


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()

def 				lowercase_		(		_A      :   Dict   , _A      :   Dict   , _A      :   List[str]						):

      """simple docstring"""




      lowerCamelCase__		:    Dict   =  ConvBertConfig.from_json_file(_A						)
      lowerCamelCase__		:    Tuple   =  ConvBertModel(_A						)

      lowerCamelCase__		:    Dict   =  load_tf_weights_in_convbert(_A   , _A   , _A						)
      model.save_pretrained(_A						)

      lowerCamelCase__		:    Optional[int]   =  TFConvBertModel.from_pretrained(_A   , from_pt=_A						)
      tf_model.save_pretrained(_A						)


if __name__ == "__main__":
    A				:   Optional[int]		= 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(
        "--convbert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ConvBERT 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				:   Union[str, Any]		= parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "AutoFeatureExtractor"
       A__ 						=      "Speech2Text2Tokenizer"





       def __init__(       self							:      List[str] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             super().__init__(__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  self.feature_extractor
             lowerCamelCase__		:    Optional[int]   =  False





       def __call__(       self							:      List[str] ,				*__lowerCamelCase							:      Union[str, Any] ,				**__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             if self._in_target_context_manager:
                   return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase	)

             if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
                   lowerCamelCase__		:    List[Any]   =  kwargs.pop("raw_speech"	)
             else:
                   lowerCamelCase__		:    str   =  kwargs.pop("audio" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  kwargs.pop("sampling_rate" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  kwargs.pop("text" ,				__lowerCamelCase	)
             if len(__lowerCamelCase	) > 0:
                   lowerCamelCase__		:    Dict   =  args[0]
                   lowerCamelCase__		:    int   =  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:
                   lowerCamelCase__		:    str   =  self.feature_extractor(__lowerCamelCase ,				*__lowerCamelCase ,				sampling_rate=__lowerCamelCase ,				**__lowerCamelCase	)
             if text is not None:
                   lowerCamelCase__		:    Optional[int]   =  self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase	)

             if text is None:
                   return inputs
             elif audio is None:
                   return encodings
             else:
                   lowerCamelCase__		:    Optional[int]   =  encodings["input_ids"]
                   return inputs





       def 						lowerCAmelCase					(       self							:      Any ,				*__lowerCamelCase							:      Any ,				**__lowerCamelCase							:      Any	):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict ,				*__lowerCamelCase							:      int ,				**__lowerCamelCase							:      List[str]	):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       @contextmanager
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             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."	)
             lowerCamelCase__		:    int   =  True
             lowerCamelCase__		:    List[Any]   =  self.tokenizer
             yield
             lowerCamelCase__		:    Optional[int]   =  self.feature_extractor
             lowerCamelCase__		:    List[str]   =  False


