


'''simple docstring'''







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

from .logging import get_logger


a__    :   int								=			set()
a__    :   List[str]								=			get_logger(__name__)







def _UpperCamelCase			(	__A = None       )				->						Tuple:


      '''simple docstring'''



      def decorator(__A       ):
            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(*__A ,     **__A       ):
                  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    lowercase_	(       enum.EnumMeta  ):


    def __getattribute__(	cls    ,   a       ):
          UpperCamelCase__      =							super().__getattribute__(a       )
          if isinstance(a    ,   enum.Enum       ) and obj._on_access:
                obj._on_access()
          return obj


    def __getitem__(	cls    ,   a       ):
          UpperCamelCase__      =							super().__getitem__(a       )
          if member._on_access:
                member._on_access()
          return member


    def __call__(	cls    ,   a    ,   a=None    ,   *, a=None    ,   a=None    ,   a=None    ,   a=1       ):
          UpperCamelCase__      =							super().__call__(a    ,   a    ,   module=a    ,   qualname=a    ,   type=a    ,   start=a       )
          if isinstance(a    ,   enum.Enum       ) and obj._on_access:
                obj._on_access()
          return obj

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


    def __new__(	cls    ,   a       ):
          UpperCamelCase__      =							object.__new__(cls       )
          UpperCamelCase__      =							value
          UpperCamelCase__      =							member.deprecate
          return member


    @property
    def   __a				(	self       ):
          return ""


    def   __a				(	self       ):
          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    ,   a    ,   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)





