



'''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


a__    =	logging.get_logger(__name__)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Union[str, Any]          =		["""input_features""", """is_longer"""]

   def __init__(					self							:			int				,		lowerCAmelCase							:			List[str]=64				,		lowerCAmelCase							:			Any=4_8000				,		lowerCAmelCase							:			List[Any]=480				,		lowerCAmelCase							:			str=10				,		lowerCAmelCase							:			Tuple=1024				,		lowerCAmelCase							:			Dict=0.0				,		lowerCAmelCase							:			Optional[Any]=False				,		lowerCAmelCase							:			float = 0				,		lowerCAmelCase							:			float = 1_4000				,		lowerCAmelCase							:			int = None				,		lowerCAmelCase							:			str = "fusion"				,		lowerCAmelCase							:			str = "repeatpad"				,		**lowerCAmelCase							:			Dict				,		)				->				Optional[Any]:

    """simple docstring"""







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

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

    """simple docstring"""







    _snake_case    :  Dict					=					copy.deepcopy(self.__dict__)
    _snake_case    :  Union[str, Any]					=					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      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			np.array				,		lowerCAmelCase							:			Optional[np.array] = None)				->				np.ndarray:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					spectrogram(
        lowerCAmelCase				,		window_function(self.fft_window_size				,		"""hann""")				,		frame_length=self.fft_window_size				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=lowerCAmelCase				,		log_mel="""dB"""				,		)
    return log_mel_spectrogram.T

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			int)				->				List[str]:

    """simple docstring"""







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

    _snake_case    :  Optional[int]					=					mel[idx_front : idx_front + chunk_frames, :]
    _snake_case    :  Any					=					mel[idx_middle : idx_middle + chunk_frames, :]
    _snake_case    :  Union[str, Any]					=					mel[idx_back : idx_back + chunk_frames, :]

    _snake_case    :  List[str]					=					torch.tensor(mel[None, None, :])
    _snake_case    :  Optional[Any]					=					torch.nn.functional.interpolate(
        lowerCAmelCase				,		size=[chunk_frames, 64]				,		mode="""bilinear"""				,		align_corners=lowerCAmelCase)
    _snake_case    :  str					=					mel_shrink[0][0].numpy()
    _snake_case    :  int					=					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,		axis=0)
    return mel_fusion

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			np.array				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[int])				->				np.array:

    """simple docstring"""







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

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

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

    return input_mel, longer


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

    """simple docstring"""







    _snake_case    :  Dict					=					truncation if truncation is not None else self.truncation
    _snake_case    :  int					=					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.""")

    _snake_case    :  int					=					isinstance(lowerCAmelCase				,		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}''')
    _snake_case    :  int					=					is_batched_numpy or (
        isinstance(lowerCAmelCase				,		(list, tuple)) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)))
    )

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

    # always return batch
    if not is_batched:
     _snake_case    :  Optional[Any]					=					[np.asarray(lowerCAmelCase)]

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

    _snake_case    :  List[Any]					=					[]
    _snake_case    :  int					=					[]
    for mel, longer in padded_inputs:
     input_mel.append(lowerCAmelCase)
     is_longer.append(lowerCAmelCase)

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

    if isinstance(input_mel[0]				,		lowerCAmelCase):
     _snake_case    :  List[str]					=					[np.asarray(lowerCAmelCase				,		dtype=np.floataa) for feature in input_mel]

    # is_longer is a list of bool
    _snake_case    :  Dict					=					[[longer] for longer in is_longer]

    _snake_case    :  Optional[Any]					=					{"""input_features""": input_mel, """is_longer""": is_longer}
    _snake_case    :  Optional[int]					=					BatchFeature(lowerCAmelCase)

    if return_tensors is not None:
     _snake_case    :  int					=					input_features.convert_to_tensors(lowerCAmelCase)

    return input_features





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
