


"""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


_lowerCamelCase     =  random.Random()


def __lowercase     (      lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      List[Any]=1.0						,					lowerCamelCase_  :      Tuple=None						,					lowerCamelCase_  :      Optional[int]=None  ):
				if rng is None:
								SCREAMING_SNAKE_CASE__     							=				global_rng

				SCREAMING_SNAKE_CASE__     							=				[]
				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 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=400       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=1_6000       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=80       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=64       ,		UpperCAmelCase__="hann_window"       ,		UpperCAmelCase__=80       ,		UpperCAmelCase__=7600       ,		UpperCAmelCase__=1e-10       ,		UpperCAmelCase__=True       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				min_seq_length
								SCREAMING_SNAKE_CASE__     							=				max_seq_length
								SCREAMING_SNAKE_CASE__     							=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								SCREAMING_SNAKE_CASE__     							=				feature_size
								SCREAMING_SNAKE_CASE__     							=				padding_value
								SCREAMING_SNAKE_CASE__     							=				sampling_rate
								SCREAMING_SNAKE_CASE__     							=				do_normalize
								SCREAMING_SNAKE_CASE__     							=				num_mel_bins
								SCREAMING_SNAKE_CASE__     							=				hop_length
								SCREAMING_SNAKE_CASE__     							=				win_length
								SCREAMING_SNAKE_CASE__     							=				win_function
								SCREAMING_SNAKE_CASE__     							=				fmin
								SCREAMING_SNAKE_CASE__     							=				fmax
								SCREAMING_SNAKE_CASE__     							=				mel_floor
								SCREAMING_SNAKE_CASE__     							=				return_attention_mask



				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=False     ):
								def _flatten(UpperCAmelCase__     ):
												return list(itertools.chain(*UpperCAmelCase__     )     )

								if equal_length:
												SCREAMING_SNAKE_CASE__     							=				floats_list((self.batch_size, self.max_seq_length)     )
								else:
												# make sure that inputs increase in size
												SCREAMING_SNAKE_CASE__     							=				[
												    _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:
												SCREAMING_SNAKE_CASE__     							=				[np.asarray(UpperCAmelCase__     ) for x in speech_inputs]

								return speech_inputs






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=False     ):
								if equal_length:
												SCREAMING_SNAKE_CASE__     							=				[floats_list((self.max_seq_length, self.num_mel_bins)     ) for _ in range(self.batch_size     )]
								else:
												# make sure that inputs increase in size
												SCREAMING_SNAKE_CASE__     							=				[
												    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:
												SCREAMING_SNAKE_CASE__     							=				[np.asarray(UpperCAmelCase__     ) for x in speech_inputs]

								return speech_inputs


@require_torch
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					SpeechTaFeatureExtractor



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				SpeechTaFeatureExtractionTester(self     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								self.assertTrue(np.all(np.mean(UpperCAmelCase__       ,		axis=0     ) < 1e-3     )     )
								self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase__       ,		axis=0     ) - 1     ) < 1e-3     )     )



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

								# Test not batched input
								SCREAMING_SNAKE_CASE__     							=				feat_extract(speech_inputs[0]       ,		return_tensors="np"     ).input_values
								SCREAMING_SNAKE_CASE__     							=				feat_extract(np_speech_inputs[0]       ,		return_tensors="np"     ).input_values
								self.assertTrue(np.allclose(UpperCAmelCase__       ,		UpperCAmelCase__       ,		atol=1e-3     )     )

								# Test batched
								SCREAMING_SNAKE_CASE__     							=				feat_extract(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								SCREAMING_SNAKE_CASE__     							=				feat_extract(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												self.assertTrue(np.allclose(UpperCAmelCase__       ,		UpperCAmelCase__       ,		atol=1e-3     )     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in range(800       ,		1400       ,		200     )]

								SCREAMING_SNAKE_CASE__     							=				["longest", "max_length", "do_not_pad"]
								SCREAMING_SNAKE_CASE__     							=				[None, 1600, None]
								for max_length, padding in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				feat_extract(UpperCAmelCase__       ,		padding=UpperCAmelCase__       ,		max_length=UpperCAmelCase__       ,		return_tensors="np"     )
												SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				range(800       ,		1400       ,		200     )
								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in lengths]

								SCREAMING_SNAKE_CASE__     							=				["longest", "max_length", "do_not_pad"]
								SCREAMING_SNAKE_CASE__     							=				[None, 1600, None]

								for max_length, padding in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				feat_extract(UpperCAmelCase__       ,		max_length=UpperCAmelCase__       ,		padding=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in range(800       ,		1400       ,		200     )]
								SCREAMING_SNAKE_CASE__     							=				feat_extract(
								    UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		max_length=1000       ,		padding="max_length"       ,		return_tensors="np"     )
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in range(800       ,		1400       ,		200     )]
								SCREAMING_SNAKE_CASE__     							=				feat_extract(
								    UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		max_length=1000       ,		padding="longest"       ,		return_tensors="np"     )
								SCREAMING_SNAKE_CASE__     							=				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)     )

								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in range(800       ,		1400       ,		200     )]
								SCREAMING_SNAKE_CASE__     							=				feat_extract(
								    UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		max_length=2000       ,		padding="longest"       ,		return_tensors="np"     )
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				np.random.rand(100     ).astype(np.floataa     )
								SCREAMING_SNAKE_CASE__     							=				np_speech_inputs.tolist()

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



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

								# Test feature size
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(audio_target=UpperCAmelCase__       ,		padding=UpperCAmelCase__       ,		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
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(speech_inputs[0]       ,		return_tensors="np"     ).input_values
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(np_speech_inputs[0]       ,		return_tensors="np"     ).input_values
								self.assertTrue(np.allclose(UpperCAmelCase__       ,		UpperCAmelCase__       ,		atol=1e-3     )     )

								# Test batched
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												self.assertTrue(np.allclose(UpperCAmelCase__       ,		UpperCAmelCase__       ,		atol=1e-3     )     )

								# Test 2-D numpy arrays are batched.
								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in (800, 800, 800)]
								SCREAMING_SNAKE_CASE__     							=				np.asarray(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_values
								for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												self.assertTrue(np.allclose(UpperCAmelCase__       ,		UpperCAmelCase__       ,		atol=1e-3     )     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target()
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_dict     )
								SCREAMING_SNAKE_CASE__     							=				feat_extract.model_input_names[0]

								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}     )

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

								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}       ,		tensor_type="np"     )

								SCREAMING_SNAKE_CASE__     							=				processed_features[input_name]

								if len(batch_features_input.shape     ) < 3:
												SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_dict     )
								SCREAMING_SNAKE_CASE__     							=				feat_extract.model_input_names[0]

								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}       ,		tensor_type="pt"     )

								SCREAMING_SNAKE_CASE__     							=				processed_features[input_name]

								if len(batch_features_input.shape     ) < 3:
												SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_dict     )
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target()
								SCREAMING_SNAKE_CASE__     							=				feat_extract.model_input_names[0]

								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}     )

								SCREAMING_SNAKE_CASE__     							=				feat_extract.num_mel_bins  # hack!

								SCREAMING_SNAKE_CASE__     							=				feat_extract.pad(UpperCAmelCase__       ,		padding="longest"       ,		return_tensors="np"     )[input_name]
								SCREAMING_SNAKE_CASE__     							=				feat_extract.pad(UpperCAmelCase__       ,		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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_dict
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target()
								SCREAMING_SNAKE_CASE__     							=				[len(UpperCAmelCase__     ) for x in speech_inputs]
								SCREAMING_SNAKE_CASE__     							=				feat_extract.model_input_names[0]

								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}     )

								SCREAMING_SNAKE_CASE__     							=				feat_extract.num_mel_bins  # hack!

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_dict
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.feat_extract_tester.prepare_inputs_for_target()
								SCREAMING_SNAKE_CASE__     							=				[len(UpperCAmelCase__     ) for x in speech_inputs]
								SCREAMING_SNAKE_CASE__     							=				feat_extract.model_input_names[0]

								SCREAMING_SNAKE_CASE__     							=				BatchFeature({input_name: speech_inputs}     )
								SCREAMING_SNAKE_CASE__     							=				min(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				feat_extract.num_mel_bins  # hack!

								SCREAMING_SNAKE_CASE__     							=				feat_extract.pad(
								    UpperCAmelCase__       ,		padding="max_length"       ,		max_length=UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		return_tensors="np"     )
								self.assertIn("attention_mask"       ,		UpperCAmelCase__     )
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								from datasets import load_dataset

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

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



				def   lowerCAmelCase__		(						self     ):
								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
								     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
								     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
								     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
								     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
								     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]     )
								# fmt: on

								SCREAMING_SNAKE_CASE__     							=				self._load_datasamples(1     )
								SCREAMING_SNAKE_CASE__     							=				SpeechTaFeatureExtractor()
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="pt"     ).input_values
								self.assertEquals(input_values.shape       ,		(1, 9_3680)     )
								self.assertTrue(torch.allclose(input_values[0, :30]       ,		UpperCAmelCase__       ,		atol=1e-6     )     )






				def   lowerCAmelCase__		(						self     ):
								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				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

								SCREAMING_SNAKE_CASE__     							=				self._load_datasamples(1     )
								SCREAMING_SNAKE_CASE__     							=				SpeechTaFeatureExtractor()
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(audio_target=UpperCAmelCase__       ,		return_tensors="pt"     ).input_values
								self.assertEquals(input_values.shape       ,		(1, 366, 80)     )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  ) if (len(lowerCamelCase_  ) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8  )						,					"Stack".center(lowerCamelCase_  )						,					"Postfix".center(lowerCamelCase_  )						,					sep=" | "						,					)
				print("-" * (print_width * 3 + 7)  )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase_  )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase_  )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase_  ) == 0:
																stack.append(lowerCamelCase_  )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase_  ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()  )  # pop stack & add to Postfix
																stack.append(lowerCamelCase_  )  # push x to stack

								print(
								    x.center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				while len(lowerCamelCase_  ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()  )  # pop stack & add to Postfix
								print(
								    " ".center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				return "".join(lowerCamelCase_  )  # return Postfix as str


def __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(infix[::-1]  )  # reverse the infix equation

				for i in range(len(lowerCamelCase_  )  ):
								if infix[i] == "(":
												SCREAMING_SNAKE_CASE__     							=				")"  # change "(" to ")"
								elif infix[i] == ")":
												SCREAMING_SNAKE_CASE__     							=				"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase_  )  ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	_lowerCamelCase     =  input('\nEnter an Infix Equation = ')  # Input an Infix equation
	_lowerCamelCase     =  ''.join(Infix.split())  # Remove spaces from the input
	print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
