
"""simple docstring"""







import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class 				A__ (			_lowerCamelCase):
		# to overwrite at feature extractactor specific tests
		A_		: int            =			None
		A_		: Optional[int]            =			None

		@property
		def 							__lowerCamelCase							(   self				):
			return self.feat_extract_tester.prepare_feat_extract_dict()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'feature_size'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'sampling_rate'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'padding_value'				)				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase    :		Optional[Any]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Union[str, Any]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		Union[str, Any]		    =   BatchFeature({input_name: speech_inputs}				)

			self.assertTrue(all(len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				) for x, y in zip(_SCREAMING_SNAKE_CASE ,	processed_features[input_name]				)				)				)

			__lowerCAmelCase    :		Tuple		    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   BatchFeature({input_name: speech_inputs} ,	tensor_type='np'				)

			__lowerCAmelCase    :		Any		    =   processed_features[input_name]

			if len(batch_features_input.shape				) < 3:
				__lowerCAmelCase    :		Tuple		    =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Tuple		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		Union[str, Any]		    =   BatchFeature({input_name: speech_inputs} ,	tensor_type='pt'				)

			__lowerCAmelCase    :		Optional[int]		    =   processed_features[input_name]

			if len(batch_features_input.shape				) < 3:
				__lowerCAmelCase    :		Dict		    =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)

		@require_tf
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Optional[int]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		Any		    =   BatchFeature({input_name: speech_inputs} ,	tensor_type='tf'				)

			__lowerCAmelCase    :		Dict		    =   processed_features[input_name]

			if len(batch_features_input.shape				) < 3:
				__lowerCAmelCase    :		Tuple		    =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=False				):
			def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Optional[Any]		    =   len(input[0]				)
				for input_slice in input[1:]:
					if len(_SCREAMING_SNAKE_CASE				) != length:
						return False
				return True

			def _inputs_are_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
					return False

				for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE				) ,	np.asarray(_SCREAMING_SNAKE_CASE				) ,	atol=1E-3				):
						return False
				return True

			__lowerCAmelCase    :		Optional[int]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Union[str, Any]		    =   self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		Union[str, Any]		    =   BatchFeature({input_name: speech_inputs}				)

			__lowerCAmelCase    :		Dict		    =   self.feat_extract_tester.seq_length_diff
			__lowerCAmelCase    :		Tuple		    =   self.feat_extract_tester.max_seq_length + pad_diff
			__lowerCAmelCase    :		List[Any]		    =   self.feat_extract_tester.min_seq_length
			__lowerCAmelCase    :		List[Any]		    =   self.feat_extract_tester.batch_size
			__lowerCAmelCase    :		Any		    =   self.feat_extract_tester.feature_size

			# test padding for List[int] + numpy
			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   input_a[input_name]

			__lowerCAmelCase    :		List[str]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest'				)
			__lowerCAmelCase    :		Any		    =   input_a[input_name]

			__lowerCAmelCase    :		List[Any]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[-1]				)				)
			__lowerCAmelCase    :		int		    =   input_a[input_name]

			__lowerCAmelCase    :		Dict		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='np'				)
			__lowerCAmelCase    :		Tuple		    =   input_a[input_name]

			# max_length parameter has to be provided when setting `padding="max_length"`
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='max_length'				)[input_name]

			__lowerCAmelCase    :		Union[str, Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=_SCREAMING_SNAKE_CASE ,	return_tensors='np'				)
			__lowerCAmelCase    :		int		    =   input_a[input_name]

			self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(len(input_a[0]				) == pad_min_length				)
			self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
			self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
			self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

			# test padding for `pad_to_multiple_of` for List[int] + numpy
			__lowerCAmelCase    :		str		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=10				)
			__lowerCAmelCase    :		int		    =   input_a[input_name]

			__lowerCAmelCase    :		List[Any]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	pad_to_multiple_of=10				)
			__lowerCAmelCase    :		str		    =   input_a[input_name]

			__lowerCAmelCase    :		Tuple		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	pad_to_multiple_of=10 ,	max_length=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   input_a[input_name]

			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	pad_to_multiple_of=10 ,	max_length=_SCREAMING_SNAKE_CASE ,	return_tensors='np' ,	)
			__lowerCAmelCase    :		Dict		    =   input_a[input_name]

			self.assertTrue(all(len(_SCREAMING_SNAKE_CASE				) % 10 == 0 for x in input_a				)				)
			self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

			__lowerCAmelCase    :		List[Any]		    =   pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
			self.assertTrue(all(len(_SCREAMING_SNAKE_CASE				) == expected_mult_pad_length for x in input_a				)				)
			self.assertEqual(input_a.shape[:2] ,	(batch_size, expected_mult_pad_length)				)

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == feature_size				)

			# Check padding value is correct
			__lowerCAmelCase    :		int		    =   (np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
			self.assertTrue(
			    abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
			    < 1E-3				)
			self.assertTrue(
			    abs(
			        np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
			    < 1E-3				)
			self.assertTrue(
			    abs(
			        np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
			    < 1E-3				)
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1E-3				)
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
			    < 1E-3				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=False				):
			def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Optional[int]		    =   len(input[0]				)
				for input_slice in input[1:]:
					if len(_SCREAMING_SNAKE_CASE				) != length:
						return False
				return True

			def _inputs_are_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
					return False

				for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE				) ,	np.asarray(_SCREAMING_SNAKE_CASE				) ,	atol=1E-3				):
						return False
				return True

			__lowerCAmelCase    :		Any		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Dict		    =   self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		int		    =   BatchFeature({input_name: speech_inputs}				)

			# truncate to smallest
			__lowerCAmelCase    :		List[str]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				) ,	truncation=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   input_a[input_name]

			__lowerCAmelCase    :		Dict		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				)				)
			__lowerCAmelCase    :		Union[str, Any]		    =   input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)

			# truncate to smallest with np
			__lowerCAmelCase    :		Union[str, Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				) ,	return_tensors='np' ,	truncation=_SCREAMING_SNAKE_CASE ,	)
			__lowerCAmelCase    :		Optional[Any]		    =   input_a[input_name]

			__lowerCAmelCase    :		Dict		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				) ,	return_tensors='np'				)
			__lowerCAmelCase    :		List[Any]		    =   input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)

			# truncate to middle
			__lowerCAmelCase    :		str		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[1]				) ,	truncation=_SCREAMING_SNAKE_CASE ,	return_tensors='np' ,	)
			__lowerCAmelCase    :		int		    =   input_a[input_name]

			__lowerCAmelCase    :		List[Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[1]				) ,	truncation=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   input_a[input_name]

			__lowerCAmelCase    :		Optional[int]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[1]				) ,	return_tensors='np'				)
			__lowerCAmelCase    :		Any		    =   input_a[input_name]

			self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				feat_extract.pad(_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE				)[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	truncation=_SCREAMING_SNAKE_CASE				)[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	truncation=_SCREAMING_SNAKE_CASE				)[input_name]

			# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='max_length' ,	truncation=_SCREAMING_SNAKE_CASE				)[input_name]

			# test truncation for `pad_to_multiple_of` for List[int] + numpy
			__lowerCAmelCase    :		str		    =   12
			__lowerCAmelCase    :		List[Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				) ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	)
			__lowerCAmelCase    :		Optional[Any]		    =   input_a[input_name]

			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=len(speech_inputs[0]				) ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	)
			__lowerCAmelCase    :		Any		    =   input_a[input_name]

			# retrieve expected_length as multiple of pad_to_multiple_of
			__lowerCAmelCase    :		List[str]		    =   len(speech_inputs[0]				)
			if expected_length % pad_to_multiple_of != 0:
				__lowerCAmelCase    :		str		    =   ((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

			self.assertTrue(len(input_a[0]				) == expected_length				)
			self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)
			self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE				)				)

		def 							__lowerCamelCase							(   self				):
			self._check_padding(numpify=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self._check_padding(numpify=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self._check_truncation(numpify=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self._check_truncation(numpify=_SCREAMING_SNAKE_CASE				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		List[Any]		    =   self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase    :		Any		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		Any		    =   BatchFeature({input_name: speech_inputs}				)

			__lowerCAmelCase    :		int		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='np'				)[input_name]
			__lowerCAmelCase    :		Any		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='pt'				)[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1E-2				)

		@require_tf
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.feature_extraction_class(**self.feat_extract_dict				)
			__lowerCAmelCase    :		Any		    =   self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase    :		int		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		str		    =   BatchFeature({input_name: speech_inputs}				)

			__lowerCAmelCase    :		str		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='np'				)[input_name]
			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='tf'				)[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1E-2				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   self.feat_extract_dict
			__lowerCAmelCase    :		Union[str, Any]		    =   True
			__lowerCAmelCase    :		List[str]		    =   self.feature_extraction_class(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase    :		Union[str, Any]		    =   [len(_SCREAMING_SNAKE_CASE				) for x in speech_inputs]
			__lowerCAmelCase    :		Optional[int]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		List[Any]		    =   BatchFeature({input_name: speech_inputs}				)

			__lowerCAmelCase    :		Optional[Any]		    =   feat_extract.pad(_SCREAMING_SNAKE_CASE ,	padding='longest' ,	return_tensors='np'				)
			self.assertIn('attention_mask' ,	_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(list(processed.attention_mask.shape				) ,	list(processed[input_name].shape[:2]				)				)
			self.assertListEqual(processed.attention_mask.sum(-1				).tolist() ,	_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.feat_extract_dict
			__lowerCAmelCase    :		Optional[int]		    =   True
			__lowerCAmelCase    :		Union[str, Any]		    =   self.feature_extraction_class(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase    :		List[str]		    =   [len(_SCREAMING_SNAKE_CASE				) for x in speech_inputs]
			__lowerCAmelCase    :		Union[str, Any]		    =   feat_extract.model_input_names[0]

			__lowerCAmelCase    :		List[str]		    =   BatchFeature({input_name: speech_inputs}				)
			__lowerCAmelCase    :		int		    =   min(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   feat_extract.pad(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	return_tensors='np'				)
			self.assertIn('attention_mask' ,	_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    list(processed_pad.attention_mask.shape				) ,	[processed_pad[input_name].shape[0], max_length]				)
			self.assertListEqual(
			    processed_pad.attention_mask[:, :max_length].sum(-1				).tolist() ,	[max_length for x in speech_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