
"""simple docstring"""




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 		a__						(      a_						):
   __lowerCAmelCase            =				"""MCTCTFeatureExtractor"""
   __lowerCAmelCase            =				"""AutoTokenizer"""
   def __init__(			self	,    _a	,    _a				):
          super().__init__(_a	,    _a				)
          lowercase    :				Any       		=							self.feature_extractor
          lowercase    :				List[str]       		=							False
   def __call__(			self	,    *_a	,    **_a				):
          # For backward compatibility
          if self._in_target_context_manager:
                 return self.current_processor(*_a	,    **_a				)

          if "raw_speech" in kwargs:
                 warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
                 lowercase    :				List[Any]       		=							kwargs.pop("raw_speech"				)
          else:
                 lowercase    :				Tuple       		=							kwargs.pop("audio"	,    _a				)
          lowercase    :				Dict       		=							kwargs.pop("sampling_rate"	,    _a				)
          lowercase    :				int       		=							kwargs.pop("text"	,    _a				)
          if len(_a				) > 0:
                 lowercase    :				int       		=							args[0]
                 lowercase    :				Tuple       		=							args[1:]

          if audio is None and text is None:
                 raise ValueError("You need to specify either an `audio` or `text` input to process."				)

          if audio is not None:
                 lowercase    :				Union[str, Any]       		=							self.feature_extractor(_a	,    *_a	,    sampling_rate=_a	,    **_a				)
          if text is not None:
                 lowercase    :				Dict       		=							self.tokenizer(_a	,    **_a				)

          if text is None:
                 return inputs
          elif audio is None:
                 return encodings
          else:
                 lowercase    :				Optional[Any]       		=							encodings["input_ids"]
                 return inputs
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          return self.tokenizer.batch_decode(*_a	,    **_a				)
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          # For backward compatibility
          if self._in_target_context_manager:
                 return self.current_processor.pad(*_a	,    **_a				)

          lowercase    :				int       		=							kwargs.pop("input_features"	,    _a				)
          lowercase    :				Optional[int]       		=							kwargs.pop("labels"	,    _a				)
          if len(_a				) > 0:
                 lowercase    :				Optional[Any]       		=							args[0]
                 lowercase    :				Dict       		=							args[1:]

          if input_features is not None:
                 lowercase    :				Optional[Any]       		=							self.feature_extractor.pad(_a	,    *_a	,    **_a				)
          if labels is not None:
                 lowercase    :				Tuple       		=							self.tokenizer.pad(_a	,    **_a				)

          if labels is None:
                 return input_features
          elif input_features is None:
                 return labels
          else:
                 lowercase    :				Dict       		=							labels["input_ids"]
                 return input_features
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          return self.tokenizer.decode(*_a	,    **_a				)

   @contextmanager
   def 			__magic_name__			(			self				):
          warnings.warn(
              "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
              "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
              "your audio inputs, or in a separate call."				)
          lowercase    :				int       		=							True
          lowercase    :				Optional[Any]       		=							self.tokenizer
          yield
          lowercase    :				Optional[int]       		=							self.feature_extractor
          lowercase    :				Any       		=							False




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


