




'''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 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Optional[Any]	)->   Optional[int]:




     '''simple docstring'''





     __snake_case    					=  {'''small''': (5_12, 8, 24), '''medium''': (10_24, 8, 36), '''large''': (15_36, 16, 48)}
     __snake_case					,    __snake_case					,    __snake_case    					=  MODELS[model_size]  # set model hyperparameters
     __snake_case    					=  ImageGPTConfig(n_embd=_lowerCamelCase   ,	n_layer=_lowerCamelCase   ,	n_head=_lowerCamelCase	)
     __snake_case    					=  ImageGPTForCausalLM(_lowerCamelCase	)

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

     # Save pytorch-model
     __snake_case    					=  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     __snake_case    					=  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__":
     UpperCAmelCase_       :  Tuple      =		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_       :  List[str]      =		parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


UpperCAmelCase_       :  int      =		logging.get_logger(__name__)





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  None:



            '''simple docstring'''




            warnings.warn(
                '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MaskFormerImageProcessor instead.'''    ,  __SCREAMING_SNAKE_CASE    ,  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






