



import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	json.load(open(snake_case_				,   '''r'''      )      )
						UpperCAmelCase_					=	GPTNeoConfig(
						    hidden_size=config_json['''n_embd''']				,   num_layers=config_json['''n_layer''']				,   num_heads=config_json['''n_head''']				,   attention_types=config_json['''attention_types''']				,   max_position_embeddings=config_json['''n_positions''']				,   resid_dropout=config_json['''res_dropout''']				,   embed_dropout=config_json['''embed_dropout''']				,   attention_dropout=config_json['''attn_dropout''']				,   )
						print(f"Building PyTorch model from configuration: {config}"      )
						UpperCAmelCase_					=	GPTNeoForCausalLM(snake_case_      )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(snake_case_				,   snake_case_				,   snake_case_      )

						# Save pytorch-model
						print(f"Save PyTorch model to {pytorch_dump_path}"      )
						model.save_pretrained(snake_case_      )


if __name__ == "__main__":
							UpperCamelCase_      			=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained mesh-tf model. \n"
							        "This specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCamelCase_      			=  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()