"""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


lowerCamelCase__           =	logging.get_logger(__name__)




class 				A__ (			_lowerCamelCase):
		A_		: Union[str, Any]            =			['audio_values', 'audio_mask']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=[16, 16] ,	_SCREAMING_SNAKE_CASE=1_28 ,	_SCREAMING_SNAKE_CASE=4_41_00 ,	_SCREAMING_SNAKE_CASE=86 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=0.0 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(
			    feature_size=_SCREAMING_SNAKE_CASE ,	sampling_rate=_SCREAMING_SNAKE_CASE ,	padding_value=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   spectrogram_length
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		Any		    =   patch_size
			__lowerCAmelCase    :		Dict		    =   feature_size // self.patch_size[1]
			__lowerCAmelCase    :		Dict		    =   n_fft
			__lowerCAmelCase    :		str		    =   sampling_rate // hop_length_to_sampling_rate
			__lowerCAmelCase    :		List[str]		    =   sampling_rate
			__lowerCAmelCase    :		str		    =   padding_value
			__lowerCAmelCase    :		Tuple		    =   mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2 ,	num_mel_filters=_SCREAMING_SNAKE_CASE ,	min_frequency=0.0 ,	max_frequency=2_20_50.0 ,	sampling_rate=_SCREAMING_SNAKE_CASE ,	norm='slaney' ,	mel_scale='slaney' ,	).T

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   spectrogram(
			    _SCREAMING_SNAKE_CASE ,	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 ,	)
			__lowerCAmelCase    :		Optional[int]		    =   log_spec[:, :-1]
			__lowerCAmelCase    :		List[str]		    =   log_spec - 20.0
			__lowerCAmelCase    :		Dict		    =   np.clip(log_spec / 40.0 ,	-2.0 ,	0.0				) + 1.0
			return log_spec







		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	**_SCREAMING_SNAKE_CASE ,	):

			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.'				)

			__lowerCAmelCase    :		int		    =   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}"				)
			__lowerCAmelCase    :		int		    =   is_batched_numpy or (
			    isinstance(_SCREAMING_SNAKE_CASE ,	(list, tuple)				) and (isinstance(raw_speech[0] ,	(np.ndarray, tuple, list)				))
			)
			if is_batched:
				__lowerCAmelCase    :		Union[str, Any]		    =   [np.asarray([speech] ,	dtype=np.floataa				).T for speech in raw_speech]
			elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE ,	np.ndarray				):
				__lowerCAmelCase    :		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				):
				__lowerCAmelCase    :		Optional[Any]		    =   raw_speech.astype(np.floataa				)
			# always return batch
			if not is_batched:
				__lowerCAmelCase    :		Optional[Any]		    =   [np.asarray([raw_speech]				).T]

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

			# Create audio attention mask
			__lowerCAmelCase    :		List[Any]		    =   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:
				__lowerCAmelCase    :		List[str]		    =   [
				    (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
				]
				__lowerCAmelCase    :		Optional[Any]		    =   np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				)

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

			# return as BatchFeature
			if return_attention_mask:
				__lowerCAmelCase    :		Optional[Any]		    =   {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
			else:
				__lowerCAmelCase    :		Any		    =   {'audio_values': padded_audio_features}

			__lowerCAmelCase    :		Dict		    =   BatchFeature(data=_SCREAMING_SNAKE_CASE ,	tensor_type=_SCREAMING_SNAKE_CASE				)
			return encoded_inputs

"""simple docstring"""







class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   0
			__lowerCAmelCase    :		Any		    =   0
			__lowerCAmelCase    :		List[Any]		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if vertex not in self.adjacency:
				__lowerCAmelCase    :		Dict		    =   {}
				self.num_vertices += 1

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self.add_vertex(_SCREAMING_SNAKE_CASE				)
			self.add_vertex(_SCREAMING_SNAKE_CASE				)

			if head == tail:
				return

			__lowerCAmelCase    :		Union[str, Any]		    =   weight
			__lowerCAmelCase    :		str		    =   weight

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.get_edges()
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   edge
				edges.remove((tail, head, weight)				)
			for i in range(len(_SCREAMING_SNAKE_CASE				)				):
				__lowerCAmelCase    :		List[str]		    =   list(edges[i]				)

			edges.sort(key=lambda _SCREAMING_SNAKE_CASE				: e[2]				)
			for i in range(len(_SCREAMING_SNAKE_CASE				) - 1				):
				if edges[i][2] >= edges[i + 1][2]:
					__lowerCAmelCase    :		Dict		    =   edges[i][2] + 1
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   edge
				__lowerCAmelCase    :		Union[str, Any]		    =   weight
				__lowerCAmelCase    :		Optional[int]		    =   weight

		def __str__(   self				):
			__lowerCAmelCase    :		List[Any]		    =   ''
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					__lowerCAmelCase    :		str		    =   self.adjacency[head][tail]
					string += f"{head} -> {tail} == {weight}\n"
			return string.rstrip('\n'				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   []
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					output.append((tail, head, self.adjacency[head][tail])				)
			return output

		def 							__lowerCamelCase							(   self				):
			return self.adjacency.keys()

		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   Graph()
			if vertices is None:
				__lowerCAmelCase    :		int		    =   []
			if edges is None:
				__lowerCAmelCase    :		Optional[int]		    =   []
			for vertex in vertices:
				g.add_vertex(_SCREAMING_SNAKE_CASE				)
			for edge in edges:
				g.add_edge(*_SCREAMING_SNAKE_CASE				)
			return g




		class 				A__ :

				def __init__(   self				):
					__lowerCAmelCase    :		Dict		    =   {}
					__lowerCAmelCase    :		Optional[int]		    =   {}

				def __len__(   self				):
					return len(self.parent				)

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item in self.parent:
						return self.find(_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Dict		    =   item
					__lowerCAmelCase    :		int		    =   0
					return item

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item not in self.parent:
						return self.make_set(_SCREAMING_SNAKE_CASE				)
					if item != self.parent[item]:
						__lowerCAmelCase    :		Dict		    =   self.find(self.parent[item]				)
					return self.parent[item]







				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Any		    =   self.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		List[str]		    =   self.find(_SCREAMING_SNAKE_CASE				)

					if roota == roota:
						return roota

					if self.rank[roota] > self.rank[roota]:
						__lowerCAmelCase    :		Tuple		    =   roota
						return roota

					if self.rank[roota] < self.rank[roota]:
						__lowerCAmelCase    :		str		    =   roota
						return roota

					if self.rank[roota] == self.rank[roota]:
						self.rank[roota] += 1
						__lowerCAmelCase    :		Optional[int]		    =   roota
						return roota
					return None



		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   graph.num_vertices

			__lowerCAmelCase    :		List[Any]		    =   Graph.UnionFind()
			__lowerCAmelCase    :		Tuple		    =   []
			while num_components > 1:
				__lowerCAmelCase    :		str		    =   {}
				for vertex in graph.get_vertices():
					__lowerCAmelCase    :		Union[str, Any]		    =   -1

				__lowerCAmelCase    :		int		    =   graph.get_edges()
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   edge
					edges.remove((tail, head, weight)				)
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   edge
					__lowerCAmelCase    :		Optional[int]		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		str		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					if seta != seta:
						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]

						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]
				for vertex in cheap_edge:
					if cheap_edge[vertex] != -1:
						__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   cheap_edge[vertex]
						if union_find.find(_SCREAMING_SNAKE_CASE				) != union_find.find(_SCREAMING_SNAKE_CASE				):
							union_find.union(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
							mst_edges.append(cheap_edge[vertex]				)
							__lowerCAmelCase    :		List[str]		    =   num_components - 1
			__lowerCAmelCase    :		Union[str, Any]		    =   Graph.build(edges=_SCREAMING_SNAKE_CASE				)
			return mst