"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__           =	logging.get_logger(__name__)




class 				A__ (			_lowerCamelCase):
		A_		: List[Any]            =			['input_values', 'padding_mask']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE = 1 ,	_SCREAMING_SNAKE_CASE = 2_40_00 ,	_SCREAMING_SNAKE_CASE = 0.0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(feature_size=__UpperCamelCase ,	sampling_rate=__UpperCamelCase ,	padding_value=__UpperCamelCase ,	**__UpperCamelCase				)
			__lowerCAmelCase    :		List[str]		    =   chunk_length_s
			__lowerCAmelCase    :		str		    =   overlap

		@property
		def 							__lowerCamelCase							(   self				):
			if self.chunk_length_s is None:
				return None
			else:
				return int(self.chunk_length_s * self.sampling_rate				)

		@property
		def 							__lowerCamelCase							(   self				):
			if self.chunk_length_s is None or self.overlap is None:
				return None
			else:
				return max(1 ,	int((1.0 - self.overlap) * self.chunk_length				)				)







		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	):
			if sampling_rate is not None:
				if sampling_rate != self.sampling_rate:
					raise ValueError(
					    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
					    f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
					    f" {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.'				)

			if padding and truncation:
				raise ValueError('Both padding and truncation were set. Make sure you only set one.'				)
			elif padding is None:
				# by default let's pad the inputs
				__lowerCAmelCase    :		Optional[int]		    =   True

			__lowerCAmelCase    :		Optional[int]		    =   bool(
			    isinstance(__UpperCamelCase ,	(list, tuple)				) and (isinstance(raw_audio[0] ,	(np.ndarray, tuple, list)				))				)

			if is_batched:
				__lowerCAmelCase    :		List[str]		    =   [np.asarray(__UpperCamelCase ,	dtype=np.floataa				).T for audio in raw_audio]
			elif not is_batched and not isinstance(__UpperCamelCase ,	np.ndarray				):
				__lowerCAmelCase    :		str		    =   np.asarray(__UpperCamelCase ,	dtype=np.floataa				)
			elif isinstance(__UpperCamelCase ,	np.ndarray				) and raw_audio.dtype is np.dtype(np.floataa				):
				__lowerCAmelCase    :		Any		    =   raw_audio.astype(np.floataa				)

			# always return batch
			if not is_batched:
				__lowerCAmelCase    :		List[Any]		    =   [np.asarray(__UpperCamelCase				).T]

			# verify inputs are valid
			for idx, example in enumerate(__UpperCamelCase				):
				if example.ndim > 2:
					raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}"				)
				if self.feature_size == 1 and example.ndim != 1:
					raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels"				)
				if self.feature_size == 2 and example.shape[-1] != 2:
					raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels"				)

			__lowerCAmelCase    :		Union[str, Any]		    =   None
			__lowerCAmelCase    :		Dict		    =   BatchFeature({'input_values': raw_audio}				)
			if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
				if truncation:
					__lowerCAmelCase    :		str		    =   min(array.shape[0] for array in raw_audio				)
					__lowerCAmelCase    :		Optional[int]		    =   int(np.floor(max_length / self.chunk_stride				)				)
					__lowerCAmelCase    :		Any		    =   (nb_step - 1) * self.chunk_stride + self.chunk_length
				elif padding:
					__lowerCAmelCase    :		str		    =   max(array.shape[0] for array in raw_audio				)
					__lowerCAmelCase    :		Tuple		    =   int(np.ceil(max_length / self.chunk_stride				)				)
					__lowerCAmelCase    :		Any		    =   (nb_step - 1) * self.chunk_stride + self.chunk_length
					__lowerCAmelCase    :		int		    =   'max_length'
				else:
					__lowerCAmelCase    :		Optional[Any]		    =   input_values

        # normal padding on batch
			if padded_inputs is None:
				__lowerCAmelCase    :		Dict		    =   self.pad(
				    __UpperCamelCase ,	max_length=__UpperCamelCase ,	truncation=__UpperCamelCase ,	padding=__UpperCamelCase ,	return_attention_mask=__UpperCamelCase ,	)
				if padding:
					__lowerCAmelCase    :		Tuple		    =   padded_inputs.pop('attention_mask'				)

			__lowerCAmelCase    :		Optional[Any]		    =   []
			for example in padded_inputs.pop('input_values'				):
				if self.feature_size == 1:
					__lowerCAmelCase    :		Optional[int]		    =   example[..., None]
				input_values.append(example.T				)

			__lowerCAmelCase    :		Any		    =   input_values
			if return_tensors is not None:
				__lowerCAmelCase    :		Dict		    =   padded_inputs.convert_to_tensors(__UpperCamelCase				)

			return padded_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