


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 						_lowercase       (     __lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Optional[Any]			)  ->   Dict:





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
     UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__							:  List[str]								=     MODELS[model_size]  # set model hyperparameters
     UpperCamelCase__							:  List[str]								=     ImageGPTConfig(n_embd=__lowerCamelCase		,n_layer=__lowerCamelCase		,n_head=__lowerCamelCase			)
     UpperCamelCase__							:  str								=     ImageGPTForCausalLM(__lowerCamelCase			)

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

     # Save pytorch-model
     UpperCamelCase__							:  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()		,__lowerCamelCase			)
     print(F'Save configuration file to {pytorch_config_dump_path}'			)
     with open(__lowerCamelCase		,'''w'''		,encoding='''utf-8'''			) as f:
          f.write(config.to_json_string()			)


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








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




