
'''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


lowercase     :							List[Any]       						=     logging.get_logger(__name__)





class 					A							(				__snake_case		):
     __magic_name__          =						['''input_features''', '''is_longer''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=48000		,     SCREAMING_SNAKE_CASE=480		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE = 0		,     SCREAMING_SNAKE_CASE = 14000		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = "fusion"		,     SCREAMING_SNAKE_CASE = "repeatpad"		,     **SCREAMING_SNAKE_CASE		,     )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Dict[str, Any]:






         """simple docstring"""






         A			:  Dict   =		copy.deepcopy(self.__dict__      )
         A			:  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 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None      )					->							np.ndarray:






         """simple docstring"""






         A			:  Tuple   =		spectrogram(
             SCREAMING_SNAKE_CASE		,     window_function(self.fft_window_size		,     '''hann'''      )		,     frame_length=self.fft_window_size		,     hop_length=self.hop_length		,     power=2.0		,     mel_filters=SCREAMING_SNAKE_CASE		,     log_mel='''dB'''		,     )
         return log_mel_spectrogram.T





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






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

         A			:  Tuple   =		mel[idx_front : idx_front + chunk_frames, :]
         A			:  List[Any]   =		mel[idx_middle : idx_middle + chunk_frames, :]
         A			:  int   =		mel[idx_back : idx_back + chunk_frames, :]

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							np.array:






         """simple docstring"""






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

         else:
             A			:  Tuple   =		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":
                     A			:  List[str]   =		int(max_length / len(SCREAMING_SNAKE_CASE      )      )
                     A			:  List[str]   =		np.stack(np.tile(SCREAMING_SNAKE_CASE		,     n_repeat + 1      )      )[:max_length]
                 if padding == "repeatpad":
                     A			:  int   =		int(max_length / len(SCREAMING_SNAKE_CASE      )      )
                     A			:  Any   =		np.stack(np.tile(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
                 A			:  str   =		np.pad(SCREAMING_SNAKE_CASE		,     (0, max_length - waveform.shape[0])		,     mode='''constant'''		,     constant_values=0      )

             if truncation == "fusion":
                 A			:  List[Any]   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE		,     self.mel_filters      )
                 A			:  Dict   =		np.stack([input_mel, input_mel, input_mel, input_mel]		,     axis=0      )
             else:
                 A			:  int   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE		,     self.mel_filters_slaney      )[None, :]

         return input_mel, longer





     def __call__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE		,     )					->							BatchFeature:






         """simple docstring"""






         A			:  str   =		truncation if truncation is not None else self.truncation
         A			:  str   =		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.'''      )

         A			:  Tuple   =		isinstance(SCREAMING_SNAKE_CASE		,     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}'      )
         A			:  Union[str, Any]   =		is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE		,     (list, tuple)      ) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list)      ))
         )

         if is_batched:
             A			:  Dict   =		[np.asarray(SCREAMING_SNAKE_CASE		,     dtype=np.floataa      ) for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE		,     np.ndarray      ):
             A			:  str   =		np.asarray(SCREAMING_SNAKE_CASE		,     dtype=np.floataa      )
         elif isinstance(SCREAMING_SNAKE_CASE		,     np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
             A			:  Optional[Any]   =		raw_speech.astype(np.floataa      )

         # always return batch
         if not is_batched:
             A			:  List[Any]   =		[np.asarray(SCREAMING_SNAKE_CASE      )]

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

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

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

         if isinstance(input_mel[0]		,     SCREAMING_SNAKE_CASE      ):
             A			:  Any   =		[np.asarray(SCREAMING_SNAKE_CASE		,     dtype=np.floataa      ) for feature in input_mel]

         # is_longer is a list of bool
         A			:  List[Any]   =		[[longer] for longer in is_longer]

         A			:  Any   =		{'''input_features''': input_mel, '''is_longer''': is_longer}
         A			:  str   =		BatchFeature(SCREAMING_SNAKE_CASE      )

         if return_tensors is not None:
             A			:  str   =		input_features.convert_to_tensors(SCREAMING_SNAKE_CASE      )

         return input_features






'''simple docstring'''




from random import randint, random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__ = False						,							snake_case__ = False						,							snake_case__ = 5						,							):




    '''simple docstring'''



    A			:  List[str]   =		[[-1] * number_of_cells]  # Create a highway without any car
    A			:  Optional[Any]   =		0
    A			:  Tuple   =		max(snake_case__						,							0		)
    while i < number_of_cells:
        A			:  Optional[int]   =		(
            randint(0						,							snake_case__		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1						,							max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    A			:  str   =		highway_now[car_index + 1 :]
    for cell in range(len(snake_case__		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(snake_case__						,							-1		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		len(snake_case__		)
    # Beforce calculations, the highway is empty
    A			:  int   =		[-1] * number_of_cells

    for car_index in range(snake_case__		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:  Optional[Any]   =		min(highway_now[car_index] + 1						,							snake_case__		)
            # Number of empty cell before the next car
            A			:  List[str]   =		get_distance(snake_case__						,							snake_case__		) - 1
            # We can't have the car causing an accident
            A			:  Union[str, Any]   =		min(next_highway[car_index]						,							snake_case__		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:  List[str]   =		max(next_highway[car_index] - 1						,							0		)
    return next_highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		len(highway[0]		)

    for i in range(snake_case__		):
        A			:  int   =		update(highway[i]						,							snake_case__						,							snake_case__		)
        A			:  Any   =		[-1] * number_of_cells

        for car_index in range(snake_case__		):
            A			:  Dict   =		next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:  Dict   =		(car_index + speed) % number_of_cells
                # Commit the change of position
                A			:  List[str]   =		speed
        highway.append(snake_case__		)

    return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




