



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__snake_case       :	Union[str, Any]            =logging.get_logger(__name__)



class  lowerCamelCase__    (							_SCREAMING_SNAKE_CASE):




  '''simple docstring'''



  snake_case_			=["input_values", "padding_mask"]



  def __init__(self	,__lowerCamelCase = 1	,__lowerCamelCase = 2_40_00	,__lowerCamelCase = 0.0	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    Union[str, Any]:

      """simple docstring"""






      super().__init__(feature_size=A_	,sampling_rate=A_	,padding_value=A_	,**A_				)
      lowerCAmelCase__  :       Any				=  chunk_length_s
      lowerCAmelCase__  :       List[Any]				=  overlap



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      if self.chunk_length_s is None:
          return None
      else:
          return int(self.chunk_length_s * self.sampling_rate				)



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      if self.chunk_length_s is None or self.overlap is None:
          return None
      else:
          return max(1	,int((1.0 - self.overlap) * self.chunk_length				)				)



  def __call__(self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = False	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,)  ->    BatchFeature:

      """simple docstring"""






      if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
              raise ValueError(
                  f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                  f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                  f""" {self.sampling_rate} and not {sampling_rate}."""				)
      else:
          logger.warning(
              '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
              '''Failing to do so can result in silent errors that might be hard to debug.'''				)

      if padding and truncation:
          raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''				)
      elif padding is None:
          # by default let's pad the inputs
          lowerCAmelCase__  :       Tuple				=  True

      lowerCAmelCase__  :       Union[str, Any]				=  bool(
          isinstance(A_	,(list, tuple)				) and (isinstance(raw_audio[0]	,(np.ndarray, tuple, list)				))				)

      if is_batched:
          lowerCAmelCase__  :       Any				=  [np.asarray(A_	,dtype=np.floataa				).T for audio in raw_audio]
      elif not is_batched and not isinstance(A_	,np.ndarray				):
          lowerCAmelCase__  :       Dict				=  np.asarray(A_	,dtype=np.floataa				)
      elif isinstance(A_	,np.ndarray				) and raw_audio.dtype is np.dtype(np.floataa				):
          lowerCAmelCase__  :       str				=  raw_audio.astype(np.floataa				)

      # always return batch
      if not is_batched:
          lowerCAmelCase__  :       Any				=  [np.asarray(A_				).T]

      # verify inputs are valid
      for idx, example in enumerate(A_				):
          if example.ndim > 2:
              raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""				)
          if self.feature_size == 1 and example.ndim != 1:
              raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""				)
          if self.feature_size == 2 and example.shape[-1] != 2:
              raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""				)

      lowerCAmelCase__  :       List[str]				=  None
      lowerCAmelCase__  :       int				=  BatchFeature({'''input_values''': raw_audio}				)
      if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
          if truncation:
              lowerCAmelCase__  :       Optional[Any]				=  min(array.shape[0] for array in raw_audio				)
              lowerCAmelCase__  :       Tuple				=  int(np.floor(max_length / self.chunk_stride				)				)
              lowerCAmelCase__  :       str				=  (nb_step - 1) * self.chunk_stride + self.chunk_length
          elif padding:
              lowerCAmelCase__  :       Optional[int]				=  max(array.shape[0] for array in raw_audio				)
              lowerCAmelCase__  :       Union[str, Any]				=  int(np.ceil(max_length / self.chunk_stride				)				)
              lowerCAmelCase__  :       List[Any]				=  (nb_step - 1) * self.chunk_stride + self.chunk_length
              lowerCAmelCase__  :       Tuple				=  '''max_length'''
          else:
              lowerCAmelCase__  :       List[str]				=  input_values

        # normal padding on batch
      if padded_inputs is None:
          lowerCAmelCase__  :       List[Any]				=  self.pad(
              A_	,max_length=A_	,truncation=A_	,padding=A_	,return_attention_mask=A_	,)
          if padding:
              lowerCAmelCase__  :       Dict				=  padded_inputs.pop('''attention_mask'''				)

      lowerCAmelCase__  :       List[Any]				=  []
      for example in padded_inputs.pop('''input_values'''				):
          if self.feature_size == 1:
              lowerCAmelCase__  :       Optional[int]				=  example[..., None]
          input_values.append(example.T				)

      lowerCAmelCase__  :       List[Any]				=  input_values
      if return_tensors is not None:
          lowerCAmelCase__  :       str				=  padded_inputs.convert_to_tensors(A_				)

      return padded_inputs






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	Any            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""encoder-decoder"""
		snake_case_			=True



		def __init__(self	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						lowerCAmelCase__  :       Optional[Any]				=  kwargs.pop('''encoder'''				)
						lowerCAmelCase__  :       Any				=  encoder_config.pop('''model_type'''				)
						lowerCAmelCase__  :       str				=  kwargs.pop('''decoder'''				)
						lowerCAmelCase__  :       Tuple				=  decoder_config.pop('''model_type'''				)

						from ..auto.configuration_auto import AutoConfig

						lowerCAmelCase__  :       Tuple				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  True



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase				)  ->    PretrainedConfig:

						"""simple docstring"""






						logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
						lowerCAmelCase__  :       Optional[int]				=  True
						lowerCAmelCase__  :       List[str]				=  True

						return cls(encoder=encoder_config.to_dict()	,decoder=decoder_config.to_dict()	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  copy.deepcopy(self.__dict__				)
						lowerCAmelCase__  :       Optional[Any]				=  self.encoder.to_dict()
						lowerCAmelCase__  :       str				=  self.decoder.to_dict()
						lowerCAmelCase__  :       Optional[int]				=  self.__class__.model_type
						return output

