






"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE_      :  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_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--gpt2_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



