




'''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'''



from transformers import ProcessorMixin





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			'''AutoFeatureExtractor'''
       __lowercase  :      List[Any]        =			'''AutoTokenizer'''






