


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


A_     :       List[Any]   							=4
A_     :       Dict   							=3





class 							lowercase_					(			UpperCamelCase__):






							"""simple docstring"""







							pass




def 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




						for shard in shards:
												for i in range(SCREAMING_SNAKE_CASE_   ):
																		yield {"i": i, "shard": shard}




def 	lowerCamelCase_							(       ):



						"""simple docstring"""




						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'''
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			# Construct model
			if openai_config_file == "":
						A_									= OpenAIGPTConfig()
			else:
						A_									= OpenAIGPTConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
			A_									= OpenAIGPTModel(SCREAMING_SNAKE_CASE_     )

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			# Save pytorch-model
			A_									= pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			A_									= pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'     )
			torch.save(model.state_dict()       ,SCREAMING_SNAKE_CASE_     )
			print(F'Save configuration file to {pytorch_config_dump_path}'     )
			with open(SCREAMING_SNAKE_CASE_       ,'w'       ,encoding='utf-8'     ) as f:
						f.write(config.to_json_string()     )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--openai_checkpoint_folder_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--openai_config_file",
							    default="",
							    type=str,
							    help=(
							        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
							        "This specifies the model architecture."
							    ),
							)
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)




