



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	['input_features', 'is_longer']







							def __init__(     self,    __a=64,    __a=4_8000,    __a=480,    __a=10,    __a=1024,    __a=0.0,    __a=False,    __a = 0,    __a = 1_4000,    __a = None,    __a = "fusion",    __a = "repeatpad",    **__a,    ):

														'''simple docstring'''

														super().__init__(
														    feature_size=__a,    sampling_rate=__a,    padding_value=__a,    return_attention_mask=__a,    **__a,    )
														_lowerCAmelCase  :							List[str]				     =					top_db
														_lowerCAmelCase  :							Dict				     =					truncation
														_lowerCAmelCase  :							Dict				     =					padding
														_lowerCAmelCase  :							List[str]				     =					fft_window_size
														_lowerCAmelCase  :							int				     =					(fft_window_size >> 1) + 1
														_lowerCAmelCase  :							Any				     =					hop_length
														_lowerCAmelCase  :							Any				     =					max_length_s
														_lowerCAmelCase  :							int				     =					max_length_s * sampling_rate
														_lowerCAmelCase  :							Tuple				     =					sampling_rate
														_lowerCAmelCase  :							Dict				     =					frequency_min
														_lowerCAmelCase  :							int				     =					frequency_max
														_lowerCAmelCase  :							int				     =					mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins,    num_mel_filters=__a,    min_frequency=__a,    max_frequency=__a,    sampling_rate=__a,    norm=__a,    mel_scale="htk",    )
														_lowerCAmelCase  :							Optional[int]				     =					mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins,    num_mel_filters=__a,    min_frequency=__a,    max_frequency=__a,    sampling_rate=__a,    norm="slaney",    mel_scale="slaney",    )







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							int				     =					self.__class__.__name__
														if "mel_filters" in output:
																					del output["mel_filters"]
														if "mel_filters_slaney" in output:
																					del output["mel_filters_slaney"]
														return output







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					spectrogram(
														    __a,    window_function(self.fft_window_size,    "hann"),    frame_length=self.fft_window_size,    hop_length=self.hop_length,    power=2.0,    mel_filters=__a,    log_mel="dB",    )
														return log_mel_spectrogram.T







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					np.array_split(list(range(0,    total_frames - chunk_frames + 1)),    3)
														if len(ranges[1]) == 0:
																					# if the audio is too short, we just use the first chunk
																					_lowerCAmelCase  :							List[Any]				     =					[0]
														if len(ranges[2]) == 0:
																					# if the audio is too short, we just use the first chunk
																					_lowerCAmelCase  :							List[Any]				     =					[0]
														# randomly choose index for each part
														_lowerCAmelCase  :							str				     =					np.random.choice(ranges[0])
														_lowerCAmelCase  :							List[Any]				     =					np.random.choice(ranges[1])
														_lowerCAmelCase  :							Tuple				     =					np.random.choice(ranges[2])

														_lowerCAmelCase  :							Dict				     =					mel[idx_front : idx_front + chunk_frames, :]
														_lowerCAmelCase  :							Tuple				     =					mel[idx_middle : idx_middle + chunk_frames, :]
														_lowerCAmelCase  :							List[str]				     =					mel[idx_back : idx_back + chunk_frames, :]

														_lowerCAmelCase  :							Dict				     =					torch.tensor(mel[None, None, :])
														_lowerCAmelCase  :							Optional[Any]				     =					torch.nn.functional.interpolate(
														    __a,    size=[chunk_frames, 64],    mode="bilinear",    align_corners=__a)
														_lowerCAmelCase  :							Tuple				     =					mel_shrink[0][0].numpy()
														_lowerCAmelCase  :							Dict				     =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],    axis=0)
														return mel_fusion







							def      snake_case__   (     self,    __a,    __a,    __a,    __a):

														'''simple docstring'''

														if waveform.shape[0] > max_length:
																					if truncation == "rand_trunc":
																												_lowerCAmelCase  :							Dict				     =					True
																												# random crop to max_length (for compatibility) -> this should be handled by self.pad
																												_lowerCAmelCase  :							List[str]				     =					len(__a) - max_length
																												_lowerCAmelCase  :							Union[str, Any]				     =					np.random.randint(0,    overflow + 1)
																												_lowerCAmelCase  :							str				     =					waveform[idx : idx + max_length]
																												_lowerCAmelCase  :							Any				     =					self._np_extract_fbank_features(__a,    self.mel_filters_slaney)[None, :]
																					elif truncation == "fusion":
																												_lowerCAmelCase  :							List[Any]				     =					self._np_extract_fbank_features(__a,    self.mel_filters)
																												_lowerCAmelCase  :							List[Any]				     =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																												_lowerCAmelCase  :							Union[str, Any]				     =					mel.shape[0]
																												if chunk_frames == total_frames:
																																			# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																			# In this case, we just use the whole audio.
																																			_lowerCAmelCase  :							int				     =					np.stack([mel, mel, mel, mel],    axis=0)
																																			_lowerCAmelCase  :							int				     =					False
																												else:
																																			_lowerCAmelCase  :							Any				     =					self._random_mel_fusion(__a,    __a,    __a)
																																			_lowerCAmelCase  :							str				     =					True
																					else:
																												raise NotImplementedError(f"data_truncating {truncation} not implemented")

														else:
																					_lowerCAmelCase  :							Dict				     =					False
																					# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																					if waveform.shape[0] < max_length:
																												if padding == "repeat":
																																			_lowerCAmelCase  :							Optional[Any]				     =					int(max_length / len(__a))
																																			_lowerCAmelCase  :							Dict				     =					np.stack(np.tile(__a,    n_repeat + 1))[:max_length]
																												if padding == "repeatpad":
																																			_lowerCAmelCase  :							Any				     =					int(max_length / len(__a))
																																			_lowerCAmelCase  :							Optional[Any]				     =					np.stack(np.tile(__a,    __a))
																												_lowerCAmelCase  :							Optional[int]				     =					np.pad(__a,    (0, max_length - waveform.shape[0]),    mode="constant",    constant_values=0)

																					if truncation == "fusion":
																												_lowerCAmelCase  :							Union[str, Any]				     =					self._np_extract_fbank_features(__a,    self.mel_filters)
																												_lowerCAmelCase  :							List[str]				     =					np.stack([input_mel, input_mel, input_mel, input_mel],    axis=0)
																					else:
																												_lowerCAmelCase  :							List[str]				     =					self._np_extract_fbank_features(__a,    self.mel_filters_slaney)[None, :]

														return input_mel, longer







							def __call__(     self,    __a,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					truncation if truncation is not None else self.truncation
														_lowerCAmelCase  :							Tuple				     =					padding if padding else self.padding

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
																												    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
																												    f" was sampled with {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.")

														_lowerCAmelCase  :							List[Any]				     =					isinstance(__a,    np.ndarray) and len(raw_speech.shape) > 1
														if is_batched_numpy and len(raw_speech.shape) > 2:
																					raise ValueError(f"Only mono-channel audio is supported for input to {self}")
														_lowerCAmelCase  :							str				     =					is_batched_numpy or (
														    isinstance(__a,    (list, tuple)) and (isinstance(raw_speech[0],    (np.ndarray, tuple, list)))
														)

														if is_batched:
																					_lowerCAmelCase  :							Optional[Any]				     =					[np.asarray(__a,    dtype=np.floataa) for speech in raw_speech]
														elif not is_batched and not isinstance(__a,    np.ndarray):
																					_lowerCAmelCase  :							List[Any]				     =					np.asarray(__a,    dtype=np.floataa)
														elif isinstance(__a,    np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
																					_lowerCAmelCase  :							Any				     =					raw_speech.astype(np.floataa)

														# always return batch
														if not is_batched:
																					_lowerCAmelCase  :							List[str]				     =					[np.asarray(__a)]

														# convert to mel spectrogram, truncate and pad if needed.
														_lowerCAmelCase  :							Any				     =					[
														    self._get_input_mel(__a,    max_length if max_length else self.nb_max_samples,    __a,    __a)
														    for waveform in raw_speech
														]

														_lowerCAmelCase  :							List[Any]				     =					[]
														_lowerCAmelCase  :							List[Any]				     =					[]
														for mel, longer in padded_inputs:
																					input_mel.append(__a)
																					is_longer.append(__a)

														if truncation == "fusion" and sum(__a) == 0:
																					# if no audio is longer than 10s, then randomly select one audio to be longer
																					_lowerCAmelCase  :							Tuple				     =					np.random.randint(0,    len(__a))
																					_lowerCAmelCase  :							Tuple				     =					True

														if isinstance(input_mel[0],    __a):
																					_lowerCAmelCase  :							Tuple				     =					[np.asarray(__a,    dtype=np.floataa) for feature in input_mel]

														# is_longer is a list of bool
														_lowerCAmelCase  :							str				     =					[[longer] for longer in is_longer]

														_lowerCAmelCase  :							str				     =					{"input_features": input_mel, "is_longer": is_longer}
														_lowerCAmelCase  :							Optional[int]				     =					BatchFeature(__a)

														if return_tensors is not None:
																					_lowerCAmelCase  :							Optional[int]				     =					input_features.convert_to_tensors(__a)

														return input_features





from __future__ import annotations

from collections.abc import MutableSequence



class 						UpperCAmelCase_					:







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														if len(__a) != degree + 1:
																					raise ValueError(
																					    "The number of coefficients should be equal to the degree + 1.")

														_lowerCAmelCase  :							list[float]				     =					list(__a)
														_lowerCAmelCase  :							Any				     =					degree







							def __add__(     self,    __a):

														'''simple docstring'''

														if self.degree > polynomial_a.degree:
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[:]
																					for i in range(polynomial_a.degree + 1):
																												coefficients[i] += polynomial_a.coefficients[i]
																					return Polynomial(self.degree,    __a)
														else:
																					_lowerCAmelCase  :							Union[str, Any]				     =					polynomial_a.coefficients[:]
																					for i in range(self.degree + 1):
																												coefficients[i] += self.coefficients[i]
																					return Polynomial(polynomial_a.degree,    __a)







							def __sub__(     self,    __a):

														'''simple docstring'''

														return self + polynomial_a * Polynomial(0,    [-1])







							def __neg__(     self):

														'''simple docstring'''

														return Polynomial(self.degree,    [-c for c in self.coefficients])







							def __mul__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + polynomial_a.degree + 1)
														for i in range(self.degree + 1):
																					for j in range(polynomial_a.degree + 1):
																												coefficients[i + j] += (
																												    self.coefficients[i] * polynomial_a.coefficients[j]
																												)

														return Polynomial(self.degree + polynomial_a.degree,    __a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int | float				     =					0
														for i in range(self.degree + 1):
																					result += self.coefficients[i] * (substitution**i)
														return result







							def __str__(     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					""
														for i in range(self.degree,    -1,    -1):
																					if self.coefficients[i] == 0:
																												continue
																					elif self.coefficients[i] > 0:
																												if polynomial:
																																			polynomial += " + "
																					else:
																												polynomial += " - "

																					if i == 0:
																												polynomial += str(abs(self.coefficients[i]))
																					elif i == 1:
																												polynomial += str(abs(self.coefficients[i])) + "x"
																					else:
																												polynomial += str(abs(self.coefficients[i])) + "x^" + str(__a)

														return polynomial







							def __repr__(     self):

														'''simple docstring'''

														return self.__str__()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * self.degree
														for i in range(self.degree):
																					_lowerCAmelCase  :							List[Any]				     =					self.coefficients[i + 1] * (i + 1)
														return Polynomial(self.degree - 1,    __a)







							def      snake_case__   (     self,    __a = 0):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + 2)
														_lowerCAmelCase  :							Optional[Any]				     =					constant
														for i in range(self.degree + 1):
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[i] / (i + 1)
														return Polynomial(self.degree + 1,    __a)







							def __eq__(     self,    __a):

														'''simple docstring'''

														if not isinstance(__a,    __a):
																					return False

														if self.degree != polynomial_a.degree:
																					return False

														for i in range(self.degree + 1):
																					if self.coefficients[i] != polynomial_a.coefficients[i]:
																												return False

														return True







							def __ne__(     self,    __a):

														'''simple docstring'''

														return not self.__eq__(__a)
