



from typing import List, Optional

import numpy as np

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






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      "EncodecFeatureExtractor"
    __lowerCamelCase			: Any					=      ("T5Tokenizer", "T5TokenizerFast")






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)
        A :       Any          =   self.feature_extractor
        A :       int          =   False






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=True		):
        return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase__,		language=lowerCamelCase__,		no_timestamps=lowerCamelCase__		)






    def __call__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*lowerCamelCase__,		**lowerCamelCase__		)

        A :       Union[str, Any]          =   kwargs.pop("""audio""",		lowerCamelCase__		)
        A :       Any          =   kwargs.pop("""sampling_rate""",		lowerCamelCase__		)
        A :       Any          =   kwargs.pop("""text""",		lowerCamelCase__		)
        if len(lowerCamelCase__		) > 0:
            A :       List[Any]          =   args[0]
            A :       Any          =   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:
            A :       Optional[int]          =   self.tokenizer(lowerCamelCase__,		**lowerCamelCase__		)

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

        if audio is None:
            return inputs

        elif text is None:
            return audio_inputs

        else:
            A :       Optional[int]          =   audio_inputs["""input_values"""]
            if "padding_mask" in audio_inputs:
                A :       List[str]          =   audio_inputs["""padding_mask"""]
            return inputs






    def 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        A :       str          =   kwargs.pop("""audio""",		lowerCamelCase__		)
        A :       Optional[int]          =   kwargs.pop("""padding_mask""",		lowerCamelCase__		)

        if len(lowerCamelCase__		) > 0:
            A :       Any          =   args[0]
            A :       Optional[Any]          =   args[1:]

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






    def 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        return self.tokenizer.decode(*lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       List[Any]          =   to_numpy(lowerCamelCase__		)
        A     ,							A     ,							A :       Union[str, Any]          =   audio_values.shape

        if padding_mask is None:
            return list(lowerCamelCase__		)

        A :       Dict          =   to_numpy(lowerCamelCase__		)

        # 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)
        A :       Any          =   seq_len - padding_mask.shape[-1]
        A :       List[Any]          =   1 - self.feature_extractor.padding_value
        A :       Optional[int]          =   np.pad(lowerCamelCase__,		((0, 0), (0, difference)),		"""constant""",		constant_values=lowerCamelCase__		)

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

        return audio_values







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


