






"""simple docstring"""



import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   str ,				snake_case					:   Tuple ,				snake_case					:   Dict							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						List[Any]       =       {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						Tuple       =       MODELS[model_size]  # set model hyperparameters
     UpperCAmelCase__	:						List[str]       =       ImageGPTConfig(n_embd=snake_case ,				n_layer=snake_case ,				n_head=snake_case							)
     UpperCAmelCase__	:						List[str]       =       ImageGPTForCausalLM(snake_case							)

     # Load weights from numpy
     load_tf_weights_in_imagegpt(snake_case ,				snake_case ,				snake_case							)

     # Save pytorch-model
     UpperCAmelCase__	:						Optional[Any]       =       pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__	:						Optional[Any]       =       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  :  Optional[int]   =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--imagegpt_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help="""Path to the TensorFlow checkpoint path.""",
 )
 parser.add_argument(
     """--model_size""",
     default=None,
     type=str,
     required=True,
     help="""Size of the model (can be either 'small', 'medium' or 'large').""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase  :  Tuple   =			parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )










"""simple docstring"""



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int							)->		list:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       [True for i in range(n + 1							)]
     UpperCAmelCase__	:						List[str]       =       [i - 1 for i in range(n + 1							)]
     UpperCAmelCase__	:						int       =       []
     for i in range(2 ,				n + 1							):
          if is_prime[i]:
               primes.append(snake_case							)
          for j in range(0 ,				len(snake_case							)							):
               if i * primes[j] >= n:
                    break
               UpperCAmelCase__	:						Optional[Any]       =       False

               if i % primes[j] == 0:
                    UpperCAmelCase__	:						str       =       totients[i] * primes[j]
                    break

               UpperCAmelCase__	:						Union[str, Any]       =       totients[i] * (primes[j] - 1)

     return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()


