


'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


_lowercase    :    Tuple			     =  logging.get_logger(__name__)






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   str							= ["audio_values", "audio_mask"]




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				str=2048   ,					lowerCAmelCase    :				str=1   ,					lowerCAmelCase    :				Any=[16, 16]   ,					lowerCAmelCase    :				List[str]=128   ,					lowerCAmelCase    :				Tuple=44100   ,					lowerCAmelCase    :				Dict=86   ,					lowerCAmelCase    :				List[str]=2048   ,					lowerCAmelCase    :				int=0.0   ,					**lowerCAmelCase    :				Optional[Any]   ,					)->  Dict:

										"""simple docstring"""




										super().__init__(
										    feature_size=lowerCAmelCase   ,					sampling_rate=lowerCAmelCase   ,					padding_value=lowerCAmelCase   ,					**lowerCAmelCase   ,					)

										UpperCAmelCase         =			spectrogram_length
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			feature_size // self.patch_size[1]
										UpperCAmelCase         =			n_fft
										UpperCAmelCase         =			sampling_rate // hop_length_to_sampling_rate
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			padding_value
										UpperCAmelCase         =			mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2   ,					num_mel_filters=lowerCAmelCase   ,					min_frequency=0.0   ,					max_frequency=22050.0   ,					sampling_rate=lowerCAmelCase   ,					norm='''slaney'''   ,					mel_scale='''slaney'''   ,					).T




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				np.array   )->  np.ndarray:

										"""simple docstring"""




										UpperCAmelCase         =			spectrogram(
										    lowerCAmelCase   ,					window_function(self.n_fft   ,					'''hann'''   )   ,					frame_length=self.n_fft   ,					hop_length=self.hop_length   ,					power=2.0   ,					mel_filters=self.mel_filters.T   ,					log_mel='''dB'''   ,					db_range=80.0   ,					)
										UpperCAmelCase         =			log_spec[:, :-1]
										UpperCAmelCase         =			log_spec - 20.0
										UpperCAmelCase         =			np.clip(log_spec / 40.0   ,					-2.0   ,					0.0   ) + 1.0
										return log_spec





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

										"""simple docstring"""




										if sampling_rate is not None:
																if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    '''This feature extractor is set to support sampling rate'''
																						    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
																						    F""" 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(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}"""   )
										UpperCAmelCase         =			is_batched_numpy or (
										    isinstance(lowerCAmelCase   ,					(list, tuple)   ) and (isinstance(raw_speech[0]   ,					(np.ndarray, tuple, list)   ))
										)
										if is_batched:
																UpperCAmelCase         =			[np.asarray([speech]   ,					dtype=np.floataa   ).T for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase   ,					np.ndarray   ):
																UpperCAmelCase         =			np.asarray(lowerCAmelCase   ,					dtype=np.floataa   )
										elif isinstance(lowerCAmelCase   ,					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([raw_speech]   ).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										UpperCAmelCase         =			[
										    self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]   ,					lowerCAmelCase   ):
																UpperCAmelCase         =			[np.asarray(lowerCAmelCase   ,					dtype=np.floataa   ) for feature in audio_features]

										# Create audio attention mask
										UpperCAmelCase         =			max(
										    [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
										if return_attention_mask:
																UpperCAmelCase         =			[
																    (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
																    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
																    for feature in audio_features
																]
																UpperCAmelCase         =			np.array(lowerCAmelCase   ).astype(np.floataa   )

										# convert into correct format for padding
										UpperCAmelCase         =			max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										UpperCAmelCase         =			np.ones([len(lowerCAmelCase   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
										UpperCAmelCase         =			padded_audio_features * self.padding_value
										for i in range(len(lowerCAmelCase   )   ):
																UpperCAmelCase         =			audio_features[i]
																UpperCAmelCase         =			feature

										# return as BatchFeature
										if return_attention_mask:
																UpperCAmelCase         =			{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
										else:
																UpperCAmelCase         =			{'''audio_values''': padded_audio_features}

										UpperCAmelCase         =			BatchFeature(data=lowerCAmelCase   ,					tensor_type=lowerCAmelCase   )
										return encoded_inputs









'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						# for each node and his adjacency list add them and the rank of the node to queue
						# using heapq module the queue will be filled like a Priority Queue
						# heapq works with a min priority queue, so I used -1*len(v) to build it
						for key, value in graph.items():
												# O(log(n))
												heapq.heappush(A   ,     [-1 * len(A  ), (key, value)]  )

						# chosen_vertices = set of chosen vertices
						UpperCAmelCase         =			set()

						# while queue isn't empty and there are still edges
						#   (queue[0][0] is the rank of the node with max rank)
						while queue and queue[0][0] != 0:
												# extract vertex with max rank from queue and add it to chosen_vertices
												UpperCAmelCase         =			heapq.heappop(A  )[1][0]
												chosen_vertices.add(A  )

												# Remove all arcs adjacent to argmax
												for elem in queue:
																		# if v haven't adjacent node, skip
																		if elem[0] == 0:
																								continue
																		# if argmax is reachable from elem
																		# remove argmax from elem's adjacent list and update his rank
																		if argmax in elem[1][1]:
																								UpperCAmelCase         =			elem[1][1].index(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")





