

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	# Construct model
	if openai_config_file == "":
		__lowerCamelCase    :    Optional[Any] 	=			OpenAIGPTConfig()
	else:
		__lowerCamelCase    :    Optional[int] 	=			OpenAIGPTConfig.from_json_file(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[Any] 	=			OpenAIGPTModel(lowerCamelCase__      )

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

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


if __name__ == "__main__":
					a		=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."""
					    ),
					)
					a		=parser.parse_args()
					convert_openai_checkpoint_to_pytorch(
					    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
					)







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




