




"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					str:
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase			      =					GPTaConfig()
			else:
						lowerCamelCase			      =					GPTaConfig.from_json_file(snake_case__				)
			lowerCamelCase			      =					GPTaModel(snake_case__				)

			# Load weights from numpy
			load_tf_weights_in_gpta(snake_case__       , snake_case__       , snake_case__				)

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


if __name__ == "__main__":
					lowerCAmelCase							:			int     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--gpt2_checkpoint_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(
					    """--gpt2_config_file""",
					    default="""""",
					    type=str,
					    help=(
					        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					lowerCAmelCase							:			Dict     							=							parser.parse_args()
					convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









"""simple docstring"""




from __future__ import annotations



def     a__ (				snake_case__       , snake_case__				)						->					bool:
			if len(snake_case__				) == 0:
						return False
			lowerCamelCase			      =					len(snake_case__				) // 2
			if a_list[midpoint] == item:
						return True
			if item < a_list[midpoint]:
						return binary_search(a_list[:midpoint]       , snake_case__				)
			else:
						return binary_search(a_list[midpoint + 1 :]       , snake_case__				)


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



