






'''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  lowerCAmelCase__		(					a_  :     int			,						a_  :     Tuple			,						a_  :     Any			)   -> Any:
       # Construct configuration depending on size
       UpperCAmelCase__	:   Optional[Any]												=	{'''small''': (5_1_2, 8, 2_4), '''medium''': (1_0_2_4, 8, 3_6), '''large''': (1_5_3_6, 1_6, 4_8)}
       UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	MODELS[model_size]  # set model hyperparameters
       UpperCAmelCase__	:   Union[str, Any]												=	ImageGPTConfig(n_embd=a_			,						n_layer=a_			,						n_head=a_			)
       UpperCAmelCase__	:   Optional[int]												=	ImageGPTForCausalLM(a_			)

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

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


if __name__ == "__main__":
  UpperCamelCase_					=				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."
  )
  UpperCamelCase_					=				parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )







'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor