



"""simple docstring"""




import re

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     Optional[int]:





     '''simple docstring'''

     def docstring_decorator(SCREAMING_SNAKE_CASE__			:		Dict       ):
          _UpperCAmelCase    :			Tuple              =					fn.__doc__
          _UpperCAmelCase    :			List[Any]              =					func_doc.split("\n"       )
          _UpperCAmelCase    :			Optional[int]              =					0
          while i < len(SCREAMING_SNAKE_CASE__       ) and re.search(R"^\s*Examples?:\s*$"		,						lines[i]       ) is None:
               i += 1
          if i < len(SCREAMING_SNAKE_CASE__       ):
               _UpperCAmelCase    :			List[str]              =					example_docstring
               _UpperCAmelCase    :			Dict              =					"\n".join(SCREAMING_SNAKE_CASE__       )
          else:
               raise ValueError(
                   f'The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '
                   f'current docstring is:\n{func_doc}'       )
          _UpperCAmelCase    :			str              =					func_doc
          return fn

     return docstring_decorator





"""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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
     _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
     _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )

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

     # Save pytorch-model
     _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
     torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
     print(f'Save configuration file to {pytorch_config_dump_path}'       )
     with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
          f.write(config.to_json_string()       )


if __name__ == "__main__":
 _lowerCAmelCase       :      List[str]							=							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       :      Optional[int]							=							parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )
