


"""simple docstring"""






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase     =  random.Random()


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Optional[int]=1.0						,					lowerCamelCase_  :      List[Any]=None						,					lowerCamelCase_  :      Dict=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
@require_torchaudio
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=400       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=24       ,		UpperCAmelCase__=24       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=1_6000       ,		UpperCAmelCase__=True       ,		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__     							=				num_mel_bins
								SCREAMING_SNAKE_CASE__     							=				padding_value
								SCREAMING_SNAKE_CASE__     							=				sampling_rate
								SCREAMING_SNAKE_CASE__     							=				return_attention_mask
								SCREAMING_SNAKE_CASE__     							=				do_normalize



				def   lowerCAmelCase__		(						self     ):
								return {
								    "feature_size": self.feature_size,
								    "num_mel_bins": self.num_mel_bins,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								    "do_normalize": self.do_normalize,
								}






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

								if equal_length:
												SCREAMING_SNAKE_CASE__     							=				[floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
								else:
												# make sure that inputs increase in size
												SCREAMING_SNAKE_CASE__     							=				[
												    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


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





				"""simple docstring"""







				_lowerCAmelCase      :      List[str]      							=					SpeechaTextFeatureExtractor if is_speech_available() else None



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				SpeechaTextFeatureExtractionTester(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 feature size
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		padding=UpperCAmelCase__       ,		return_tensors="np"     ).input_features
								self.assertTrue(input_features.ndim == 3     )
								self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_features
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_features
								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_features
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="np"     ).input_features
								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, 16, None]
								for max_length, padding in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				feature_extractor(
												    UpperCAmelCase__       ,		padding=UpperCAmelCase__       ,		max_length=UpperCAmelCase__       ,		return_attention_mask=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				inputs.input_features
												SCREAMING_SNAKE_CASE__     							=				inputs.attention_mask
												SCREAMING_SNAKE_CASE__     							=				[np.sum(UpperCAmelCase__     ) for x in attention_mask]

												self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
												self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
												self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[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__     							=				["longest", "max_length", "do_not_pad"]
								SCREAMING_SNAKE_CASE__     							=				[None, 16, None]
								for max_length, padding in zip(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				feature_extractor(
												    UpperCAmelCase__       ,		max_length=UpperCAmelCase__       ,		padding=UpperCAmelCase__       ,		return_tensors="np"       ,		return_attention_mask=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				inputs.input_features
												SCREAMING_SNAKE_CASE__     							=				inputs.attention_mask
												SCREAMING_SNAKE_CASE__     							=				[np.sum(UpperCAmelCase__     ) for x in attention_mask]

												self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
												self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6     )
												self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
												self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6     )
												self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[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__     							=				feature_extractor(
								    UpperCAmelCase__       ,		padding="max_length"       ,		max_length=4       ,		truncation=UpperCAmelCase__       ,		return_tensors="np"       ,		return_attention_mask=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				inputs.input_features
								SCREAMING_SNAKE_CASE__     							=				inputs.attention_mask
								SCREAMING_SNAKE_CASE__     							=				np.sum(attention_mask == 1       ,		axis=1     )

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
								self._check_zero_mean_unit_variance(input_features[1]     )
								self._check_zero_mean_unit_variance(input_features[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__     							=				feature_extractor(
								    UpperCAmelCase__       ,		padding="longest"       ,		max_length=4       ,		truncation=UpperCAmelCase__       ,		return_tensors="np"       ,		return_attention_mask=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				inputs.input_features
								SCREAMING_SNAKE_CASE__     							=				inputs.attention_mask
								SCREAMING_SNAKE_CASE__     							=				np.sum(attention_mask == 1       ,		axis=1     )

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
								self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
								self._check_zero_mean_unit_variance(input_features[2]     )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertEqual(input_features.shape       ,		(3, 4, 24)     )

								SCREAMING_SNAKE_CASE__     							=				[floats_list((1, x)     )[0] for x in range(800       ,		1400       ,		200     )]
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(
								    UpperCAmelCase__       ,		padding="longest"       ,		max_length=16       ,		truncation=UpperCAmelCase__       ,		return_tensors="np"       ,		return_attention_mask=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				inputs.input_features
								SCREAMING_SNAKE_CASE__     							=				inputs.attention_mask
								SCREAMING_SNAKE_CASE__     							=				np.sum(attention_mask == 1       ,		axis=1     )

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
								self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
								self._check_zero_mean_unit_variance(input_features[2]     )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertEqual(input_features.shape       ,		(3, 6, 24)     )



				def   lowerCAmelCase__		(						self     ):
								import torch

								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				np.random.rand(100       ,		32     ).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_features": inputs}]       ,		return_tensors="np"     )
												self.assertTrue(np_processed.input_features.dtype == np.floataa     )
												SCREAMING_SNAKE_CASE__     							=				feature_extractor.pad([{"input_features": inputs}]       ,		return_tensors="pt"     )
												self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )



				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__     							=				np.array([
								    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
								    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
								    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
								]     )
								# fmt: on

								SCREAMING_SNAKE_CASE__     							=				self._load_datasamples(1     )
								SCREAMING_SNAKE_CASE__     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								SCREAMING_SNAKE_CASE__     							=				feature_extractor(UpperCAmelCase__       ,		return_tensors="pt"     ).input_features
								self.assertEquals(input_features.shape       ,		(1, 584, 24)     )
								self.assertTrue(np.allclose(input_features[0, 0, :30]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
