
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 				lowerCamelCase__				(						a__  :  str	,      a__  :  Dict	,      a__  :  int     ):
       UpperCamelCase_							       =       json.load(open(SCREAMING_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(SCREAMING_SNAKE_CASE__     )

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

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


if __name__ == "__main__":
      _A	  =					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.'''
      )
      _A	  =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





def 				lowerCamelCase__				(						a__  :  float	,      a__  :  float	,      a__  :  float     )		->	float:
       if mass < 0:
              raise ValueError("""The mass of the body cannot be negative"""     )
       if radius <= 0:
              raise ValueError("""The radius is always a positive non zero integer"""     )
       return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



