code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  _lowerCAmelCase       :      Optional[Any]							=							logging.get_logger(__name__)
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        def __init__(  self  :			Tuple  ,			A  :			Optional[int]  ,			A  :			List[str]=None		):
             _UpperCAmelCase    :			Union[str, Any]              =					language
             _UpperCAmelCase    :			Dict              =					train_language
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			Tuple		):
             _UpperCAmelCase    :			Any              =					self.language if self.train_language is None else self.train_language
             _UpperCAmelCase    :			str              =					self._read_tsv(os.path.join(A  ,			f'XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv'		)		)
             _UpperCAmelCase    :			List[str]              =					[]
             for i, line in enumerate(A		):
                  if i == 0:
                       continue
                  _UpperCAmelCase    :			List[str]              =					f'train-{i}'
                  _UpperCAmelCase    :			Optional[int]              =					line[0]
                  _UpperCAmelCase    :			Dict              =					line[1]
                  _UpperCAmelCase    :			Union[str, Any]              =					"contradiction" if line[2] == "contradictory" else line[2]
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training input {text_a} is not a string'		)
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training input {text_b} is not a string'		)
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training label {label} is not a string'		)
                  examples.append(InputExample(guid=A  ,			text_a=A  ,			text_b=A  ,			label=A		)		)
             return examples
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			Union[str, Any]		):
             _UpperCAmelCase    :			Tuple              =					self._read_tsv(os.path.join(A  ,			"XNLI-1.0/xnli.test.tsv"		)		)
             _UpperCAmelCase    :			Optional[int]              =					[]
             for i, line in enumerate(A		):
                  if i == 0:
                       continue
                  _UpperCAmelCase    :			str              =					line[0]
                  if language != self.language:
                       continue
                  _UpperCAmelCase    :			List[Any]              =					f'test-{i}'
                  _UpperCAmelCase    :			Any              =					line[6]
                  _UpperCAmelCase    :			Dict              =					line[7]
                  _UpperCAmelCase    :			Dict              =					line[1]
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training input {text_a} is not a string'		)
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training input {text_b} is not a string'		)
                  if not isinstance(A  ,			A		):
                       raise ValueError(f'Training label {label} is not a string'		)
                  examples.append(InputExample(guid=A  ,			text_a=A  ,			text_b=A  ,			label=A		)		)
             return examples
  
  
  
        def 		snake_case_      (  self  :			str		):
             return ["contradiction", "entailment", "neutral"]
  
  
  
  
  
  
  
  _lowerCAmelCase       :      Any							=							{
      "xnli": XnliProcessor,
  }
  
  _lowerCAmelCase       :      Tuple							=							{
      "xnli": "classification",
  }
  
  _lowerCAmelCase       :      Any							=							{
      "xnli": 3,
  }
  
style_context: 
  
  
  
  """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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )
  
       # Load weights from numpy
       load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       # Save pytorch-model
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
       print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
       torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
       print(f'Save configuration file to {pytorch_config_dump_path}'       )
       with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
            f.write(config.to_json_string()       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      List[str]							=							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."
   )
   _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )
  