





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


UpperCamelCase__   : Optional[Any]					  = 4
UpperCamelCase__   : Union[str, Any]					  = 3







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					pass




def    A_(			A   ):
	for shard in shards:
		for i in range(A   ):
			yield {"i": i, "shard": shard}




def    A_(			):
	UpperCAmelCase_								=     int(os.environ["""RANK"""]   )
	UpperCAmelCase_								=     int(os.environ["""WORLD_SIZE"""]   )

	UpperCAmelCase_								=     ArgumentParser()
	parser.add_argument("""--streaming"""				,      type=A   )
	parser.add_argument("""--local_rank"""				,      type=A   )
	parser.add_argument("""--num_workers"""				,      type=A				,      default=0   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     args.streaming
	UpperCAmelCase_								=     args.num_workers

	UpperCAmelCase_								=     {"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(A   )]}
	UpperCAmelCase_								=     IterableDataset.from_generator(A				,      gen_kwargs=A   )
	if not streaming:
		UpperCAmelCase_								=     Dataset.from_list(list(A   )   )

	UpperCAmelCase_								=     split_dataset_by_node(A				,      rank=A				,      world_size=A   )
	UpperCAmelCase_								=     torch.utils.data.DataLoader(A				,      num_workers=A   )

	UpperCAmelCase_								=     NUM_SHARDS * NUM_ITEMS_PER_SHARD
	UpperCAmelCase_								=     full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)   )

	UpperCAmelCase_								=     sum(1 for _ in dataloader   )
	if local_size != expected_local_size:
		raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""   )


if __name__ == "__main__":
				main()









from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase__   : Any					  = logging.get_logger(__name__)

UpperCamelCase__   : int					  = {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       'trajectory_transformer'
					lowerCamelCase     :     Optional[Any]			    =       ['past_key_values']
					lowerCamelCase     :     Tuple			    =       {
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}





					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  Optional[Any]=1_00 ,					__lowercase    :  int=5 ,					__lowercase    :  str=1 ,					__lowercase    :  Union[str, Any]=1 ,					__lowercase    :  Optional[Any]=2_49 ,					__lowercase    :  Tuple=6 ,					__lowercase    :  Tuple=17 ,					__lowercase    :  List[str]=25 ,					__lowercase    :  Tuple=4 ,					__lowercase    :  Dict=4 ,					__lowercase    :  int=1_28 ,					__lowercase    :  List[str]=0.1 ,					__lowercase    :  Optional[Any]=0.1 ,					__lowercase    :  Union[str, Any]=0.1 ,					__lowercase    :  Tuple=0.0006 ,					__lowercase    :  Optional[int]=5_12 ,					__lowercase    :  Any=0.02 ,					__lowercase    :  Tuple=1e-12 ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  Dict=True ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  str=5_02_56 ,					__lowercase    :  Any=5_02_56 ,					**__lowercase    :  str ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     action_weight
						UpperCAmelCase_								=     reward_weight
						UpperCAmelCase_								=     value_weight
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     block_size
						UpperCAmelCase_								=     action_dim
						UpperCAmelCase_								=     observation_dim
						UpperCAmelCase_								=     transition_dim
						UpperCAmelCase_								=     learning_rate
						UpperCAmelCase_								=     n_layer
						UpperCAmelCase_								=     n_head
						UpperCAmelCase_								=     n_embd
						UpperCAmelCase_								=     embd_pdrop
						UpperCAmelCase_								=     attn_pdrop
						UpperCAmelCase_								=     resid_pdrop
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     layer_norm_eps
						UpperCAmelCase_								=     kaiming_initializer_range
						UpperCAmelCase_								=     use_cache
						super().__init__(pad_token_id=__lowercase ,					bos_token_id=__lowercase ,					eos_token_id=__lowercase ,					**__lowercase		)


