




'''simple docstring'''
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


_SCREAMING_SNAKE_CASE			=						4
_SCREAMING_SNAKE_CASE			=						3
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					pass





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Any:
			for shard in shards:
						for i in range(SCREAMING_SNAKE_CASE_     ):
									yield {"i": i, "shard": shard}





def 			lowerCamelCase(							)		->		Optional[Any]:
			A_									= int(os.environ['RANK']     )
			A_									= int(os.environ['WORLD_SIZE']     )

			A_									= ArgumentParser()
			parser.add_argument('--streaming'       ,type=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--local_rank'       ,type=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--num_workers'       ,type=SCREAMING_SNAKE_CASE_       ,default=0     )
			A_									= parser.parse_args()
			A_									= args.streaming
			A_									= args.num_workers

			A_									= {'shards': [F'shard_{shard_idx}' for shard_idx in range(SCREAMING_SNAKE_CASE_     )]}
			A_									= IterableDataset.from_generator(SCREAMING_SNAKE_CASE_       ,gen_kwargs=SCREAMING_SNAKE_CASE_     )
			if not streaming:
						A_									= Dataset.from_list(list(SCREAMING_SNAKE_CASE_     )     )

			A_									= split_dataset_by_node(SCREAMING_SNAKE_CASE_       ,rank=SCREAMING_SNAKE_CASE_       ,world_size=SCREAMING_SNAKE_CASE_     )
			A_									= torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE_       ,num_workers=SCREAMING_SNAKE_CASE_     )

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

			A_									= 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()










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""openai-gpt"""
					_UpperCamelCase							=		{
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}



					def __init__(	self     :						Any      ,       _lowercase     :						List[Any]=40_478      ,       _lowercase     :						Tuple=512      ,       _lowercase     :						Dict=768      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Optional[int]=12      ,       _lowercase     :						Tuple="gelu"      ,       _lowercase     :						int=0.1      ,       _lowercase     :						Tuple=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						List[str]=1E-5      ,       _lowercase     :						str=0.02      ,       _lowercase     :						Optional[Any]="cls_index"      ,       _lowercase     :						int=True      ,       _lowercase     :						str=None      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						List[Any]=0.1      ,       **_lowercase     :						int      ,       )    ->							Optional[int]:
								A_									= vocab_size
								A_									= n_positions
								A_									= n_embd
								A_									= n_layer
								A_									= n_head
								A_									= afn
								A_									= resid_pdrop
								A_									= embd_pdrop
								A_									= attn_pdrop
								A_									= layer_norm_epsilon
								A_									= initializer_range
								A_									= summary_type
								A_									= summary_use_proj
								A_									= summary_activation
								A_									= summary_first_dropout
								A_									= summary_proj_to_labels
								super().__init__(**_lowercase)




