code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
  
  import datasets
  
  
  _lowerCAmelCase       :      Dict							=							"\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n"
  
  _lowerCAmelCase       :      Union[str, Any]							=							"\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n"
  
  
  _lowerCAmelCase       :      Optional[int]							=							"\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric(\"frugalscore\")\n    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])\n    >>> print([round(s, 3) for s in results[\"scores\"]])\n    [0.631, 0.645]\n"
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,    _KWARGS_DESCRIPTION		)
  class 			UpperCAmelCase_						(       datasets.Metric		):
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,			citation=_CITATION  ,			inputs_description=_KWARGS_DESCRIPTION  ,			features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"		),
                         "references": datasets.Value("string"		),
                     }		)  ,			homepage="https://github.com/moussaKam/FrugalScore"  ,			)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			Any		):
             if self.config_name == "default":
                  _UpperCAmelCase    :			int              =					"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                  _UpperCAmelCase    :			Any              =					self.config_name
             _UpperCAmelCase    :			Optional[Any]              =					AutoModelForSequenceClassification.from_pretrained(A		)
             _UpperCAmelCase    :			List[Any]              =					AutoTokenizer.from_pretrained(A		)
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			Optional[int]  ,			A  :			Optional[int]  ,			A  :			List[str]=3_2  ,			A  :			Optional[int]=1_2_8  ,			A  :			Tuple=None  ,			):
             assert len(A		) == len(
                 A		), "predictions and references should have the same number of sentences."
             if device is not None:
                  assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                  _UpperCAmelCase    :			Tuple              =					"gpu" if torch.cuda.is_available() else "cpu"
             _UpperCAmelCase    :			Tuple              =					TrainingArguments(
                 "trainer"  ,			fpaa=(device == "gpu")  ,			per_device_eval_batch_size=A  ,			report_to="all"  ,			no_cuda=(device == "cpu")  ,			log_level="warning"  ,			)
             _UpperCAmelCase    :			List[Any]              =					{"sentence1": predictions, "sentence2": references}
             _UpperCAmelCase    :			List[str]              =					datasets.Dataset.from_dict(A		)
  
             def tokenize_function(A  :			Any		):
                  return self.tokenizer(
                      data["sentence1"]  ,			data["sentence2"]  ,			max_length=A  ,			truncation=A  ,			padding=A		)
  
             _UpperCAmelCase    :			Optional[int]              =					raw_datasets.map(A  ,			batched=A		)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"]		)
             _UpperCAmelCase    :			Optional[int]              =					Trainer(self.model  ,			A  ,			tokenizer=self.tokenizer		)
             _UpperCAmelCase    :			Optional[int]              =					trainer.predict(A		)
             return {"scores": list(predictions.predictions.squeeze(-1		)		)}
  
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
   )
  