


'''simple docstring'''






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


__A :  int    							=				logging.get_logger(__name__)






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['input_values', 'padding_mask']

    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  int = 2_40_00   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  float = None   ,						lowerCamelCase   :  float = None   ,						**lowerCamelCase   :  Dict   ,						)       ->  Any:
      super().__init__(feature_size=lowerCamelCase   ,						sampling_rate=lowerCamelCase   ,						padding_value=lowerCamelCase   ,						**lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       chunk_length_s
      lowerCAmelCase_      :				Optional[int]      =       overlap

    @property
    def __lowercase       (       self   :  Dict		)       ->  Optional[int]:
      if self.chunk_length_s is None:
        return None
      else:
        return int(self.chunk_length_s * self.sampling_rate		)

    @property
    def __lowercase       (       self   :  Tuple		)       ->  Optional[int]:
      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   :  int   ,						lowerCamelCase   :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,						lowerCamelCase   :  Optional[Union[bool, str, PaddingStrategy]] = None   ,						lowerCamelCase   :  Optional[bool] = False   ,						lowerCamelCase   :  Optional[int] = None   ,						lowerCamelCase   :  Optional[Union[str, TensorType]] = None   ,						lowerCamelCase   :  Optional[int] = None   ,						)       ->  BatchFeature:
      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_      :				str      =       True

      lowerCAmelCase_      :				Any      =       bool(
          isinstance(lowerCamelCase   ,						(list, tuple)		) and (isinstance(raw_audio[0]   ,						(np.ndarray, tuple, list)		))		)

      if is_batched:
        lowerCAmelCase_      :				Tuple      =       [np.asarray(lowerCamelCase   ,						dtype=np.floataa		).T for audio in raw_audio]
      elif not is_batched and not isinstance(lowerCamelCase   ,						np.ndarray		):
        lowerCAmelCase_      :				Optional[Any]      =       np.asarray(lowerCamelCase   ,						dtype=np.floataa		)
      elif isinstance(lowerCamelCase   ,						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_      :				Union[str, Any]      =       [np.asarray(lowerCamelCase		).T]

      # verify inputs are valid
      for idx, example in enumerate(lowerCamelCase		):
        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_      :				Optional[Any]      =       None
      lowerCAmelCase_      :				Tuple      =       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[int]      =       min(array.shape[0] for array in raw_audio		)
          lowerCAmelCase_      :				int      =       int(np.floor(max_length / self.chunk_stride		)		)
          lowerCAmelCase_      :				Dict      =       (nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
          lowerCAmelCase_      :				Any      =       max(array.shape[0] for array in raw_audio		)
          lowerCAmelCase_      :				Tuple      =       int(np.ceil(max_length / self.chunk_stride		)		)
          lowerCAmelCase_      :				Optional[Any]      =       (nb_step - 1) * self.chunk_stride + self.chunk_length
          lowerCAmelCase_      :				str      =       """max_length"""
        else:
          lowerCAmelCase_      :				Dict      =       input_values

        # normal padding on batch
      if padded_inputs is None:
        lowerCAmelCase_      :				Dict      =       self.pad(
            lowerCamelCase   ,						max_length=lowerCamelCase   ,						truncation=lowerCamelCase   ,						padding=lowerCamelCase   ,						return_attention_mask=lowerCamelCase   ,						)
        if padding:
          lowerCAmelCase_      :				Tuple      =       padded_inputs.pop("""attention_mask"""		)

      lowerCAmelCase_      :				Optional[Any]      =       []
      for example in padded_inputs.pop("""input_values"""		):
        if self.feature_size == 1:
          lowerCAmelCase_      :				Dict      =       example[..., None]
        input_values.append(example.T		)

      lowerCAmelCase_      :				str      =       input_values
      if return_tensors is not None:
        lowerCAmelCase_      :				Any      =       padded_inputs.convert_to_tensors(lowerCamelCase		)

      return padded_inputs







'''simple docstring'''






from ..utils import DummyObject, requires_backends






class        __snake_case	(       metaclass=_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['note_seq']

    def __init__(       self   :  Tuple   ,						*lowerCamelCase   :  Optional[int]   ,						**lowerCamelCase   :  Tuple		)       ->  Any:
      requires_backends(self   ,						["""note_seq"""]		)

    @classmethod
    def __lowercase       (       cls   :  Optional[Any]   ,						*lowerCamelCase   :  List[Any]   ,						**lowerCamelCase   :  Dict		)       ->  List[Any]:
      requires_backends(cls   ,						["""note_seq"""]		)





    @classmethod
    def __lowercase       (       cls   :  Union[str, Any]   ,						*lowerCamelCase   :  Union[str, Any]   ,						**lowerCamelCase   :  List[Any]		)       ->  Optional[int]:
      requires_backends(cls   ,						["""note_seq"""]		)



