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_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  List[Any]:






      '''simple docstring'''





      UpperCAmelCase    : int      =json.load(open(__lowerCAmelCase		,					'''r'''						)						)
      UpperCAmelCase    : int      =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    : Optional[int]      =GPTNeoForCausalLM(__lowerCAmelCase						)

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

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


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

import math





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  list[int]:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase		,					__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =f'''Input value of [number={number}] must be an integer'''
            raise TypeError(__lowerCAmelCase						)
      if number < 1:
            UpperCAmelCase    : Dict      =f'''Input value of [number={number}] must be a positive integer'''
            raise ValueError(__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    : Optional[Any]      =math.floor(math.sqrt(__lowerCAmelCase						)						)
            else:
                  UpperCAmelCase    : List[str]      =math.floor(
                      math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						)						)
            sequence.append(__lowerCAmelCase						)
      return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()
