



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_							:      int      =  4
UpperCAmelCase_							:      int      =  3






class      UpperCamelCase    (						_UpperCAmelCase     ):
	pass





def  UpperCamelCase						(       _A			:				List[str]    )->       List[Any]:





						"""simple docstring"""





						for shard in shards:
												for i in range(_A    ):
																		yield {"i": i, "shard": shard}
def  UpperCamelCase						(       )->       Dict:





						"""simple docstring"""





						A__							=       int(os.environ["RANK"]    )
						A__							=       int(os.environ["WORLD_SIZE"]    )

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

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

						A__							=       split_dataset_by_node(_A			,							rank=_A			,							world_size=_A    )
						A__							=       torch.utils.data.DataLoader(_A			,							num_workers=_A    )

						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()










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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
													raise ValueError(
													    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"		)



	def 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) or len(self.rope_scaling		) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    F"""got {self.rope_scaling}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





