



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

UpperCamelCase__ =						random.Random()






def  UpperCAmelCase__    (    lowercase__						,       lowercase__=1.0						,       lowercase__=None						,       lowercase__=None   ) ->      str:
						if rng is None:
												__lowercase					=				global_rng

						__lowercase					=				[]
						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	:       List[Any]			,     lowercase	:       Tuple			,     lowercase	:       Union[str, Any]=7			,     lowercase	:       List[Any]=400			,     lowercase	:       Any=2_000			,     lowercase	:       Optional[int]=24			,     lowercase	:       Any=24			,     lowercase	:       List[str]=0.0			,     lowercase	:       Dict=16_000			,     lowercase	:       Union[str, Any]=True			,     lowercase	:       Dict=True			,     )						->					Any:
									"""simple docstring"""







									__lowercase					=				parent
									__lowercase					=				batch_size
									__lowercase					=				min_seq_length
									__lowercase					=				max_seq_length
									__lowercase					=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__lowercase					=				feature_size
									__lowercase					=				num_mel_bins
									__lowercase					=				padding_value
									__lowercase					=				sampling_rate
									__lowercase					=				return_attention_mask
									__lowercase					=				do_normalize





			def 		snake_case__			(				self	:       Optional[int]   )						->					Any:
									"""simple docstring"""







									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 		snake_case__			(				self	:       List[str]			,     lowercase	:       Tuple=False			,     lowercase	:       int=False   )						->					Optional[Any]:
									"""simple docstring"""







									def _flatten(lowercase	:       Optional[Any]   ):
															return list(itertools.chain(*lowercase   )   )

									if equal_length:
															__lowercase					=				[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
									else:
															# make sure that inputs increase in size
															__lowercase					=				[
															    floats_list((x, self.feature_size)   )
															    for x in range(self.min_seq_length			,     self.max_seq_length			,     self.seq_length_diff   )
															]
									if numpify:
															__lowercase					=				[np.asarray(lowercase   ) for x in speech_inputs]
									return speech_inputs





@require_torch
@require_torchaudio
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			SpeechaTextFeatureExtractor if is_speech_available() else None





			def 		snake_case__			(				self	:       Dict   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				SpeechaTextFeatureExtractionTester(self   )





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       Optional[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self.assertTrue(np.all(np.mean(lowercase			,     axis=0   ) < 1E-3   )   )
									self.assertTrue(np.all(np.abs(np.var(lowercase			,     axis=0   ) - 1   ) < 1E-3   )   )





			def 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									# create three inputs of length 800, 1000, and 1200
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				[np.asarray(lowercase   ) for speech_input in speech_inputs]

									# Test feature size
									__lowercase					=				feature_extractor(lowercase			,     padding=lowercase			,     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
									__lowercase					=				feature_extractor(speech_inputs[0]			,     return_tensors="""np"""   ).input_features
									__lowercase					=				feature_extractor(np_speech_inputs[0]			,     return_tensors="""np"""   ).input_features
									self.assertTrue(np.allclose(lowercase			,     lowercase			,     atol=1E-3   )   )

									# Test batched
									__lowercase					=				feature_extractor(lowercase			,     return_tensors="""np"""   ).input_features
									__lowercase					=				feature_extractor(lowercase			,     return_tensors="""np"""   ).input_features
									for enc_seq_a, enc_seq_a in zip(lowercase			,     lowercase   ):
															self.assertTrue(np.allclose(lowercase			,     lowercase			,     atol=1E-3   )   )

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





			def 		snake_case__			(				self	:       List[str]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]

									__lowercase					=				["""longest""", """max_length""", """do_not_pad"""]
									__lowercase					=				[None, 16, None]
									for max_length, padding in zip(lowercase			,     lowercase   ):
															__lowercase					=				feature_extractor(
															    lowercase			,     padding=lowercase			,     max_length=lowercase			,     return_attention_mask=lowercase   )
															__lowercase					=				inputs.input_features
															__lowercase					=				inputs.attention_mask
															__lowercase					=				[np.sum(lowercase   ) 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 		snake_case__			(				self	:       Any   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]

									__lowercase					=				["""longest""", """max_length""", """do_not_pad"""]
									__lowercase					=				[None, 16, None]
									for max_length, padding in zip(lowercase			,     lowercase   ):
															__lowercase					=				feature_extractor(
															    lowercase			,     max_length=lowercase			,     padding=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase   )
															__lowercase					=				inputs.input_features
															__lowercase					=				inputs.attention_mask
															__lowercase					=				[np.sum(lowercase   ) 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 		snake_case__			(				self	:       str   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""max_length"""			,     max_length=4			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""longest"""			,     max_length=4			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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)   )

									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""longest"""			,     max_length=16			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									import torch

									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				np.random.rand(100			,     32   ).astype(np.floataa   )
									__lowercase					=				np_speech_inputs.tolist()

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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Union[str, Any]   )						->					int:
									"""simple docstring"""







									from datasets import load_dataset

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

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







			def 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									__lowercase					=				np.array([
									    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
									    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
									    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
									]   )
									# fmt: on

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










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
			print(F"""{solution() = }""")





