





from collections.abc import Sequence
from queue import Queue


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Optional[int]:
								a   :	List[str]			=       start
								a   :	Dict			=       end
								a   :	Optional[int]			=       val
								a   :	List[str]			=       (start + end) // 2
								a   :	Any			=       left
								a   :	str			=       right






							def __repr__(     self   )			->				List[Any]:
								return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Tuple			=       collection
								a   :	Optional[int]			=       function
								if self.collection:
									a   :	int			=       self._build_tree(0   ,      len(lowerCAmelCase__   ) - 1   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								self._update_tree(self.root   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								return self._query_range(self.root   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								if start == end:
									return SegmentTreeNode(lowerCAmelCase__   ,      lowerCAmelCase__   ,      self.collection[start]   )
								a   :	int			=       (start + end) // 2
								a   :	List[str]			=       self._build_tree(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[Any]			=       self._build_tree(mid + 1   ,      lowerCAmelCase__   )
								return SegmentTreeNode(lowerCAmelCase__   ,      lowerCAmelCase__   ,      self.fn(left.val   ,      right.val   )   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								if node.start == i and node.end == i:
									a   :	str			=       val
									return
								if i <= node.mid:
									self._update_tree(node.left   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								else:
									self._update_tree(node.right   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.fn(node.left.val   ,      node.right.val   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[Any]:
								if node.start == i and node.end == j:
									return node.val

								if i <= node.mid:
									if j <= node.mid:
										# range in left child tree
										return self._query_range(node.left   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
									else:
										# range in left child tree and right child tree
										return self.fn(
										    self._query_range(node.left   ,      lowerCAmelCase__   ,      node.mid   )   ,      self._query_range(node.right   ,      node.mid + 1   ,      lowerCAmelCase__   )   ,      )
								else:
									# range in right child tree
									return self._query_range(node.right   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Any:
								if self.root is not None:
									a   :	Union[str, Any]			=       Queue()
									queue.put(self.root   )
									while not queue.empty():
										a   :	Any			=       queue.get()
										yield node

										if node.left is not None:
											queue.put(node.left   )

										if node.right is not None:
											queue.put(node.right   )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				a		:				List[Any]  			=    SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()









"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


a		:				List[Any]  			=    random.Random()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int=1.0					, _lowercase		:					Optional[int]=None					, _lowercase		:					Union[str, Any]=None      )				->Optional[Any]:






	'''simple docstring'''






	if rng is None:
		a   :	Tuple			=       global_rng

	a   :	Tuple			=       []
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=2000   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1_6000   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__="hann_window"   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=7600   ,      lowerCAmelCase__=1E-10   ,      lowerCAmelCase__=True   ,      )			->				Optional[Any]:
								a   :	int			=       parent
								a   :	Tuple			=       batch_size
								a   :	Dict			=       min_seq_length
								a   :	Any			=       max_seq_length
								a   :	Optional[int]			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a   :	Union[str, Any]			=       feature_size
								a   :	Tuple			=       padding_value
								a   :	str			=       sampling_rate
								a   :	Dict			=       do_normalize
								a   :	str			=       num_mel_bins
								a   :	List[str]			=       hop_length
								a   :	str			=       win_length
								a   :	Optional[Any]			=       win_function
								a   :	List[str]			=       fmin
								a   :	Any			=       fmax
								a   :	Optional[int]			=       mel_floor
								a   :	Tuple			=       return_attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Tuple:
								def _flatten(lowerCAmelCase__   ):
									return list(itertools.chain(*lowerCAmelCase__   )   )

								if equal_length:
									a   :	Union[str, Any]			=       floats_list((self.batch_size, self.max_seq_length)   )
								else:
									# make sure that inputs increase in size
									a   :	str			=       [
									    _flatten(floats_list((x, self.feature_size)   )   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Dict:
								if equal_length:
									a   :	str			=       [floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
								else:
									# make sure that inputs increase in size
									a   :	Any			=       [
									    floats_list((x, self.num_mel_bins)   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Optional[int]			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs


@require_torch
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =SpeechTaFeatureExtractor






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       SpeechTaFeatureExtractionTester(self   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,      axis=0   ) < 1E-3   )   )
								self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,      axis=0   ) - 1   ) < 1E-3   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Any			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								a   :	Optional[int]			=       feat_extract(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	Optional[Any]			=       feat_extract(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Dict			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]

								a   :	int			=       ["longest", "max_length", "do_not_pad"]
								a   :	Tuple			=       [None, 1600, None]
								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Dict			=       feat_extract(lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      return_tensors="np"   )
									a   :	List[Any]			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self.assertTrue(input_values[0][800:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self.assertTrue(input_values[0][1000:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	List[str]			=       range(800   ,      1400   ,      200   )
								a   :	List[str]			=       [floats_list((1, x)   )[0] for x in lengths]

								a   :	Any			=       ["longest", "max_length", "do_not_pad"]
								a   :	Any			=       [None, 1600, None]

								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       feat_extract(lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding=lowerCAmelCase__   )
									a   :	Dict			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	int			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Union[str, Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="max_length"   ,      return_tensors="np"   )
								a   :	List[Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1]   )
								self._check_zero_mean_unit_variance(input_values[2]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Tuple			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	List[Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Union[str, Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1000)   )

								a   :	List[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	int			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=2000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Dict			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Any			=       np.random.rand(100   ).astype(np.floataa   )
								a   :	Optional[Any]			=       np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
									a   :	str			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="np"   )
									self.assertTrue(np_processed.input_values.dtype == np.floataa   )
									a   :	List[str]			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="pt"   )
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )






							def 					__a     (     self   )			->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Union[str, Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Tuple			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test feature size
								a   :	Union[str, Any]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								self.assertTrue(input_values.ndim == 3   )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

								# Test not batched input
								a   :	Dict			=       feature_extractor(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	List[Any]			=       feature_extractor(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	Optional[int]			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	Any			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
								a   :	List[Any]			=       np.asarray(lowerCAmelCase__   )
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Union[str, Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

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

								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="np"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	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.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="pt"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	List[str]			=       batch_features_input[:, :, None]

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






							@require_torch
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Tuple			=       feat_extract.num_mel_bins  # hack!

								a   :	List[Any]			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )[input_name]
								a   :	Any			=       feat_extract.pad(lowerCAmelCase__   ,      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   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.feat_extract_dict
								a   :	Optional[Any]			=       True
								a   :	Union[str, Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	Any			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	int			=       feat_extract.model_input_names[0]

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

								a   :	Union[str, Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Dict			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(list(processed.attention_mask.shape   )   ,      list(processed[input_name].shape[:2]   )   )
								self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       self.feat_extract_dict
								a   :	str			=       True
								a   :	Optional[Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	List[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	str			=       BatchFeature({input_name: speech_inputs}   )
								a   :	Optional[Any]			=       min(lowerCAmelCase__   )

								a   :	List[Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Any			=       feat_extract.pad(
								    lowerCAmelCase__   ,      padding="max_length"   ,      max_length=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								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]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								from datasets import load_dataset

								a   :	Tuple			=       load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,      "clean"   ,      split="validation"   )
								# automatic decoding with librispeech
								a   :	Optional[Any]			=       ds.sort("id"   ).select(range(lowerCAmelCase__   )   )[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	List[Any]			=       torch.tensor(
								    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
								     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
								     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
								     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
								     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
								     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]   )
								# fmt: on

								a   :	List[str]			=       self._load_datasamples(1   )
								a   :	Union[str, Any]			=       SpeechTaFeatureExtractor()
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 9_3680)   )
								self.assertTrue(torch.allclose(input_values[0, :30]   ,      lowerCAmelCase__   ,      atol=1E-6   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	Tuple			=       torch.tensor(
								    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
								     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
								     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
								     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
								# fmt: on

								a   :	Dict			=       self._load_datasamples(1   )
								a   :	Tuple			=       SpeechTaFeatureExtractor()
								a   :	Optional[int]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 366, 80)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      lowerCAmelCase__   ,      atol=1E-4   )   )


