





'''simple docstring'''

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''EncodecFeatureExtractor'''
    UpperCamelCase     = ('''T5Tokenizer''', '''T5TokenizerFast''')


    def __init__(		self ,		A ,		A       )		->   Tuple:
        super().__init__(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			self.feature_extractor
        _SCREAMING_SNAKE_CASE									=			False


    def 			snake_case_(		self ,		A=None ,		A=None ,		A=True       )		->   Dict:
        return self.tokenizer.get_decoder_prompt_ids(task=A ,		language=A ,		no_timestamps=A       )


    def __call__(		self ,		*A ,		**A       )		->   Optional[Any]:
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""audio""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""sampling_rate""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text""" ,		A       )
        if len(A       ) > 0:
            _SCREAMING_SNAKE_CASE									=			args[0]
            _SCREAMING_SNAKE_CASE									=			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 text is not None:
            _SCREAMING_SNAKE_CASE									=			self.tokenizer(A ,		**A       )

        if audio is not None:
            _SCREAMING_SNAKE_CASE									=			self.feature_extractor(A ,		*A ,		sampling_rate=A ,		**A       )

        if audio is None:
            return inputs

        elif text is None:
            return audio_inputs

        else:
            _SCREAMING_SNAKE_CASE									=			audio_inputs["""input_values"""]
            if "padding_mask" in audio_inputs:
                _SCREAMING_SNAKE_CASE									=			audio_inputs["""padding_mask"""]
            return inputs


    def 			snake_case_(		self ,		*A ,		**A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""audio""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""padding_mask""" ,		A       )

        if len(A       ) > 0:
            _SCREAMING_SNAKE_CASE									=			args[0]
            _SCREAMING_SNAKE_CASE									=			args[1:]

        if audio_values is not None:
            return self._decode_audio(A ,		padding_mask=A       )
        else:
            return self.tokenizer.batch_decode(*A ,		**A       )


    def 			snake_case_(		self ,		*A ,		**A       )		->   Dict:
        return self.tokenizer.decode(*A ,		**A       )





    def 			snake_case_(		self ,		A ,		A = None       )		->   List[np.ndarray]:
        _SCREAMING_SNAKE_CASE									=			to_numpy(A       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			audio_values.shape

        if padding_mask is None:
            return list(A       )

        _SCREAMING_SNAKE_CASE									=			to_numpy(A       )

        # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
        # token (so that the generated audio values are **not** treated as padded tokens)
        _SCREAMING_SNAKE_CASE									=			seq_len - padding_mask.shape[-1]
        _SCREAMING_SNAKE_CASE									=			1 - self.feature_extractor.padding_value
        _SCREAMING_SNAKE_CASE									=			np.pad(A ,		((0, 0), (0, difference)) ,		"""constant""" ,		constant_values=A       )

        _SCREAMING_SNAKE_CASE									=			audio_values.tolist()
        for i in range(A       ):
            _SCREAMING_SNAKE_CASE									=			np.asarray(audio_values[i]       )[
                padding_mask[i][None, :] != self.feature_extractor.padding_value
            ]
            _SCREAMING_SNAKE_CASE									=			sliced_audio.reshape(A ,		-1       )

        return audio_values










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
     )






