2d547d72c7eda7aa817acf8791f0146b12b9eba1d4432c531fb0352228"},
- {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00f2fdef6b41c9db3d2fc0e7f94cb3db86693e5c45d6de09625caad9a469635b"},
- {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:23cfd9ca09acaf07a43e5a695143d9a21bf00f5b49b15c07d5388cadf1f9ce11"},
- {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:6a9ff50a3cf88355ca4731682c168049af1ca222d1d2925ef7119c1a78e95b3b"},
- {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:f1d7c69a1e9ca5faa75546fdd267f214f63f52f12692f9b3a2f6467c9e67d5e7"},
- {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:672b273040d5d5a6864a36287f3514efcd1d4b1b6a7480f294c4b1d1ee1b8de0"},
- {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:4178f78d70e88f1c4a89ff1ffe9f43147185930bb962ee3979dba15f2b1cc799"},
- {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:9804b9eb254d4b8cc83ab5a2002128f7d631dd427aa873c8727dba7f1f0d1c2b"},
- {file = "xxhash-3.4.1-cp310-cp310-win32.whl", hash = "sha256:c09c49473212d9c87261d22c74370457cfff5db2ddfc7fd1e35c80c31a8c14ce"},
- {file = "xxhash-3.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:ebbb1616435b4a194ce3466d7247df23499475c7ed4eb2681a1fa42ff766aff6"},
- {file = "xxhash-3.4.1-cp310-cp310-win_arm64.whl", hash = "sha256:25dc66be3db54f8a2d136f695b00cfe88018e59ccff0f3b8f545869f376a8a46"},
- {file = "xxhash-3.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:58c49083801885273e262c0f5bbeac23e520564b8357fbb18fb94ff09d3d3ea5"},
- {file = "xxhash-3.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b526015a973bfbe81e804a586b703f163861da36d186627e27524f5427b0d520"},
- {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36ad4457644c91a966f6fe137d7467636bdc51a6ce10a1d04f365c70d6a16d7e"},
- {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:248d3e83d119770f96003271fe41e049dd4ae52da2feb8f832b7a20e791d2920"},
- {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2070b6d5bbef5ee031666cf21d4953c16e92c2f8a24a94b5c240f8995ba3b1d0"},
- {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2746035f518f0410915e247877f7df43ef3372bf36cfa52cc4bc33e85242641"},
- {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2a8ba6181514681c2591840d5632fcf7356ab287d4aff1c8dea20f3c78097088"},
- {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0aac5010869240e95f740de43cd6a05eae180c59edd182ad93bf12ee289484fa"},
- {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4cb11d8debab1626181633d184b2372aaa09825bde709bf927704ed72765bed1"},
- {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b29728cff2c12f3d9f1d940528ee83918d803c0567866e062683f300d1d2eff3"},
- {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:a15cbf3a9c40672523bdb6ea97ff74b443406ba0ab9bca10ceccd9546414bd84"},
- {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:6e66df260fed01ed8ea790c2913271641c58481e807790d9fca8bfd5a3c13844"},
- {file = "xxhash-3.4.1-cp311-cp311-win32.whl", hash = "sha256:e867f68a8f381ea12858e6d67378c05359d3a53a888913b5f7d35fbf68939d5f"},
- {file = "xxhash-3.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:200a5a3ad9c7c0c02ed1484a1d838b63edcf92ff538770ea07456a3732c577f4"},
- {file = "xxhash-3.4.1-cp311-cp311-win_arm64.whl", hash = "sha256:1d03f1c0d16d24ea032e99f61c552cb2b77d502e545187338bea461fde253583"},
- {file = "xxhash-3.4.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c4bbba9b182697a52bc0c9f8ec0ba1acb914b4937cd4a877ad78a3b3eeabefb3"},
- {file = "xxhash-3.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9fd28a9da300e64e434cfc96567a8387d9a96e824a9be1452a1e7248b7763b78"},
- {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6066d88c9329ab230e18998daec53d819daeee99d003955c8db6fc4971b45ca3"},
- {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:93805bc3233ad89abf51772f2ed3355097a5dc74e6080de19706fc447da99cd3"},
- {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:64da57d5ed586ebb2ecdde1e997fa37c27fe32fe61a656b77fabbc58e6fbff6e"},
- {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a97322e9a7440bf3c9805cbaac090358b43f650516486746f7fa482672593df"},
- {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbe750d512982ee7d831838a5dee9e9848f3fb440e4734cca3f298228cc957a6"},
- {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:fd79d4087727daf4d5b8afe594b37d611ab95dc8e29fe1a7517320794837eb7d"},
- {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:743612da4071ff9aa4d055f3f111ae5247342931dedb955268954ef7201a71ff"},
- {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:b41edaf05734092f24f48c0958b3c6cbaaa5b7e024880692078c6b1f8247e2fc"},
- {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:a90356ead70d715fe64c30cd0969072de1860e56b78adf7c69d954b43e29d9fa"},
- {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ac56eebb364e44c85e1d9e9cc5f6031d78a34f0092fea7fc80478139369a8b4a"},
- {file = "xxhash-3.4.1-cp312-cp312-win32.whl", hash = "sha256:911035345932a153c427107397c1518f8ce456f93c618dd1c5b54ebb22e73747"},
- {file = "xxhash-3.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:f31ce76489f8601cc7b8713201ce94b4bd7b7ce90ba3353dccce7e9e1fee71fa"},
- {file = "xxhash-3.4.1-cp312-cp312-win_arm64.whl", hash = "sha256:b5beb1c6a72fdc7584102f42c4d9df232ee018ddf806e8c90906547dfb43b2da"},
- {file = "xxhash-3.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6d42b24d1496deb05dee5a24ed510b16de1d6c866c626c2beb11aebf3be278b9"},
- {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b685fab18876b14a8f94813fa2ca80cfb5ab6a85d31d5539b7cd749ce9e3624"},
- {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:419ffe34c17ae2df019a4685e8d3934d46b2e0bbe46221ab40b7e04ed9f11137"},
- {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0e041ce5714f95251a88670c114b748bca3bf80cc72400e9f23e6d0d59cf2681"},
- {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc860d887c5cb2f524899fb8338e1bb3d5789f75fac179101920d9afddef284b"},
- {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:312eba88ffe0a05e332e3a6f9788b73883752be63f8588a6dc1261a3eaaaf2b2"},
- {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:e01226b6b6a1ffe4e6bd6d08cfcb3ca708b16f02eb06dd44f3c6e53285f03e4f"},
- {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:9f3025a0d5d8cf406a9313cd0d5789c77433ba2004b1c75439b67678e5136537"},
- {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:6d3472fd4afef2a567d5f14411d94060099901cd8ce9788b22b8c6f13c606a93"},
- {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:43984c0a92f06cac434ad181f329a1445017c33807b7ae4f033878d860a4b0f2"},
- {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:a55e0506fdb09640a82ec4f44171273eeabf6f371a4ec605633adb2837b5d9d5"},
- {file = "xxhash-3.4.1-cp37-cp37m-win32.whl", hash = "sha256:faec30437919555b039a8bdbaba49c013043e8f76c999670aef146d33e05b3a0"},
- {file = "xxhash-3.4.1-cp37-cp37m-win_amd64.whl", hash = "sha256:c9e1b646af61f1fc7083bb7b40536be944f1ac67ef5e360bca2d73430186971a"},
- {file = "xxhash-3.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:961d948b7b1c1b6c08484bbce3d489cdf153e4122c3dfb07c2039621243d8795"},
- {file = "xxhash-3.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:719a378930504ab159f7b8e20fa2aa1896cde050011af838af7e7e3518dd82de"},
- {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74fb5cb9406ccd7c4dd917f16630d2e5e8cbbb02fc2fca4e559b2a47a64f4940"},
- {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5dab508ac39e0ab988039bc7f962c6ad021acd81fd29145962b068df4148c476"},
- {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8c59f3e46e7daf4c589e8e853d700ef6607afa037bfad32c390175da28127e8c"},
- {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8cc07256eff0795e0f642df74ad096f8c5d23fe66bc138b83970b50fc7f7f6c5"},
- {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e9f749999ed80f3955a4af0eb18bb43993f04939350b07b8dd2f44edc98ffee9"},
- {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:7688d7c02149a90a3d46d55b341ab7ad1b4a3f767be2357e211b4e893efbaaf6"},
- {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a8b4977963926f60b0d4f830941c864bed16aa151206c01ad5c531636da5708e"},
- {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:8106d88da330f6535a58a8195aa463ef5281a9aa23b04af1848ff715c4398fb4"},
- {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:4c76a77dbd169450b61c06fd2d5d436189fc8ab7c1571d39265d4822da16df22"},
- {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:11f11357c86d83e53719c592021fd524efa9cf024dc7cb1dfb57bbbd0d8713f2"},
- {file = "xxhash-3.4.1-cp38-cp38-win32.whl", hash = "sha256:0c786a6cd74e8765c6809892a0d45886e7c3dc54de4985b4a5eb8b630f3b8e3b"},
- {file = "xxhash-3.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:aabf37fb8fa27430d50507deeab2ee7b1bcce89910dd10657c38e71fee835594"},
- {file = "xxhash-3.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6127813abc1477f3a83529b6bbcfeddc23162cece76fa69aee8f6a8a97720562"},
- {file = "xxhash-3.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ef2e194262f5db16075caea7b3f7f49392242c688412f386d3c7b07c7733a70a"},
- {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71be94265b6c6590f0018bbf73759d21a41c6bda20409782d8117e76cd0dfa8b"},
- {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:10e0a619cdd1c0980e25eb04e30fe96cf8f4324758fa497080af9c21a6de573f"},
- {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fa122124d2e3bd36581dd78c0efa5f429f5220313479fb1072858188bc2d5ff1"},
- {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e17032f5a4fea0a074717fe33477cb5ee723a5f428de7563e75af64bfc1b1e10"},
- {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca7783b20e3e4f3f52f093538895863f21d18598f9a48211ad757680c3bd006f"},
- {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d77d09a1113899fad5f354a1eb4f0a9afcf58cefff51082c8ad643ff890e30cf"},
- {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:21287bcdd299fdc3328cc0fbbdeaa46838a1c05391264e51ddb38a3f5b09611f"},
- {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:dfd7a6cc483e20b4ad90224aeb589e64ec0f31e5610ab9957ff4314270b2bf31"},
- {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:543c7fcbc02bbb4840ea9915134e14dc3dc15cbd5a30873a7a5bf66039db97ec"},
- {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:fe0a98d990e433013f41827b62be9ab43e3cf18e08b1483fcc343bda0d691182"},
- {file = "xxhash-3.4.1-cp39-cp39-win32.whl", hash = "sha256:b9097af00ebf429cc7c0e7d2fdf28384e4e2e91008130ccda8d5ae653db71e54"},
- {file = "xxhash-3.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:d699b921af0dcde50ab18be76c0d832f803034d80470703700cb7df0fbec2832"},
- {file = "xxhash-3.4.1-cp39-cp39-win_arm64.whl", hash = "sha256:2be491723405e15cc099ade1280133ccfbf6322d2ef568494fb7d07d280e7eee"},
- {file = "xxhash-3.4.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:431625fad7ab5649368c4849d2b49a83dc711b1f20e1f7f04955aab86cd307bc"},
- {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc6dbd5fc3c9886a9e041848508b7fb65fd82f94cc793253990f81617b61fe49"},
- {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3ff8dbd0ec97aec842476cb8ccc3e17dd288cd6ce3c8ef38bff83d6eb927817"},
- {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef73a53fe90558a4096e3256752268a8bdc0322f4692ed928b6cd7ce06ad4fe3"},
- {file = "xxhash-3.4.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:450401f42bbd274b519d3d8dcf3c57166913381a3d2664d6609004685039f9d3"},
- {file = "xxhash-3.4.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a162840cf4de8a7cd8720ff3b4417fbc10001eefdd2d21541a8226bb5556e3bb"},
- {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b736a2a2728ba45017cb67785e03125a79d246462dfa892d023b827007412c52"},
- {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d0ae4c2e7698adef58710d6e7a32ff518b66b98854b1c68e70eee504ad061d8"},
- {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6322c4291c3ff174dcd104fae41500e75dad12be6f3085d119c2c8a80956c51"},
- {file = "xxhash-3.4.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:dd59ed668801c3fae282f8f4edadf6dc7784db6d18139b584b6d9677ddde1b6b"},
- {file = "xxhash-3.4.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:92693c487e39523a80474b0394645b393f0ae781d8db3474ccdcead0559ccf45"},
- {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4603a0f642a1e8d7f3ba5c4c25509aca6a9c1cc16f85091004a7028607ead663"},
- {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fa45e8cbfbadb40a920fe9ca40c34b393e0b067082d94006f7f64e70c7490a6"},
- {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:595b252943b3552de491ff51e5bb79660f84f033977f88f6ca1605846637b7c6"},
- {file = "xxhash-3.4.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:562d8b8f783c6af969806aaacf95b6c7b776929ae26c0cd941d54644ea7ef51e"},
- {file = "xxhash-3.4.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:41ddeae47cf2828335d8d991f2d2b03b0bdc89289dc64349d712ff8ce59d0647"},
- {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c44d584afdf3c4dbb3277e32321d1a7b01d6071c1992524b6543025fb8f4206f"},
- {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd7bddb3a5b86213cc3f2c61500c16945a1b80ecd572f3078ddbbe68f9dabdfb"},
- {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ecb6c987b62437c2f99c01e97caf8d25660bf541fe79a481d05732e5236719c"},
- {file = "xxhash-3.4.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:696b4e18b7023527d5c50ed0626ac0520edac45a50ec7cf3fc265cd08b1f4c03"},
- {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
+ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+ {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
]
-[[package]]
-name = "yarl"
-version = "1.9.2"
-description = "Yet another URL library"
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "yarl-1.9.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8c2ad583743d16ddbdf6bb14b5cd76bf43b0d0006e918809d5d4ddf7bde8dd82"},
- {file = "yarl-1.9.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:82aa6264b36c50acfb2424ad5ca537a2060ab6de158a5bd2a72a032cc75b9eb8"},
- {file = "yarl-1.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c0c77533b5ed4bcc38e943178ccae29b9bcf48ffd1063f5821192f23a1bd27b9"},
- {file = "yarl-1.9.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ee4afac41415d52d53a9833ebae7e32b344be72835bbb589018c9e938045a560"},
- {file = "yarl-1.9.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9bf345c3a4f5ba7f766430f97f9cc1320786f19584acc7086491f45524a551ac"},
- {file = "yarl-1.9.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a96c19c52ff442a808c105901d0bdfd2e28575b3d5f82e2f5fd67e20dc5f4ea"},
- {file = "yarl-1.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:891c0e3ec5ec881541f6c5113d8df0315ce5440e244a716b95f2525b7b9f3608"},
- {file = "yarl-1.9.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c3a53ba34a636a256d767c086ceb111358876e1fb6b50dfc4d3f4951d40133d5"},
- {file = "yarl-1.9.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:566185e8ebc0898b11f8026447eacd02e46226716229cea8db37496c8cdd26e0"},
- {file = "yarl-1.9.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:2b0738fb871812722a0ac2154be1f049c6223b9f6f22eec352996b69775b36d4"},
- {file = "yarl-1.9.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:32f1d071b3f362c80f1a7d322bfd7b2d11e33d2adf395cc1dd4df36c9c243095"},
- {file = "yarl-1.9.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:e9fdc7ac0d42bc3ea78818557fab03af6181e076a2944f43c38684b4b6bed8e3"},
- {file = "yarl-1.9.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:56ff08ab5df8429901ebdc5d15941b59f6253393cb5da07b4170beefcf1b2528"},
- {file = "yarl-1.9.2-cp310-cp310-win32.whl", hash = "sha256:8ea48e0a2f931064469bdabca50c2f578b565fc446f302a79ba6cc0ee7f384d3"},
- {file = "yarl-1.9.2-cp310-cp310-win_amd64.whl", hash = "sha256:50f33040f3836e912ed16d212f6cc1efb3231a8a60526a407aeb66c1c1956dde"},
- {file = "yarl-1.9.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:646d663eb2232d7909e6601f1a9107e66f9791f290a1b3dc7057818fe44fc2b6"},
- {file = "yarl-1.9.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:aff634b15beff8902d1f918012fc2a42e0dbae6f469fce134c8a0dc51ca423bb"},
- {file = "yarl-1.9.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a83503934c6273806aed765035716216cc9ab4e0364f7f066227e1aaea90b8d0"},
- {file = "yarl-1.9.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b25322201585c69abc7b0e89e72790469f7dad90d26754717f3310bfe30331c2"},
- {file = "yarl-1.9.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:22a94666751778629f1ec4280b08eb11815783c63f52092a5953faf73be24191"},
- {file = "yarl-1.9.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8ec53a0ea2a80c5cd1ab397925f94bff59222aa3cf9c6da938ce05c9ec20428d"},
- {file = "yarl-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:159d81f22d7a43e6eabc36d7194cb53f2f15f498dbbfa8edc8a3239350f59fe7"},
- {file = "yarl-1.9.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:832b7e711027c114d79dffb92576acd1bd2decc467dec60e1cac96912602d0e6"},
- {file = "yarl-1.9.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:95d2ecefbcf4e744ea952d073c6922e72ee650ffc79028eb1e320e732898d7e8"},
- {file = "yarl-1.9.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:d4e2c6d555e77b37288eaf45b8f60f0737c9efa3452c6c44626a5455aeb250b9"},
- {file = "yarl-1.9.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:783185c75c12a017cc345015ea359cc801c3b29a2966c2655cd12b233bf5a2be"},
- {file = "yarl-1.9.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:b8cc1863402472f16c600e3e93d542b7e7542a540f95c30afd472e8e549fc3f7"},
- {file = "yarl-1.9.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:822b30a0f22e588b32d3120f6d41e4ed021806418b4c9f0bc3048b8c8cb3f92a"},
- {file = "yarl-1.9.2-cp311-cp311-win32.whl", hash = "sha256:a60347f234c2212a9f0361955007fcf4033a75bf600a33c88a0a8e91af77c0e8"},
- {file = "yarl-1.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:be6b3fdec5c62f2a67cb3f8c6dbf56bbf3f61c0f046f84645cd1ca73532ea051"},
- {file = "yarl-1.9.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:38a3928ae37558bc1b559f67410df446d1fbfa87318b124bf5032c31e3447b74"},
- {file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac9bb4c5ce3975aeac288cfcb5061ce60e0d14d92209e780c93954076c7c4367"},
- {file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3da8a678ca8b96c8606bbb8bfacd99a12ad5dd288bc6f7979baddd62f71c63ef"},
- {file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:13414591ff516e04fcdee8dc051c13fd3db13b673c7a4cb1350e6b2ad9639ad3"},
- {file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf74d08542c3a9ea97bb8f343d4fcbd4d8f91bba5ec9d5d7f792dbe727f88938"},
- {file = "yarl-1.9.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6e7221580dc1db478464cfeef9b03b95c5852cc22894e418562997df0d074ccc"},
- {file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:494053246b119b041960ddcd20fd76224149cfea8ed8777b687358727911dd33"},
- {file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:52a25809fcbecfc63ac9ba0c0fb586f90837f5425edfd1ec9f3372b119585e45"},
- {file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:e65610c5792870d45d7b68c677681376fcf9cc1c289f23e8e8b39c1485384185"},
- {file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:1b1bba902cba32cdec51fca038fd53f8beee88b77efc373968d1ed021024cc04"},
- {file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:662e6016409828ee910f5d9602a2729a8a57d74b163c89a837de3fea050c7582"},
- {file = "yarl-1.9.2-cp37-cp37m-win32.whl", hash = "sha256:f364d3480bffd3aa566e886587eaca7c8c04d74f6e8933f3f2c996b7f09bee1b"},
- {file = "yarl-1.9.2-cp37-cp37m-win_amd64.whl", hash = "sha256:6a5883464143ab3ae9ba68daae8e7c5c95b969462bbe42e2464d60e7e2698368"},
- {file = "yarl-1.9.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5610f80cf43b6202e2c33ba3ec2ee0a2884f8f423c8f4f62906731d876ef4fac"},
- {file = "yarl-1.9.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b9a4e67ad7b646cd6f0938c7ebfd60e481b7410f574c560e455e938d2da8e0f4"},
- {file = "yarl-1.9.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:83fcc480d7549ccebe9415d96d9263e2d4226798c37ebd18c930fce43dfb9574"},
- {file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5fcd436ea16fee7d4207c045b1e340020e58a2597301cfbcfdbe5abd2356c2fb"},
- {file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84e0b1599334b1e1478db01b756e55937d4614f8654311eb26012091be109d59"},
- {file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3458a24e4ea3fd8930e934c129b676c27452e4ebda80fbe47b56d8c6c7a63a9e"},
- {file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:838162460b3a08987546e881a2bfa573960bb559dfa739e7800ceeec92e64417"},
- {file = "yarl-1.9.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f4e2d08f07a3d7d3e12549052eb5ad3eab1c349c53ac51c209a0e5991bbada78"},
- {file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:de119f56f3c5f0e2fb4dee508531a32b069a5f2c6e827b272d1e0ff5ac040333"},
- {file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:149ddea5abf329752ea5051b61bd6c1d979e13fbf122d3a1f9f0c8be6cb6f63c"},
- {file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:674ca19cbee4a82c9f54e0d1eee28116e63bc6fd1e96c43031d11cbab8b2afd5"},
- {file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:9b3152f2f5677b997ae6c804b73da05a39daa6a9e85a512e0e6823d81cdad7cc"},
- {file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5415d5a4b080dc9612b1b63cba008db84e908b95848369aa1da3686ae27b6d2b"},
- {file = "yarl-1.9.2-cp38-cp38-win32.whl", hash = "sha256:f7a3d8146575e08c29ed1cd287068e6d02f1c7bdff8970db96683b9591b86ee7"},
- {file = "yarl-1.9.2-cp38-cp38-win_amd64.whl", hash = "sha256:63c48f6cef34e6319a74c727376e95626f84ea091f92c0250a98e53e62c77c72"},
- {file = "yarl-1.9.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:75df5ef94c3fdc393c6b19d80e6ef1ecc9ae2f4263c09cacb178d871c02a5ba9"},
- {file = "yarl-1.9.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c027a6e96ef77d401d8d5a5c8d6bc478e8042f1e448272e8d9752cb0aff8b5c8"},
- {file = "yarl-1.9.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f3b078dbe227f79be488ffcfc7a9edb3409d018e0952cf13f15fd6512847f3f7"},
- {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:59723a029760079b7d991a401386390c4be5bfec1e7dd83e25a6a0881859e716"},
- {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b03917871bf859a81ccb180c9a2e6c1e04d2f6a51d953e6a5cdd70c93d4e5a2a"},
- {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3"},
- {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955"},
- {file = "yarl-1.9.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8c56986609b057b4839968ba901944af91b8e92f1725d1a2d77cbac6972b9ed1"},
- {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2c315df3293cd521033533d242d15eab26583360b58f7ee5d9565f15fee1bef4"},
- {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6"},
- {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf"},
- {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:066c163aec9d3d073dc9ffe5dd3ad05069bcb03fcaab8d221290ba99f9f69ee3"},
- {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8288d7cd28f8119b07dd49b7230d6b4562f9b61ee9a4ab02221060d21136be80"},
- {file = "yarl-1.9.2-cp39-cp39-win32.whl", hash = "sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623"},
- {file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
- {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[metadata]
lock-version = "2.0"
-python-versions = ">=3.11,<3.13"
-content-hash = "ed04bd951ee7f9773ceea5655c43d3ea8f0e43445ad6e328be6773ffbb68af84"
+python-versions = ">=3.9,<3.13"
+content-hash = "d265b7789c918f4c2dc7d3db9ea80871958320fedc47576d0e0f78f327f42f6e"
diff --git a/pyproject.toml b/pyproject.toml
index 0268fd0..f43a0b0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,30 +1,35 @@
[tool.poetry]
name = "src"
version = "0.1.0"
-description = "Trying to use a AdaVAE (an LLM VAE) as a world model in an RL agent in a text game"
+description = ""
authors = ["wassname "]
-license = "MIT"
readme = "README.md"
[tool.poetry.dependencies]
-python = ">=3.11,<3.13"
+python = ">=3.9,<3.13"
torch = {version = "^2.1.0+cu118", source = "pytorch"}
-simple-parsing = "^0.1.4"
-tqdm = "^4.66.1"
-numpy = "^1.26.1"
-pandas = "^2.1.1"
-lightning = "^2.1.0"
matplotlib = "^3.8.0"
loguru = "^0.7.2"
-einops = "^0.7.0"
-scikit-learn = "^1.3.1"
-pytorch-optimizer = "^2.12.0"
+einops = "^0.3.1"
torchinfo = "^1.8.0"
accelerate = "^0.24.1"
-datasets = "^2.14.6"
peft = "^0.5.0"
bitsandbytes = {url = "https://github.com/TimDettmers/bitsandbytes/releases/download/0.41.0/bitsandbytes-0.41.0-py3-none-any.whl"}
transformers = "4.34.0"
+tqdm = "^4.66.1"
+wandb = "^0.12.6"
+ale-py = "^0.7.4"
+pygame = "^2.5.2"
+psutil = "^5.9.6"
+protobuf = "^3.10.0"
+opencv-python = "^4.8.1.78"
+hydra-core = "^1.3.2"
+torchvision = "^0.16.0"
+numpy = ">=1.18.0"
+gym = {version = "0.22.0", extras = ["accept-rom-license", "atari"]}
+scipy = "^1.11.3"
+crafter = "^1.8.2"
+minihack = "^0.1.5"
[[tool.poetry.source]]
name = "pytorch"
diff --git a/research_journal.md b/research_journal.md
new file mode 100644
index 0000000..b3478be
--- /dev/null
+++ b/research_journal.md
@@ -0,0 +1,407 @@
+# 2023-11-12 13:17:35
+
+Try IRIs but with pretrained transformer with LoRA adapter
+
+- [x] first can I run it yes with a 1/2 batch size
+- [ ] then can I add 3B with adapter...
+
+```sh
+poetry install
+. ./.venv/bin/activate
+python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0 wandb.mode=offline
+
+# or for quick debug
+WANDB_MODE=disabled python -m pdb src/main.py env.train.id=BreakoutNoFrameskip-v4
+```
+
+
+```sh
+# TODO use this code to load a transformer, and other code from my bigvae repo https://github.com/wassname/bigvae_wm
+def load_model(config, device='cuda'):
+ tokenizer = AutoTokenizer.from_pretrained(config.model_name, trust_remote_code=True)
+ tokenizer.padding_side = "left"
+ if tokenizer.pad_token is None:
+ tokenizer.pad_token = tokenizer.eos_token
+ bnb_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_compute_dtype=torch.bfloat16,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_use_double_quant=True,
+ )
+ base_model = AutoModelForCausalLM.from_pretrained(
+ config.model_name,
+ device_map={"": device},
+ quantization_config=bnb_config,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True
+ )
+ peft_config = peft.LoraConfig(
+ peft.TaskType.CAUSAL_LM,
+ inference_mode=False,
+ r=config.rank,
+ lora_alpha=8,
+ lora_dropout=config.dropout,
+ target_modules=[
+ "self_attn.q_proj",
+ "self_attn.k_proj",
+ "self_attn.v_proj",
+ "self_attn.o_proj",
+ "mlp.gate_proj",
+ "mlp.up_proj",
+ "mlp.down_proj",
+ ],
+ )
+ base_model_peft = peft.get_peft_model(base_model, peft_config)
+ vae_model = BigVAE(
+ base_model_peft, device, peft_config, z_dim=config.z_dim,
+ )
+ if config.start_from:
+ vae_model.load_pretrained(config.start_from)
+ base_model_peft.requires_grad_(False)
+ vae_model.vae_head.requires_grad_(False)
+ vae_model.vae_head.w_d.requires_grad_()
+ router = BigVAERouter(base_model_peft, vae_model, device, peft_config)
+ if config.start_from:
+ router.load_pretrained(config.start_from, is_trainable=True)
+ print(router.model.print_trainable_parameters())
+ router.model.set_adapter("router")
+```
+
+Debugging:
+ batch['observations'].shape
+ torch.Size([16, 20, 3, 64, 64])
+
+ obs_tokens.shape
+ torch.Size([16, 20, 16])
+
+ https://vscode.dev/github/wassname/iris_bigvae/blob/just_llms2/src/models/world_model.py#L105
+ tokens
+ tensor([[222, 222, 222, ..., 409, 55, 2],
+ [222, 222, 222, ..., 409, 139, 1],
+ [222, 222, 222, ..., 168, 190, 3],
+ ...,
+ [222, 222, 222, ..., 168, 55, 0],
+ [222, 222, 222, ..., 237, 190, 3],
+ [222, 222, 222, ..., 168, 55, 0]], device='cuda:0')
+ tokens.shape
+ torch.Size([16, 340])
+ where 16 is the batch size. 340 is the step size?. actions was 16,20 int
+
+ tokens.shape int
+ torch.Size([16, 340])
+
+ sequences.shape float32
+ torch.Size([16, 340, 256])
+
+ transfrmer
+ x.shape
+ torch.Size([16, 340, 256])
+
+# 2023-11-12 16:58:37
+
+So I got it training, but during imagination it passes in a single token with no past steps. But the slicer seems to need at least on block? And so I get none?
+
+hmm it's because num_kept_tokens is 16 not 1. So there should be a whole block passed in ?
+
+wait apparently it's also a problem in the normal repo.... I confuse! maybe it's my config! maybe I need >larger than block size. nope
+
+hmm it still happens in the original repo with my debug params. maybe it's my debug params
+
+
+... trying a full run without my debug params...
+
+note trains.world_model.batch_num_samples:4 fill 20GB gpu ram for the 3b stability ai llm
+
+ok even with a full run I get the error. I think it's a bug in the original repo. I'll try to debug it there.
+
+ Epoch 51 / 600
+
+ Experience collection (train_dataset): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:03<00:00, 59.91it/s]
+ Training tokenizer: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:17<00:00, 11.53it/s]
+ Training world_model: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [02:11<00:00, 1.53it/s]
+ Training actor_critic: 0%| | 0/200 [00:00 (B, K)
+ ```
+- Embed $Emb(t_0) = z_0$
+ ```py
+ embedded_tokens = self.tokenizer.embedding(self.obs_tokens) # (B, K, E)
+ z = rearrange(embedded_tokens, 'b (h w) e -> b e h w', h=int(np.sqrt(self.num_observations_tokens)))
+ ```
+- Dynamics $D(z_0, a_0) = z_1$
+ ```py
+ outputs_wm = self.world_model(tokenRedmond AI, past_keys_values=self.keys_values_wm)
+ ```
+- Decoder $D(z_0, a_0) = x_1$
+ ```py
+ rec = self.tokenizer.decode(z, should_postprocess=True) # (B, C, H, W)
+ ```
+
+
+but we have tokens vs z
+
+Questions:
+- wait why are we just passing in "action_token" to the transformer and not obs? that must have obs in it right... right??? confirm
+- in iris-delta how did they pass everything in? I guess obs_prev was tokenized too? I think the slices are annoying so maybe I should just pass things seperatly
+
+# 2023-11-24 10:56:40
+
+If I unfreeze the whole transformer, it seem to learn the most obvious dynamics (the next latent space is the same as the last).
+
+To summarize
+- with Qlora it didn't learn that
+- with unfrozen head it didn't
+- when training transformer and obs embedding together it did not (frozen llm embeddings)
+
+
+no it didn't work with tokenizer sep hmm
+
+Oh it did with whole transfrmer and tokenizer at same time https://wandb.ai/wassname/iris/runs/w7lvs4gi?workspace=user-wassname
+wandb: world_model/eval/loss_obs ▇█▃▄▄▄▄▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb: world_model/eval/loss_rewards ▁█▃█▇▅▇▁▅▆▂▄▃▇▆▃▄▇▃▄▂▄▃▂▃▄▃
+wandb: world_model/eval/total_loss ▂█▃██▅▇▁▅▆▁▃▂█▇▃▃▆▃▄▂▃▃▂▂▄▂
diff --git a/scripts/play.sh b/scripts/play.sh
index eff9abe..45fed21 100755
--- a/scripts/play.sh
+++ b/scripts/play.sh
@@ -14,22 +14,23 @@ while [ "$1" != "" ]; do
;;
-h | --header )
header=1
- ;;
+ ;; # adds banner with env metadata like action
-r | --reconstruction )
reconstruction=1
- ;;
+ ;; # 3 panes [original_obs, resized_obs, reconstructed], doesn't do anything if any of -w -a or -e are set. shows quality of encoder decoder
-s | --save-mode )
save_mode=1
- ;;
+ ;; # lets you save the episode to mp4
-a | --agent-world-model )
mode="agent_in_world_model"
- ;;
+ ;; # the agent plays in the world model env, shows the quality of the dynamics model
-e | --episode )
mode="episode_replay"
- ;;
+ ;; # replay train, test, or imagined episodes. shows quality of dynamics model
+ # this is quick low resource way to check the dynamics model and agent while training
-w | --world-model )
mode="play_in_world_model"
- ;;
+ ;; # human plays in world model
* )
echo Invalid usage : $1
exit 1
diff --git a/src/agent.py b/src/agent.py
index f5dd1d3..ee884fd 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -4,10 +4,10 @@ import torch
from torch.distributions.categorical import Categorical
import torch.nn as nn
-from models.actor_critic import ActorCritic
-from models.tokenizer import Tokenizer
-from models.world_model import WorldModel
-from utils import extract_state_dict
+from src.models.actor_critic import ActorCritic
+from src.models.tokenizer import Tokenizer
+from src.models.world_model import WorldModel
+from src.utils import extract_state_dict
class Agent(nn.Module):
@@ -34,4 +34,5 @@ class Agent(nn.Module):
input_ac = obs if self.actor_critic.use_original_obs else torch.clamp(self.tokenizer.encode_decode(obs, should_preprocess=True, should_postprocess=True), 0, 1)
logits_actions = self.actor_critic(input_ac).logits_actions[:, -1] / temperature
act_token = Categorical(logits=logits_actions).sample() if should_sample else logits_actions.argmax(dim=-1)
+ # FIXME, is this really just an action and doesn't have an obs in?
return act_token
diff --git a/src/collector.py b/src/collector.py
index 85e0150..8c5947e 100644
--- a/src/collector.py
+++ b/src/collector.py
@@ -8,11 +8,11 @@ import torch
from tqdm import tqdm
import wandb
-from agent import Agent
-from dataset import EpisodesDataset
-from envs import SingleProcessEnv, MultiProcessEnv
-from episode import Episode
-from utils import EpisodeDirManager, RandomHeuristic
+from src.agent import Agent
+from src.dataset import EpisodesDataset
+from src.envs import SingleProcessEnv, MultiProcessEnv
+from src.episode import Episode
+from src.utils import EpisodeDirManager, RandomHeuristic
class Collector:
diff --git a/src/dataset.py b/src/dataset.py
index 59d9f30..5cc2d6e 100644
--- a/src/dataset.py
+++ b/src/dataset.py
@@ -7,7 +7,7 @@ from typing import Dict, List, Optional, Tuple
import psutil
import torch
-from episode import Episode
+from src.episode import Episode
Batch = Dict[str, torch.Tensor]
diff --git a/src/envs/__init__.py b/src/envs/__init__.py
index 5afe68b..4d85674 100644
--- a/src/envs/__init__.py
+++ b/src/envs/__init__.py
@@ -1,4 +1,4 @@
from .multi_process_env import MultiProcessEnv
-from .wrappers import make_atari, ResizeObsWrapper
+from .wrappers import make_atari, make_crafter, make_env, ResizeObsWrapper
from .single_process_env import SingleProcessEnv
from .world_model_env import WorldModelEnv
diff --git a/src/envs/world_model_env.py b/src/envs/world_model_env.py
index a159fb2..4e447d4 100644
--- a/src/envs/world_model_env.py
+++ b/src/envs/world_model_env.py
@@ -65,12 +65,13 @@ class WorldModelEnv:
token = action.clone().detach() if isinstance(action, torch.Tensor) else torch.tensor(action, dtype=torch.long)
token = token.reshape(-1, 1).to(self.device) # (B, 1)
+
for k in range(num_passes): # assumption that there is only one action token.
+ # FIXME: hold on we are ONLY passing in the action token! should it not be obs too
outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
output_sequence.append(outputs_wm.output_sequence)
-
if k == 0:
reward = Categorical(logits=outputs_wm.logits_rewards).sample().float().cpu().numpy().reshape(-1) - 1 # (B,)
done = Categorical(logits=outputs_wm.logits_ends).sample().cpu().numpy().astype(bool).reshape(-1) # (B,)
diff --git a/src/envs/wrappers.py b/src/envs/wrappers.py
index b1054a1..fa586ac 100644
--- a/src/envs/wrappers.py
+++ b/src/envs/wrappers.py
@@ -7,11 +7,22 @@ from typing import Tuple
import gym
import numpy as np
from PIL import Image
+import crafter
+
+
+def make_env(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
+ if id.startswith('Crafter'):
+ return make_crafter(id, size=size, max_episode_steps=max_episode_steps, done_on_life_loss=done_on_life_loss)
+ if id.startswith('MiniHack'):
+ return make_minihack(size=size, max_episode_steps=max_episode_steps, done_on_life_loss=done_on_life_loss)
+ else:
+ return make_atari(id, size, max_episode_steps, noop_max, frame_skip, done_on_life_loss, clip_reward)
def make_atari(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
env = gym.make(id)
- assert 'NoFrameskip' in env.spec.id or 'Frameskip' not in env.spec
+ print(env.spec)
+ assert 'NoFrameskip' in env.spec.id or 'Frameskip' not in str(env.spec)
env = ResizeObsWrapper(env, (size, size))
if clip_reward:
env = RewardClippingWrapper(env)
@@ -24,6 +35,26 @@ def make_atari(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, d
env = EpisodicLifeEnv(env)
return env
+def make_crafter(id, size=64, max_episode_steps=None, done_on_life_loss=False):
+ # https://github.com/danijar/dreamerv2/blob/07d906e9c4322c6fc2cd6ed23e247ccd6b7c8c41/dreamerv2/common/envs.py#L242
+ # https://github.com/footoredo/torchbeast/blob/12939569cc46b6a8616e4c25b138d97248cc8581/torchbeast/atari_wrappers.py#L301
+ env = gym.make(id)
+ env = ResizeObsWrapper(env, (size, size))
+ return env
+
+
+def make_minihack(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
+ # https://github.com/facebookresearch/minihack/blob/47065748f04714c49ba5b52fb74d166228c7acc1/minihack/agent/common/envs/wrapper.py#L117
+ # https://github.com/roger-creus/SOFE/blob/5551a115a9c7e1d632cf6996bf5dcabde59cdcc5/e3b/minihack/torchbeast/src/utils.py#L110
+ env = gym.make(id,
+ # https://minihack.readthedocs.io/en/latest/getting-started/observation_spaces.html
+ observation_keys=("pixel_crop"),
+ # obs_crop_h=9,
+ # obs_crop_w=9,
+ )
+ env = ResizeObsWrapper(env, (size, size))
+ return env
+
class ResizeObsWrapper(gym.ObservationWrapper):
def __init__(self, env: gym.Env, size: Tuple[int, int]) -> None:
@@ -64,7 +95,7 @@ class NoopResetEnv(gym.Wrapper):
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
- noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
+ noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
assert noops > 0
obs = None
for _ in range(noops):
diff --git a/src/game/agent_env.py b/src/game/agent_env.py
index 77831ad..19ea16b 100644
--- a/src/game/agent_env.py
+++ b/src/game/agent_env.py
@@ -4,14 +4,14 @@ from PIL import Image
import torch
from torchvision.transforms.functional import InterpolationMode, resize
-from agent import Agent
-from envs import SingleProcessEnv, WorldModelEnv
-from game.keymap import get_keymap_and_action_names
+from src.agent import Agent
+from src.envs import SingleProcessEnv, WorldModelEnv
+from src.game.keymap import get_keymap_and_action_names
class AgentEnv:
def __init__(self, agent: Agent, env: SingleProcessEnv, keymap_name: str, do_reconstruction: bool) -> None:
- assert isinstance(env, SingleProcessEnv) or isinstance(env, WorldModelEnv)
+ assert isinstance(env, SingleProcessEnv) or isinstance(env, WorldModelEnv), f"{env}"
self.agent = agent
self.env = env
_, self.action_names = get_keymap_and_action_names(keymap_name)
diff --git a/src/game/keymap.py b/src/game/keymap.py
index 62aadc7..e912868 100644
--- a/src/game/keymap.py
+++ b/src/game/keymap.py
@@ -12,6 +12,10 @@ def get_keymap_and_action_names(name):
if name == 'atari':
return ATARI_KEYMAP, ATARI_ACTION_NAMES
+
+ if name == 'atari/CrafterReward-v1':
+ env_id = name.split('atari/')[1]
+ return CRAFTER_KEYMAP, gym.make(env_id).action_names
assert name.startswith('atari/')
env_id = name.split('atari/')[1]
@@ -100,4 +104,25 @@ EMPTY_ACTION_NAMES = [
]
EMPTY_KEYMAP = {
-}
\ No newline at end of file
+}
+
+CRAFTER_KEYMAP = {
+ pygame.K_a: 1,
+ pygame.K_d: 2,
+ pygame.K_w: 3,
+ pygame.K_s: 4,
+ pygame.K_SPACE: 5,
+ pygame.K_TAB: 6,
+
+ pygame.K_r: 7,
+ pygame.K_t: 8,
+ pygame.K_f: 9,
+ pygame.K_p: 10,
+
+ pygame.K_1: 11,
+ pygame.K_2: 12,
+ pygame.K_3: 13,
+ pygame.K_4: 14,
+ pygame.K_5: 15,
+ pygame.K_6: 16,
+}
diff --git a/src/main.py b/src/main.py
index be4177b..6a84b94 100644
--- a/src/main.py
+++ b/src/main.py
@@ -2,7 +2,9 @@ import hydra
from omegaconf import DictConfig
from trainer import Trainer
-
+from loguru import logger
+import sys
+logger.add(sys.stderr, format="{time} {level} {message}", filter="my_module", level="INFO")
@hydra.main(config_path="../config", config_name="trainer")
def main(cfg: DictConfig):
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index c22e641..4d68c78 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -10,11 +10,11 @@ import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
-from dataset import Batch
-from envs.world_model_env import WorldModelEnv
-from models.tokenizer import Tokenizer
-from models.world_model import WorldModel
-from utils import compute_lambda_returns, LossWithIntermediateLosses
+from src.dataset import Batch
+from src.envs.world_model_env import WorldModelEnv
+from src.models.tokenizer import Tokenizer
+from src.models.world_model import WorldModel
+from src.utils import compute_lambda_returns, LossWithIntermediateLosses
@dataclass
@@ -34,24 +34,26 @@ class ImagineOutput:
class ActorCritic(nn.Module):
- def __init__(self, act_vocab_size, use_original_obs: bool = False) -> None:
+ def __init__(self, act_vocab_size, use_original_obs: bool = False, lstm_dim = 16) -> None:
super().__init__()
+ shrink = 1
+ s = 1
self.use_original_obs = use_original_obs
- self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)
+ self.conv1 = nn.Conv2d(3, 32//s, 3, stride=1, padding=1)
self.maxp1 = nn.MaxPool2d(2, 2)
- self.conv2 = nn.Conv2d(32, 32, 3, stride=1, padding=1)
+ self.conv2 = nn.Conv2d(32//s, 32//s, 3, stride=1, padding=1)
self.maxp2 = nn.MaxPool2d(2, 2)
- self.conv3 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
+ self.conv3 = nn.Conv2d(32//s, 64//s, 3, stride=1, padding=1)
self.maxp3 = nn.MaxPool2d(2, 2)
- self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
+ self.conv4 = nn.Conv2d(64//s, 64//shrink, 3, stride=1, padding=1)
self.maxp4 = nn.MaxPool2d(2, 2)
- self.lstm_dim = 512
- self.lstm = nn.LSTMCell(1024, self.lstm_dim)
+ self.lstm_dim = lstm_dim
+ self.lstm = nn.LSTMCell(1024//shrink, self.lstm_dim)
self.hx, self.cx = None, None
- self.critic_linear = nn.Linear(512, 1)
- self.actor_linear = nn.Linear(512, act_vocab_size)
+ self.critic_linear = nn.Linear(self.lstm_dim, 1)
+ self.actor_linear = nn.Linear(self.lstm_dim, act_vocab_size)
def __repr__(self) -> str:
return "actor_critic"
@@ -85,7 +87,7 @@ class ActorCritic(nn.Module):
x = F.relu(self.maxp2(self.conv2(x)))
x = F.relu(self.maxp3(self.conv3(x)))
x = F.relu(self.maxp4(self.conv4(x)))
- x = torch.flatten(x, start_dim=1)
+ x = torch.flatten(x, start_dim=1) # [b=32, 64//shrink, 4, 4]
if mask_padding is None:
self.hx, self.cx = self.lstm(x, (self.hx, self.cx))
@@ -146,6 +148,8 @@ class ActorCritic(nn.Module):
outputs_ac = self(obs)
action_token = Categorical(logits=outputs_ac.logits_actions).sample()
+
+ # FIXME shouldn't we pass in obs too?
obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))
all_actions.append(action_token)
diff --git a/src/models/slicer.py b/src/models/slicer.py
index 6566271..49563e9 100644
--- a/src/models/slicer.py
+++ b/src/models/slicer.py
@@ -31,7 +31,8 @@ class Head(Slicer):
self.head_module = head_module
def forward(self, x: torch.Tensor, num_steps: int, prev_steps: int) -> torch.Tensor:
- x_sliced = x[:, self.compute_slice(num_steps, prev_steps)] # x is (B, T, E)
+ s = self.compute_slice(num_steps, prev_steps)
+ x_sliced = x[:, s] # x is (B, T, E)
return self.head_module(x_sliced)
diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index 6528fa4..a96ec82 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -9,10 +9,10 @@ from einops import rearrange
import torch
import torch.nn as nn
-from dataset import Batch
+from src.dataset import Batch
from .lpips import LPIPS
from .nets import Encoder, Decoder
-from utils import LossWithIntermediateLosses
+from src.utils import LossWithIntermediateLosses
@dataclass
@@ -23,15 +23,15 @@ class TokenizerEncoderOutput:
class Tokenizer(nn.Module):
- def __init__(self, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
+ def __init__(self, transformer_embedding: nn.Embedding, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
super().__init__()
self.vocab_size = vocab_size
self.encoder = encoder
self.pre_quant_conv = torch.nn.Conv2d(encoder.config.z_channels, embed_dim, 1)
- self.embedding = nn.Embedding(vocab_size, embed_dim)
+ self.embedding = transformer_embedding # pretrained transformer embedding
self.post_quant_conv = torch.nn.Conv2d(embed_dim, decoder.config.z_channels, 1)
self.decoder = decoder
- self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
+ # self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
self.lpips = LPIPS().eval() if with_lpips else None
def __repr__(self) -> str:
@@ -46,6 +46,8 @@ class Tokenizer(nn.Module):
def compute_loss(self, batch: Batch, **kwargs: Any) -> LossWithIntermediateLosses:
assert self.lpips is not None
observations = self.preprocess_input(rearrange(batch['observations'], 'b t c h w -> (b t) c h w'))
+ # TODO: in the delta-IRIS paper (https://openreview.net/forum?id=o8IDoZggqO) they encode(x0, a0, x1) -> z1 and decode(x0, a0, z1). In esense the tokens only need to encode the change
+ # note they also do dynamics(x0, a0, z1) -> z2. decode(x1, a1, z2) -> x2
z, z_quantized, reconstructions = self(observations, should_preprocess=False, should_postprocess=False)
# Codebook loss. Notes:
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 9ba5a96..dcc6bd7 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -2,119 +2,163 @@
# Credits to https://github.com/karpathy/minGPT
# """
-# from dataclasses import dataclass
-# import math
-# from typing import Optional
-
-# from einops import rearrange
-# import torch
-# import torch.nn as nn
-# from torch.nn import functional as F
+from dataclasses import dataclass
+import math
+from typing import Optional
+from contextlib import contextmanager
+from einops import rearrange
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from loguru import logger
# from .kv_caching import KeysValues, KVCache
-# @dataclass
-# class TransformerConfig:
-# tokens_per_block: int
-# max_blocks: int
-# attention: str
+@dataclass
+class TransformerConfig:
+ # model_name: str = "stabilityai/stablelm-3b-4e1t"
+ # https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
+ vocab_size: int = 32000
+ embed_dim: int = 2048
+
+ max_blocks: int = 20
+ tokens_per_block: int = 17
+
+ model_name: str = "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
+ dropout: float = 0.1
+ rank: int = 32
-# num_layers: int
-# num_heads: int
-# embed_dim: int
-
-# embed_pdrop: float
-# resid_pdrop: float
-# attn_pdrop: float
-
-# @property
-# def max_tokens(self):
-# return self.tokens_per_block * self.max_blocks
+ @property
+ def max_tokens(self):
+ return self.tokens_per_block * self.max_blocks
+
+
+def freeze(n: nn.Module):
+ for p in n.parameters():
+ p.requires_grad = False
+ return n
-# class Transformer(nn.Module):
-# def __init__(self, config: TransformerConfig) -> None:
-# super().__init__()
-# self.config = config
-# self.drop = nn.Dropout(config.embed_pdrop)
-# self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
-# self.ln_f = nn.LayerNorm(config.embed_dim)
+class Transformer(nn.Module):
+ def __init__(self, config: TransformerConfig) -> None:
+ super().__init__()
+ self.config = config
+ self.model = load_pretrained_model(config)
+ self.ln_f = nn.Linear(self.model.config.vocab_size, config.embed_dim)
+ self.embedding = freeze(self.model.base_model.embed_tokens.to(torch.float)) # HACK: custom path to embeddings layer for model
-# def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
-# device = self.ln_f.weight.device # Assumption that all submodules are on the same device
-# return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
+ def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
+ device = self.ln_f.weight.device # Assumption that all submodules are on the same device
+ return KeysValues(n, 1, max_tokens, self.config.embed_dim, 1, device)
-# def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-# assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
-# x = self.drop(sequences)
-# for i, block in enumerate(self.blocks):
-# x = block(x, None if past_keys_values is None else past_keys_values[i])
+ def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
+ assert past_keys_values is None or len(past_keys_values) == 1
+ with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+ outputs = self.model(
+ inputs_embeds=sequences,
+ return_dict=True,
+ output_hidden_states=True,
+ )
+ x = outputs.logits
+ x = self.ln_f(x)
+
+ # fake it, since it's used to keep track of steps
+ if past_keys_values is not None:
+ k_size = past_keys_values[0]._k_cache._cache.size()
+ v_size = (k_size[0], k_size[1], x.shape[1], k_size[3])
+ past_keys_values[0].update(torch.rand(v_size), torch.rand(v_size))
+ return x
-# x = self.ln_f(x)
-# return x
+
-# class Block(nn.Module):
-# def __init__(self, config: TransformerConfig) -> None:
-# super().__init__()
-# self.ln1 = nn.LayerNorm(config.embed_dim)
-# self.ln2 = nn.LayerNorm(config.embed_dim)
-# self.attn = SelfAttention(config)
-# self.mlp = nn.Sequential(
-# nn.Linear(config.embed_dim, 4 * config.embed_dim),
-# nn.GELU(),
-# nn.Linear(4 * config.embed_dim, config.embed_dim),
-# nn.Dropout(config.resid_pdrop),
-# )
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, LoraConfig
+import peft
-# def forward(self, x: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-# x_attn = self.attn(self.ln1(x), past_keys_values)
-# x = x + x_attn
-# x = x + self.mlp(self.ln2(x))
-# return x
+def load_pretrained_model(config, device="cuda:0"):
+ tokenizer = AutoTokenizer.from_pretrained(config.model_name, trust_remote_code=True)
+ tokenizer.padding_side = "left"
+ if tokenizer.pad_token is None:
+ tokenizer.pad_token = tokenizer.eos_token
+ bnb_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_compute_dtype=torch.bfloat16,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_use_double_quant=True,
+ )
+ base_model = AutoModelForCausalLM.from_pretrained(
+ config.model_name,
+ device_map={"": device},
+ quantization_config=bnb_config,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True
+ )
+ peft_config = peft.LoraConfig(
+ peft.TaskType.CAUSAL_LM,
+ inference_mode=False,
+ r=config.rank,
+ lora_alpha=config.rank*2, # Adjusting the LoRA rank is essential, and so is selecting an apt alpha value. A good heuristic is setting alpha at twice the rank's value. https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms
+ lora_dropout=config.dropout,
+ # TODO: If you're incorporating LoRA, ensure it's applied across all layers, not just to the Key and Value matrices, to maximize model performance.
+ target_modules=[
+ "self_attn.q_proj",
+ "self_attn.k_proj",
+ "self_attn.v_proj",
+ "self_attn.o_proj",
+ "mlp.gate_proj",
+ "mlp.up_proj",
+ "mlp.down_proj",
+ # "wte", "embed_tokens",
+ # "lm_head",
+ ],
+ # bias="lora_only",
+ # tune the embedding layer and prediction head
+ modules_to_save = ["lm_head",], # we want the classifier parameters to be trained too when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify modules_to_save.
+ )
+ base_model_peft = base_model
+ # base_model_peft = peft.get_peft_model(base_model, peft_config)
+ # base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make and set an adapter
+ disable_causal_mask_always()
+ # print(base_model_peft.print_trainable_parameters())
+ logger.debug(f"loaded model {base_model_peft}")
+ return base_model_peft
+@contextmanager
+def set_adapter(model, adapter_name):
+ old_adapter_name = model.active_adapter
+ try:
+ if adapter_name is not None:
+ model.set_adapter(adapter_name)
+ yield model
+ else:
+ with model.disable_adapter():
+ yield model
+ finally:
+ model.set_adapter(old_adapter_name)
-# class SelfAttention(nn.Module):
-# def __init__(self, config: TransformerConfig) -> None:
-# super().__init__()
-# assert config.embed_dim % config.num_heads == 0
-# assert config.attention in ('causal', 'block_causal')
-# self.num_heads = config.num_heads
-# self.key = nn.Linear(config.embed_dim, config.embed_dim)
-# self.query = nn.Linear(config.embed_dim, config.embed_dim)
-# self.value = nn.Linear(config.embed_dim, config.embed_dim)
-# self.attn_drop = nn.Dropout(config.attn_pdrop)
-# self.resid_drop = nn.Dropout(config.resid_pdrop)
-# self.proj = nn.Linear(config.embed_dim, config.embed_dim)
+def disable_causal_mask_always():
+ import transformers.models.llama.modeling_llama as modeling
-# causal_mask = torch.tril(torch.ones(config.max_tokens, config.max_tokens))
-# block_causal_mask = torch.max(causal_mask, torch.block_diag(*[torch.ones(config.tokens_per_block, config.tokens_per_block) for _ in range(config.max_blocks)]))
-# self.register_buffer('mask', causal_mask if config.attention == 'causal' else block_causal_mask)
+ decoder_fn = modeling._make_causal_mask
-# def forward(self, x: torch.Tensor, kv_cache: Optional[KVCache] = None) -> torch.Tensor:
-# B, T, C = x.size()
-# if kv_cache is not None:
-# b, nh, L, c = kv_cache.shape
-# assert nh == self.num_heads and b == B and c * nh == C
-# else:
-# L = 0
+ def encoder_fn(*args, **kwargs):
+ return torch.zeros_like(decoder_fn(*args, **kwargs))
-# q = self.query(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs)
-# k = self.key(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs)
-# v = self.value(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs)
+ modeling._make_causal_mask = encoder_fn
-# if kv_cache is not None:
-# kv_cache.update(k, v)
-# k, v = kv_cache.get()
+@contextmanager
+def disable_causal_mask():
+ import transformers.models.llama.modeling_llama as modeling
-# att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-# att = att.masked_fill(self.mask[L:L + T, :L + T] == 0, float('-inf'))
-# att = F.softmax(att, dim=-1)
-# att = self.attn_drop(att)
-# y = att @ v
-# y = rearrange(y, 'b h t e -> b t (h e)')
+ decoder_fn = modeling._make_causal_mask
-# y = self.resid_drop(self.proj(y))
+ def encoder_fn(*args, **kwargs):
+ return torch.zeros_like(decoder_fn(*args, **kwargs))
-# return y
+ try:
+ modeling._make_causal_mask = encoder_fn
+ yield
+ finally:
+ modeling._make_causal_mask = decoder_fn
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 94bd119..1506926 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -6,13 +6,12 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
-from dataset import Batch
-from .kv_caching import KeysValues
-from .slicer import Embedder, Head
-from .tokenizer import Tokenizer
-# from .transformer import Transformer, TransformerConfig
-from .bigvae import BigVAE, BigVAEConfig
-from utils import init_weights, LossWithIntermediateLosses
+from src.dataset import Batch
+from src.models.kv_caching import KeysValues
+from src.models.slicer import Embedder, Head
+from src.models.tokenizer import Tokenizer
+from src.models.transformer import Transformer, TransformerConfig
+from src.utils import init_weights, LossWithIntermediateLosses
@dataclass
@@ -27,21 +26,35 @@ class WorldModel(nn.Module):
def __init__(self, obs_vocab_size: int, act_vocab_size: int, config: BigVAEConfig) -> None:
super().__init__()
self.obs_vocab_size, self.act_vocab_size = obs_vocab_size, act_vocab_size
- self.config = config
- self.transformer = BigVAE(config)
+ self.config = config
all_but_last_obs_tokens_pattern = torch.ones(config.tokens_per_block)
all_but_last_obs_tokens_pattern[-2] = 0
act_tokens_pattern = torch.zeros(self.config.tokens_per_block)
act_tokens_pattern[-1] = 1
obs_tokens_pattern = 1 - act_tokens_pattern
+
+ self.transformer = Transformer(config)
+ transformer_embedding = self.transformer.embedding
self.pos_emb = nn.Embedding(config.max_tokens, config.embed_dim)
+ self.act_emb = nn.Embedding(act_vocab_size, config.embed_dim)
+ # FIXME: having slices is unclear. maybe it's better just to have obs and action embeddings?
self.embedder = Embedder(
max_blocks=config.max_blocks,
block_masks=[act_tokens_pattern, obs_tokens_pattern],
- embedding_tables=nn.ModuleList([nn.Embedding(act_vocab_size, config.embed_dim), nn.Embedding(obs_vocab_size, config.embed_dim)])
+ embedding_tables=nn.ModuleList([self.act_emb, transformer_embedding])
+ )
+
+ # why have this? Well I worry that the transformer can't adapt, since so much is frozen
+ # TODO: If I get the dynamics model working, maybe try without it
+ self.post_embed = nn.Sequential(
+ nn.Linear(config.embed_dim, config.embed_dim),
+ nn.ReLU(),
+ nn.Linear(config.embed_dim, config.embed_dim),
+ # nn.ReLU(),
+ # nn.Linear(config.embed_dim, config.embed_dim)
)
self.head_observations = Head(
@@ -74,19 +87,28 @@ class WorldModel(nn.Module):
)
)
- self.apply(init_weights)
+ # don't apply to transformer or transformer/obs embeddings
+ self.act_emb.apply(init_weights)
+ self.pos_emb.apply(init_weights)
+ self.post_embed.apply(init_weights)
+ self.head_observations.apply(init_weights)
+ self.head_rewards.apply(init_weights)
+ self.head_ends.apply(init_weights)
+
+
def __repr__(self) -> str:
return "world_model"
def forward(self, tokens: torch.LongTensor, past_keys_values: Optional[KeysValues] = None) -> WorldModelOutput:
- num_steps = tokens.size(1) # (B, T)
+ num_steps = tokens.size(1) # (B=8, T=170) where often the last 10 are actons
assert num_steps <= self.config.max_tokens
prev_steps = 0 if past_keys_values is None else past_keys_values.size
sequences = self.embedder(tokens, num_steps, prev_steps) + self.pos_emb(prev_steps + torch.arange(num_steps, device=tokens.device))
-
+ # [batch=8, num_steps=170, embed_size=2048]
+ sequences = self.post_embed(sequences)
x = self.transformer(sequences, past_keys_values)
logits_observations = self.head_observations(x, num_steps=num_steps, prev_steps=prev_steps)
@@ -97,11 +119,13 @@ class WorldModel(nn.Module):
def compute_loss(self, batch: Batch, tokenizer: Tokenizer, **kwargs: Any) -> LossWithIntermediateLosses:
- with torch.no_grad():
- obs_tokens = tokenizer.encode(batch['observations'], should_preprocess=True).tokens # (BL, K)
+ # with torch.no_grad():
+ # [B=8, S=10, Colors=3, H=64, W=64] -> [B=8, S=10, 16]
+ obs_tokens = tokenizer.encode(batch['observations'], should_preprocess=True).tokens # (BL, K)
act_tokens = rearrange(batch['actions'], 'b l -> b l 1')
tokens = rearrange(torch.cat((obs_tokens, act_tokens), dim=2), 'b l k1 -> b (l k1)') # (B, L(K+1))
+ # So first 10 are observation, the last 10 tokens are actions
outputs = self(tokens)
diff --git a/src/play.py b/src/play.py
index 7b18dee..e3486a5 100644
--- a/src/play.py
+++ b/src/play.py
@@ -1,4 +1,4 @@
-from functools import partial
+from functools import partial
from pathlib import Path
import hydra
@@ -6,55 +6,96 @@ from hydra.utils import instantiate
from omegaconf import DictConfig
import torch
-from agent import Agent
-from envs import SingleProcessEnv, WorldModelEnv
-from game import AgentEnv, EpisodeReplayEnv, Game
-from models.actor_critic import ActorCritic
-from models.world_model import WorldModel
+from src.agent import Agent
+from src.envs import SingleProcessEnv, WorldModelEnv
+from src.game import AgentEnv, EpisodeReplayEnv, Game
+from src.models.actor_critic import ActorCritic
+from src.models.world_model import WorldModel
+from src.models.tokenizer import Tokenizer
@hydra.main(config_path="../config", config_name="trainer")
def main(cfg: DictConfig):
device = torch.device(cfg.common.device)
- assert cfg.mode in ('episode_replay', 'agent_in_env', 'agent_in_world_model', 'play_in_world_model')
+ assert cfg.mode in (
+ "episode_replay",
+ "agent_in_env",
+ "agent_in_world_model",
+ "play_in_world_model",
+ )
env_fn = partial(instantiate, config=cfg.env.test)
test_env = SingleProcessEnv(env_fn)
- if cfg.mode.startswith('agent_in_'):
+ if cfg.mode.startswith("agent_in_"):
h, w, _ = test_env.env.unwrapped.observation_space.shape
else:
h, w = 64, 64
multiplier = 800 // h
size = [h * multiplier, w * multiplier]
-
- if cfg.mode == 'episode_replay':
- env = EpisodeReplayEnv(replay_keymap_name=cfg.env.keymap, episode_dir=Path('media/episodes'))
- keymap = 'episode_replay'
+
+ if cfg.mode == "episode_replay":
+ env = EpisodeReplayEnv(
+ replay_keymap_name=cfg.env.keymap, episode_dir=Path("media/episodes")
+ )
+ keymap = "episode_replay"
else:
- tokenizer = instantiate(cfg.tokenizer)
- world_model = WorldModel(obs_vocab_size=tokenizer.vocab_size, act_vocab_size=test_env.num_actions, config=instantiate(cfg.world_model))
- actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=test_env.num_actions)
+ # tokenizer = instantiate(cfg.tokenizer)
+ world_model = WorldModel(
+ obs_vocab_size=cfg.tokenizer.vocab_size,
+ act_vocab_size=test_env.num_actions,
+ config=instantiate(cfg.world_model),
+ )
+ transformer_embedding = world_model.transformer.embedding
+ tokenizer = Tokenizer(
+ transformer_embedding=transformer_embedding,
+ vocab_size=cfg.tokenizer.vocab_size,
+ embed_dim=cfg.tokenizer.embed_dim,
+ encoder=instantiate(cfg.tokenizer.encoder),
+ decoder=instantiate(cfg.tokenizer.decoder),
+ )
+ actor_critic = ActorCritic(
+ **cfg.actor_critic, act_vocab_size=test_env.num_actions
+ )
agent = Agent(tokenizer, world_model, actor_critic).to(device)
- agent.load(Path('checkpoints/last.pt'), device)
+ agent.load(Path("checkpoints/last.pt"), device)
- if cfg.mode == 'play_in_world_model':
- env = WorldModelEnv(tokenizer=agent.tokenizer, world_model=agent.world_model, device=device, env=env_fn())
+ if cfg.mode == "play_in_world_model":
+ env = WorldModelEnv(
+ tokenizer=agent.tokenizer,
+ world_model=agent.world_model,
+ device=device,
+ env=env_fn(),
+ )
keymap = cfg.env.keymap
-
- elif cfg.mode == 'agent_in_env':
- env = AgentEnv(agent, test_env, cfg.env.keymap, do_reconstruction=cfg.reconstruction)
- keymap = 'empty'
+
+ elif cfg.mode == "agent_in_env":
+ env = AgentEnv(
+ agent, test_env, cfg.env.keymap, do_reconstruction=cfg.reconstruction
+ )
+ keymap = "empty"
if cfg.reconstruction:
size[1] *= 3
- elif cfg.mode == 'agent_in_world_model':
- wm_env = WorldModelEnv(tokenizer=agent.tokenizer, world_model=agent.world_model, device=device, env=env_fn())
+ elif cfg.mode == "agent_in_world_model":
+ wm_env = WorldModelEnv(
+ tokenizer=agent.tokenizer,
+ world_model=agent.world_model,
+ device=device,
+ env=env_fn(),
+ )
env = AgentEnv(agent, wm_env, cfg.env.keymap, do_reconstruction=False)
- keymap = 'empty'
+ keymap = "empty"
- game = Game(env, keymap_name=keymap, size=size, fps=cfg.fps, verbose=bool(cfg.header), record_mode=bool(cfg.save_mode))
+ game = Game(
+ env,
+ keymap_name=keymap,
+ size=size,
+ fps=cfg.fps,
+ verbose=bool(cfg.header),
+ record_mode=bool(cfg.save_mode),
+ )
game.run()
diff --git a/src/trainer.py b/src/trainer.py
index ec94572..c3d6399 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -14,14 +14,15 @@ import torch.nn as nn
from tqdm import tqdm
import wandb
-from agent import Agent
-from collector import Collector
-from envs import SingleProcessEnv, MultiProcessEnv
-from episode import Episode
-from make_reconstructions import make_reconstructions_from_batch
-from models.actor_critic import ActorCritic
-from models.world_model import WorldModel
-from utils import configure_optimizer, EpisodeDirManager, set_seed
+from src.agent import Agent
+from src.collector import Collector
+from src.envs import SingleProcessEnv, MultiProcessEnv
+from src.episode import Episode
+from src.make_reconstructions import make_reconstructions_from_batch
+from src.models.actor_critic import ActorCritic
+from src.models.world_model import WorldModel
+from src.utils import configure_optimizer, EpisodeDirManager, set_seed
+from src.models.tokenizer import Tokenizer
class Trainer:
@@ -46,6 +47,7 @@ class Trainer:
self.reconstructions_dir = self.media_dir / 'reconstructions'
if not cfg.common.resume:
+ print('cwd', Path.cwd())
config_dir = Path('config')
config_path = config_dir / 'trainer.yaml'
config_dir.mkdir(exist_ok=False, parents=False)
@@ -79,8 +81,15 @@ class Trainer:
assert self.cfg.training.should or self.cfg.evaluation.should
env = train_env if self.cfg.training.should else test_env
- tokenizer = instantiate(cfg.tokenizer)
- world_model = WorldModel(obs_vocab_size=tokenizer.vocab_size, act_vocab_size=env.num_actions, config=instantiate(cfg.world_model))
+ world_model = WorldModel(obs_vocab_size=cfg.tokenizer.vocab_size, act_vocab_size=env.num_actions, config=instantiate(cfg.world_model))
+ transformer_embedding = world_model.transformer.embedding
+ tokenizer = Tokenizer(
+ transformer_embedding=transformer_embedding,
+ vocab_size=cfg.tokenizer.vocab_size,
+ embed_dim=cfg.tokenizer.embed_dim,
+ encoder=instantiate(cfg.tokenizer.encoder),
+ decoder=instantiate(cfg.tokenizer.decoder),
+ )
actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=env.num_actions)
self.agent = Agent(tokenizer, world_model, actor_critic).to(self.device)
print(f'{sum(p.numel() for p in self.agent.tokenizer.parameters())} parameters in agent.tokenizer')
@@ -88,7 +97,11 @@ class Trainer:
print(f'{sum(p.numel() for p in self.agent.actor_critic.parameters())} parameters in agent.actor_critic')
self.optimizer_tokenizer = torch.optim.Adam(self.agent.tokenizer.parameters(), lr=cfg.training.learning_rate)
- self.optimizer_world_model = configure_optimizer(self.agent.world_model, cfg.training.learning_rate, cfg.training.world_model.weight_decay)
+ # self.optimizer_world_model = configure_optimizer([self.agent.tokenizer, self.agent.world_model], cfg.training.learning_rate, cfg.training.world_model.weight_decay)
+ self.optimizer_world_model = torch.optim.Adam(
+ list(self.agent.tokenizer.parameters())+list(self.agent.world_model.parameters()),
+ lr=cfg.training.learning_rate
+ )
self.optimizer_actor_critic = torch.optim.Adam(self.agent.actor_critic.parameters(), lr=cfg.training.learning_rate)
if cfg.initialization.path_to_checkpoint is not None:
@@ -136,10 +149,10 @@ class Trainer:
if epoch > cfg_tokenizer.start_after_epochs:
metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)
- self.agent.tokenizer.eval()
if epoch > cfg_world_model.start_after_epochs:
metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)
+ self.agent.tokenizer.eval()
self.agent.world_model.eval()
if epoch > cfg_actor_critic.start_after_epochs:
@@ -169,6 +182,7 @@ class Trainer:
if max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(component.parameters(), max_grad_norm)
+
optimizer.step()
metrics = {f'{str(component)}/train/total_loss': loss_total_epoch, **intermediate_losses}
@@ -190,7 +204,7 @@ class Trainer:
if epoch > cfg_world_model.start_after_epochs:
metrics_world_model = self.eval_component(self.agent.world_model, cfg_world_model.batch_num_samples, sequence_length=self.cfg.common.sequence_length, tokenizer=self.agent.tokenizer)
- if epoch > cfg_actor_critic.start_after_epochs:
+ if epoch > cfg_world_model.start_after_epochs:
self.inspect_imagination(epoch)
if cfg_tokenizer.save_reconstructions:
diff --git a/src/utils.py b/src/utils.py
index 1f134d4..0f09590 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -3,47 +3,64 @@ import cv2
from pathlib import Path
import random
import shutil
-
+from loguru import logger
import numpy as np
import torch
import torch.nn as nn
-from episode import Episode
+from src.episode import Episode
+
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_names):
+def configure_optimizer(models, learning_rate, weight_decay, *blacklist_module_names):
"""Credits to https://github.com/karpathy/minGPT"""
+ # FIXME: check this is still good for LoRA
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
+ decay_params = []
+ no_decay_params = []
+ param_dict = {}
whitelist_weight_modules = (torch.nn.Linear, torch.nn.Conv1d)
- blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
- for mn, m in model.named_modules():
- for pn, p in m.named_parameters():
- fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
- if any([fpn.startswith(module_name) for module_name in blacklist_module_names]):
- no_decay.add(fpn)
- elif 'bias' in pn:
- # all biases will not be decayed
- no_decay.add(fpn)
- elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
- # weights of whitelist modules will be weight decayed
- decay.add(fpn)
- elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
- # weights of blacklist modules will NOT be weight decayed
- no_decay.add(fpn)
+ blacklist_weight_modules = tuple(ALL_LAYERNORM_LAYERS+[torch.nn.Embedding])
+ for model in models:
+ for mn, m in model.named_modules():
+ for pn, p in m.named_parameters():
+ fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
+ if any([fpn.startswith(module_name) for module_name in blacklist_module_names]):
+ no_decay.add(fpn)
+ no_decay_params.append(p)
+ elif 'bias' in pn:
+ # all biases will not be decayed
+ no_decay.add(fpn)
+ no_decay_params.append(p)
+ elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
+ # weights of whitelist modules will be weight decayed
+ decay.add(fpn)
+ decay_params.append(p)
+ elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
+ # weights of blacklist modules will NOT be weight decayed
+ no_decay.add(fpn)
+ no_decay_params.append(p)
+ else:
+ logger.warning(f"Parameter {fpn} of module {mn} not handled!")
+ # raise NotImplementedError(f"Parameter {fpn} of module {m} not handled!")
+ decay.add(fpn)
+ decay_params.append(p)
- # validate that we considered every parameter
- param_dict = {pn: p for pn, p in model.named_parameters()}
+ # validate that we considered every parameter
+ param_dict.update({pn: p for pn, p in model.named_parameters()})
inter_params = decay & no_decay
union_params = decay | no_decay
+ # logger.debug(f"decay {decay} no_decay {no_decay}")
assert len(inter_params) == 0, f"parameters {str(inter_params)} made it into both decay/no_decay sets!"
assert len(param_dict.keys() - union_params) == 0, f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!"
# create the pytorch optimizer object
optim_groups = [
- {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": weight_decay},
- {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
+ {"params": no_decay_params, "weight_decay": weight_decay},
+ {"params": decay_params, "weight_decay": 0.0},
]
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate)
return optimizer