





import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_UpperCAmelCase										=					set()
_UpperCAmelCase										=					get_logger(__name__)




def 			lowerCAmelCase_			(				UpperCamelCase_ = None	)  ->		Tuple:



       def decorator(UpperCamelCase_	):
              global _emitted_deprecation_warnings

              if inspect.isclass(__A	):
                     UpperCamelCase_ 							=	deprecated_class_or_function.__init__
                     UpperCamelCase_ 							=	deprecated_class_or_function.__name__
              else:
                     UpperCamelCase_ 							=	deprecated_class_or_function
                     UpperCamelCase_ 							=	deprecated_function.__name__
                     # Support deprecating __init__ class method: class name instead
                     UpperCamelCase_ 							=	name if name != "__init__" else deprecated_function.__qualname__.split("."	)[-2]

              UpperCamelCase_ 							=	(
                  F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
                  if help_message
                  else ""
              )

              @wraps(__A	)
              def wrapper(*UpperCamelCase_       ,		**UpperCamelCase_	):
                     UpperCamelCase_ 							=	hash(__A	)
                     if func_hash not in _emitted_deprecation_warnings:
                            warnings.warn(__A       ,		category=__A       ,		stacklevel=2	)
                            _emitted_deprecation_warnings.add(__A	)
                     return deprecated_function(*__A       ,		**__A	)

              UpperCamelCase_ 							=	"deprecated"

              if inspect.isclass(__A	):
                     UpperCamelCase_ 							=	wrapper
                     return deprecated_class_or_function
              else:
                     return wrapper

       return decorator



class 				_UpperCamelCase       (						enum.EnumMeta  ):

       def __getattribute__(		cls:			List[Any]       , _SCREAMING_SNAKE_CASE:			str		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	super().__getattribute__(_SCREAMING_SNAKE_CASE		)
              if isinstance(_SCREAMING_SNAKE_CASE       , enum.Enum		) and obj._on_access:
                     obj._on_access()
              return obj

       def __getitem__(		cls:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	super().__getitem__(_SCREAMING_SNAKE_CASE		)
              if member._on_access:
                     member._on_access()
              return member



       def __call__(		cls:			Dict       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , *, _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Optional[int]=None       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	super().__call__(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , module=_SCREAMING_SNAKE_CASE       , qualname=_SCREAMING_SNAKE_CASE       , type=_SCREAMING_SNAKE_CASE       , start=_SCREAMING_SNAKE_CASE		)
              if isinstance(_SCREAMING_SNAKE_CASE       , enum.Enum		) and obj._on_access:
                     obj._on_access()
              return obj



class 				_UpperCamelCase       (						enum.Enum	,    metaclass=a__  ):

       def __new__(		cls:			int       , _SCREAMING_SNAKE_CASE:			Any		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	object.__new__(cls		)
              UpperCamelCase_ 							=	value
              UpperCamelCase_ 							=	member.deprecate
              return member

       @property
       def lowercase			(		self:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              return ""



       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	f''' {self.help_message}''' if self.help_message else ""
              warnings.warn(
                  f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
                  + help_message       , _SCREAMING_SNAKE_CASE       , stacklevel=3       , )





'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





