






'''simple docstring'''


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


lowerCamelCase_				: str    	=							logging.get_logger(__name__)




class    _SCREAMING_SNAKE_CASE							(		UpperCAmelCase_	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["input_features", "is_longer"]






			def __init__(   self							:						Optional[Any] ,      lowercase							:						int=6_4 ,      lowercase							:						Any=4_8_0_0_0 ,      lowercase							:						Optional[int]=4_8_0 ,      lowercase							:						Any=1_0 ,      lowercase							:						Optional[int]=1_0_2_4 ,      lowercase							:						str=0.0 ,      lowercase							:						List[str]=False ,      lowercase							:						float = 0 ,      lowercase							:						float = 1_4_0_0_0 ,      lowercase							:						int = None ,      lowercase							:						str = "fusion" ,      lowercase							:						str = "repeatpad" ,      **lowercase							:						List[Any] ,      )							->    Union[str, Any]:

									'''simple docstring'''







									super().__init__(
									    feature_size=_lowercase ,      sampling_rate=_lowercase ,      padding_value=_lowercase ,      return_attention_mask=_lowercase ,      **_lowercase ,      )
									UpperCamelCase__         = top_db
									UpperCamelCase__         = truncation
									UpperCamelCase__         = padding
									UpperCamelCase__         = fft_window_size
									UpperCamelCase__         = (fft_window_size >> 1) + 1
									UpperCamelCase__         = hop_length
									UpperCamelCase__         = max_length_s
									UpperCamelCase__         = max_length_s * sampling_rate
									UpperCamelCase__         = sampling_rate
									UpperCamelCase__         = frequency_min
									UpperCamelCase__         = frequency_max
									UpperCamelCase__         = mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins ,      num_mel_filters=_lowercase ,      min_frequency=_lowercase ,      max_frequency=_lowercase ,      sampling_rate=_lowercase ,      norm=_lowercase ,      mel_scale="""htk""" ,      )
									UpperCamelCase__         = mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins ,      num_mel_filters=_lowercase ,      min_frequency=_lowercase ,      max_frequency=_lowercase ,      sampling_rate=_lowercase ,      norm="""slaney""" ,      mel_scale="""slaney""" ,      )






			def 				A      (   self							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = copy.deepcopy(self.__dict__			)
									UpperCamelCase__         = 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 				A      (   self							:						List[str] ,      lowercase							:						np.array ,      lowercase							:						Optional[np.array] = None			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = spectrogram(
									    _lowercase ,      window_function(self.fft_window_size ,      """hann"""			) ,      frame_length=self.fft_window_size ,      hop_length=self.hop_length ,      power=2.0 ,      mel_filters=_lowercase ,      log_mel="""dB""" ,      )
									return log_mel_spectrogram.T






			def 				A      (   self							:						Tuple ,      lowercase							:						int ,      lowercase							:						List[str] ,      lowercase							:						str			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = 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
															UpperCamelCase__         = [0]
									if len(ranges[2]			) == 0:
															# if the audio is too short, we just use the first chunk
															UpperCamelCase__         = [0]
									# randomly choose index for each part
									UpperCamelCase__         = np.random.choice(ranges[0]			)
									UpperCamelCase__         = np.random.choice(ranges[1]			)
									UpperCamelCase__         = np.random.choice(ranges[2]			)

									UpperCamelCase__         = mel[idx_front : idx_front + chunk_frames, :]
									UpperCamelCase__         = mel[idx_middle : idx_middle + chunk_frames, :]
									UpperCamelCase__         = mel[idx_back : idx_back + chunk_frames, :]

									UpperCamelCase__         = torch.tensor(mel[None, None, :]			)
									UpperCamelCase__         = torch.nn.functional.interpolate(
									    _lowercase ,      size=[chunk_frames, 6_4] ,      mode="""bilinear""" ,      align_corners=_lowercase			)
									UpperCamelCase__         = mel_shrink[0][0].numpy()
									UpperCamelCase__         = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,      axis=0			)
									return mel_fusion






			def 				A      (   self							:						Any ,      lowercase							:						np.array ,      lowercase							:						Any ,      lowercase							:						List[str] ,      lowercase							:						List[Any]			)							->    List[Any]:

									'''simple docstring'''







									if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																					UpperCamelCase__         = True
																					# random crop to max_length (for compatibility) -> this should be handled by self.pad
																					UpperCamelCase__         = len(_lowercase			) - max_length
																					UpperCamelCase__         = np.random.randint(0 ,      overflow + 1			)
																					UpperCamelCase__         = waveform[idx : idx + max_length]
																					UpperCamelCase__         = self._np_extract_fbank_features(_lowercase ,      self.mel_filters_slaney			)[None, :]
															elif truncation == "fusion":
																					UpperCamelCase__         = self._np_extract_fbank_features(_lowercase ,      self.mel_filters			)
																					UpperCamelCase__         = max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																					UpperCamelCase__         = 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.
																											UpperCamelCase__         = np.stack([mel, mel, mel, mel] ,      axis=0			)
																											UpperCamelCase__         = False
																					else:
																											UpperCamelCase__         = self._random_mel_fusion(_lowercase ,      _lowercase ,      _lowercase			)
																											UpperCamelCase__         = True
															else:
																					raise NotImplementedError(f"data_truncating {truncation} not implemented"			)

									else:
															UpperCamelCase__         = 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":
																											UpperCamelCase__         = int(max_length / len(_lowercase			)			)
																											UpperCamelCase__         = np.stack(np.tile(_lowercase ,      n_repeat + 1			)			)[:max_length]
																					if padding == "repeatpad":
																											UpperCamelCase__         = int(max_length / len(_lowercase			)			)
																											UpperCamelCase__         = np.stack(np.tile(_lowercase ,      _lowercase			)			)
																					UpperCamelCase__         = np.pad(_lowercase ,      (0, max_length - waveform.shape[0]) ,      mode="""constant""" ,      constant_values=0			)

															if truncation == "fusion":
																					UpperCamelCase__         = self._np_extract_fbank_features(_lowercase ,      self.mel_filters			)
																					UpperCamelCase__         = np.stack([input_mel, input_mel, input_mel, input_mel] ,      axis=0			)
															else:
																					UpperCamelCase__         = self._np_extract_fbank_features(_lowercase ,      self.mel_filters_slaney			)[None, :]

									return input_mel, longer







			def __call__(   self							:						List[str] ,      lowercase							:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowercase							:						str = None ,      lowercase							:						Optional[str] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Union[str, TensorType]] = None ,      **lowercase							:						str ,      )							->    str:

									'''simple docstring'''







									UpperCamelCase__         = truncation if truncation is not None else self.truncation
									UpperCamelCase__         = 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."""			)

									UpperCamelCase__         = isinstance(_lowercase ,      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}"			)
									UpperCamelCase__         = is_batched_numpy or (
									    isinstance(_lowercase ,      (list, tuple)			) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)			))
									)

									if is_batched:
															UpperCamelCase__         = [np.asarray(_lowercase ,      dtype=np.floataa			) for speech in raw_speech]
									elif not is_batched and not isinstance(_lowercase ,      np.ndarray			):
															UpperCamelCase__         = np.asarray(_lowercase ,      dtype=np.floataa			)
									elif isinstance(_lowercase ,      np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
															UpperCamelCase__         = raw_speech.astype(np.floataa			)

									# always return batch
									if not is_batched:
															UpperCamelCase__         = [np.asarray(_lowercase			)]

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

									UpperCamelCase__         = []
									UpperCamelCase__         = []
									for mel, longer in padded_inputs:
															input_mel.append(_lowercase			)
															is_longer.append(_lowercase			)

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

									if isinstance(input_mel[0] ,      _lowercase			):
															UpperCamelCase__         = [np.asarray(_lowercase ,      dtype=np.floataa			) for feature in input_mel]

									# is_longer is a list of bool
									UpperCamelCase__         = [[longer] for longer in is_longer]

									UpperCamelCase__         = {"""input_features""": input_mel, """is_longer""": is_longer}
									UpperCamelCase__         = BatchFeature(_lowercase			)

									if return_tensors is not None:
															UpperCamelCase__         = input_features.convert_to_tensors(_lowercase			)

									return input_features








'''simple docstring'''


from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowerCamelCase_				: List[Any]    	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

					lowerCamelCase_				: List[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
