

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


_lowerCamelCase      :					Tuple					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  (					lowerCAmelCase__			):



       '''simple docstring'''







       lowercase_						=    ["""input_values""", """padding_mask"""]

       def __init__(							self ,   lowercase__ = 1 ,   lowercase__ = 2_4_0_0_0 ,   lowercase__ = 0.0 ,   lowercase__ = None ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(feature_size=snake_case_ ,   sampling_rate=snake_case_ ,   padding_value=snake_case_ ,   **snake_case_     )
             __A    =chunk_length_s
             __A    =overlap

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''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 ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   ):







             '''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
                   __A    =True

             __A    =bool(
                 isinstance(snake_case_ ,   (list, tuple)     ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)     ))     )

             if is_batched:
                   __A    =[np.asarray(snake_case_ ,   dtype=np.floataa     ).T for audio in raw_audio]
             elif not is_batched and not isinstance(snake_case_ ,   np.ndarray     ):
                   __A    =np.asarray(snake_case_ ,   dtype=np.floataa     )
             elif isinstance(snake_case_ ,   np.ndarray     ) and raw_audio.dtype is np.dtype(np.floataa     ):
                   __A    =raw_audio.astype(np.floataa     )

             # always return batch
             if not is_batched:
                   __A    =[np.asarray(snake_case_     ).T]

             # verify inputs are valid
             for idx, example in enumerate(snake_case_     ):
                   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'''     )

             __A    =None
             __A    =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:
                         __A    =min(array.shape[0] for array in raw_audio     )
                         __A    =int(np.floor(max_length / self.chunk_stride     )     )
                         __A    =(nb_step - 1) * self.chunk_stride + self.chunk_length
                   elif padding:
                         __A    =max(array.shape[0] for array in raw_audio     )
                         __A    =int(np.ceil(max_length / self.chunk_stride     )     )
                         __A    =(nb_step - 1) * self.chunk_stride + self.chunk_length
                         __A    ='''max_length'''
                   else:
                         __A    =input_values

        # normal padding on batch
             if padded_inputs is None:
                   __A    =self.pad(
                       snake_case_ ,   max_length=snake_case_ ,   truncation=snake_case_ ,   padding=snake_case_ ,   return_attention_mask=snake_case_ ,   )
                   if padding:
                         __A    =padded_inputs.pop('''attention_mask'''     )

             __A    =[]
             for example in padded_inputs.pop('''input_values'''     ):
                   if self.feature_size == 1:
                         __A    =example[..., None]
                   input_values.append(example.T     )

             __A    =input_values
             if return_tensors is not None:
                   __A    =padded_inputs.convert_to_tensors(snake_case_     )

             return padded_inputs






from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


