





'''simple docstring'''






from ...processing_utils import ProcessorMixin
class 					__snake_case  (					lowercase_							):






   '''simple docstring'''





   lowerCamelCase__    							=      """SpeechT5FeatureExtractor"""
   lowerCamelCase__    							=      """SpeechT5Tokenizer"""

   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    super().__init__(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def __call__(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					int    =  kwargs.pop("""audio"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  kwargs.pop("""text"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  kwargs.pop("""text_target"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  kwargs.pop("""audio_target"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  kwargs.pop("""sampling_rate"""	,		__SCREAMING_SNAKE_CASE       )

    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:
     snake_case__     :					Any    =  self.feature_extractor(__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		sampling_rate=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    elif text is not None:
     snake_case__     :					List[str]    =  self.tokenizer(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					List[str]    =  None

    if audio_target is not None:
     snake_case__     :					Dict    =  self.feature_extractor(audio_target=__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		sampling_rate=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
     snake_case__     :					List[Any]    =  targets["""input_values"""]
    elif text_target is not None:
     snake_case__     :					Optional[int]    =  self.tokenizer(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Union[str, Any]    =  targets["""input_ids"""]
    else:
     snake_case__     :					int    =  None

    if inputs is None:
     return targets

    if targets is not None:
     snake_case__     :					str    =  labels

     snake_case__     :					List[Any]    =  targets.get("""attention_mask"""       )
     if decoder_attention_mask is not None:
      snake_case__     :					Optional[int]    =  decoder_attention_mask

    return inputs

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					int    =  kwargs.pop("""input_values"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  kwargs.pop("""input_ids"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[Any]    =  kwargs.pop("""labels"""	,		__SCREAMING_SNAKE_CASE       )

    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:
     snake_case__     :					List[str]    =  self.feature_extractor.pad(__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    elif input_ids is not None:
     snake_case__     :					int    =  self.tokenizer.pad(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					Union[str, Any]    =  None

    if labels is not None:
     if "input_ids" in labels or (isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ) and "input_ids" in labels[0]):
      snake_case__     :					Optional[int]    =  self.tokenizer.pad(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[str]    =  targets["""input_ids"""]
     else:
      snake_case__     :					Optional[Any]    =  self.feature_extractor.feature_size
      snake_case__     :					List[Any]    =  self.feature_extractor.num_mel_bins
      snake_case__     :					Union[str, Any]    =  self.feature_extractor.pad(__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[str]    =  feature_size_hack
      snake_case__     :					Dict    =  targets["""input_values"""]
    else:
     snake_case__     :					Dict    =  None

    if inputs is None:
     return targets

    if targets is not None:
     snake_case__     :					int    =  labels

     snake_case__     :					str    =  targets.get("""attention_mask"""       )
     if decoder_attention_mask is not None:
      snake_case__     :					int    =  decoder_attention_mask

    return inputs

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )








'''simple docstring'''

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch
class     __lowerCAmelCase		(TensorFormatter[Mapping, """torch.Tensor""", Mapping]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		UpperCamelCase					) and column:
                    if all(
                        isinstance(UpperCamelCase  ,		torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                        for x in column					):
                           return torch.stack(UpperCamelCase					)
             return column



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		(str, bytes, type(UpperCamelCase					))					):
                    return value
             elif isinstance(UpperCamelCase  ,		(np.character, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.character					):
                    return value.tolist()

             lowercase__      =					{}

             if isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.integer					):
                    lowercase__      =					{'''dtype''': torch.intaa}
             elif isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.floating					):
                    lowercase__      =					{'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(UpperCamelCase  ,		PIL.Image.Image					):
                           lowercase__      =					np.asarray(UpperCamelCase					)
             return torch.tensor(UpperCamelCase  ,		**{**default_dtype, **self.torch_tensor_kwargs}					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

             # support for torch, tf, jax etc.
             if hasattr(UpperCamelCase  ,		'''__array__'''					) and not isinstance(UpperCamelCase  ,		torch.Tensor					):
                    lowercase__      =					data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(UpperCamelCase  ,		np.ndarray					):
                    if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                           return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             elif isinstance(UpperCamelCase  ,		(list, tuple)					):
                    return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             return self._tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







             return map_nested(self._recursive_tensorize  ,		UpperCamelCase  ,		map_list=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_column(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_column(UpperCamelCase  ,		pa_table.column_names[0]					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             lowercase__      =					self._consolidate(UpperCamelCase					)
             return column






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_batch(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_batch(UpperCamelCase					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             for column_name in batch:
                    lowercase__      =					self._consolidate(batch[column_name]					)
             return batch




