




from ...processing_utils import ProcessorMixin


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''SpeechT5FeatureExtractor'''
    UpperCAmelCase__							  =      '''SpeechT5Tokenizer'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__					, UpperCAmelCase__)


    def __call__(			self      :	Dict					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    kwargs.pop('''audio'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''audio_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''sampling_rate'''					, UpperCAmelCase__)

           if audio is not None and text is not None:
                  raise ValueError(
                      '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''')
           if audio_target is not None and text_target is not None:
                  raise ValueError(
                      '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''')
           if audio is None and audio_target is None and text is None and text_target is None:
                  raise ValueError(
                      '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''')

           if audio is not None:
                  A__       =    self.feature_extractor(UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
           elif text is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if audio_target is not None:
                  A__       =    self.feature_extractor(audio_target=UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_values''']
           elif text_target is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_ids''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    kwargs.pop('''input_values'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''input_ids'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''labels'''					, UpperCAmelCase__)

           if input_values is not None and input_ids is not None:
                  raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''')
           if input_values is None and input_ids is None and labels is None:
                  raise ValueError(
                      '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''')

           if input_values is not None:
                  A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
           elif input_ids is not None:
                  A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if labels is not None:
                  if "input_ids" in labels or (isinstance(UpperCAmelCase__					, UpperCAmelCase__) and "input_ids" in labels[0]):
                         A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    targets['''input_ids''']
                  else:
                         A__       =    self.feature_extractor.feature_size
                         A__       =    self.feature_extractor.num_mel_bins
                         A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    feature_size_hack
                         A__       =    targets['''input_values''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->Dict:





           '''simple docstring'''

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









import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCAmelCase	:	int		     =       {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE						)

  a__	:					Dict										=finetuning_task.lower() if finetuning_task is not None else ""
  if finetuning_task in GLUE_TASKS_NUM_LABELS:
    print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
    a__	:					List[str]										=finetuning_task
    a__	:					Tuple										=GLUE_TASKS_NUM_LABELS[finetuning_task]
    a__	:					List[Any]										=XLNetForSequenceClassification(SCREAMING_SNAKE_CASE						)
  elif "squad" in finetuning_task:
    a__	:					Optional[int]										=finetuning_task
    a__	:					Dict										=XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					List[Any]										=XLNetLMHeadModel(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  torch.save(model.state_dict()       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  with open(SCREAMING_SNAKE_CASE       ,       "w"       ,       encoding="utf-8"						) as f:
    f.write(config.to_json_string()						)


if __name__ == "__main__":
     UpperCAmelCase	:	List[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(
         """--xlnet_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained XLNet model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
     )
     parser.add_argument(
         """--finetuning_task""",
         default=None,
         type=str,
         help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
     )
     UpperCAmelCase	:	int		     =       parser.parse_args()
     print(args)

     convert_xlnet_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
     )






