



"""simple docstring"""




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def 		__snake_case		(   )						->     Tuple:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					HfArgumentParser(SCREAMING_SNAKE_CASE__       )
     try:
          _UpperCAmelCase    :			List[str]              =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          _UpperCAmelCase    :			List[str]              =					"Arg --no_{0} is no longer used, please use --no-{0} instead."
          _UpperCAmelCase    :			str              =					" ".join(str(SCREAMING_SNAKE_CASE__       ).split(" "       )[:-1]       )
          _UpperCAmelCase    :			List[Any]              =					""
          _UpperCAmelCase    :			Any              =					eval(str(SCREAMING_SNAKE_CASE__       ).split(" "       )[-1]       )
          _UpperCAmelCase    :			int              =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]       )
               else:
                    wrong_args.append(SCREAMING_SNAKE_CASE__       )
          if len(SCREAMING_SNAKE_CASE__       ) > 0:
               _UpperCAmelCase    :			int              =					full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE__       )
          raise ValueError(SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			Union[str, Any]              =					PyTorchBenchmark(args=SCREAMING_SNAKE_CASE__       )
     benchmark.run()


if __name__ == "__main__":
 main()





"""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
 )
