code: 
  
  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        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any] ,a__						:      Union[str, Any] ,a__						:      Dict					)				->							Any:
  	# Construct configuration depending on size
  	__A   : Any								= {"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
  	__A   : Optional[Any]								= MODELS[model_size]  # set model hyperparameters
  	__A   : List[Any]								= ImageGPTConfig(n_embd=a__ ,n_layer=a__ ,n_head=a__					)
  	__A   : Optional[Any]								= ImageGPTForCausalLM(a__					)
  
  	# Load weights from numpy
  	load_tf_weights_in_imagegpt(a__ ,a__ ,a__					)
  
  	# Save pytorch-model
  	__A   : Any								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  	__A   : List[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_       :				Dict		   =      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_       :				Optional[int]		   =      parser.parse_args()
  					convert_imagegpt_checkpoint_to_pytorch(
  					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  					)
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
      '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				List[str]		   =      [
  			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''ProphetNetDecoder''',
  			    '''ProphetNetEncoder''',
  			    '''ProphetNetForCausalLM''',
  			    '''ProphetNetForConditionalGeneration''',
  			    '''ProphetNetModel''',
  			    '''ProphetNetPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  			from .tokenization_prophetnet import ProphetNetTokenizer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_prophetnet import (
  						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    ProphetNetDecoder,
  						    ProphetNetEncoder,
  						    ProphetNetForCausalLM,
  						    ProphetNetForConditionalGeneration,
  						    ProphetNetModel,
  						    ProphetNetPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  