



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     :							Dict  							=				random.Random()

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=1.0    ,   lowerCamelCase_=None    ,   lowerCamelCase_=None    ) ->       str:
						if rng is None:
												UpperCAmelCase         =     global_rng

						UpperCAmelCase         =     []
						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  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int=7				,		UpperCamelCase__      :     Optional[Any]=4_00				,		UpperCamelCase__      :     Dict=20_00				,		UpperCamelCase__      :     Any=24				,		UpperCamelCase__      :     int=24				,		UpperCamelCase__      :     List[str]=0.0				,		UpperCamelCase__      :     Dict=1_60_00				,		UpperCamelCase__      :     int=True				,		UpperCamelCase__      :     Dict=True				,		)				->					str:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Optional[int]=False      )				->					Optional[Any]:
								'''simple docstring'''







								def _flatten(UpperCamelCase__      :     str      ):
														return list(itertools.chain(*UpperCamelCase__      )      )

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





@require_torch
@require_torchaudio
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =SpeechaTextFeatureExtractor if is_speech_available() else None






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     SpeechaTextFeatureExtractionTester(self      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str      )				->					Dict:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								# create three inputs of length 800, 1000, and 1200
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     [np.asarray(UpperCamelCase__      ) for speech_input in speech_inputs]

								# Test feature size
								UpperCAmelCase         =     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
								UpperCAmelCase         =     feature_extractor(speech_inputs[0]				,		return_tensors="np"      ).input_features
								UpperCAmelCase         =     feature_extractor(np_speech_inputs[0]				,		return_tensors="np"      ).input_features
								self.assertTrue(np.allclose(UpperCamelCase__				,		UpperCamelCase__				,		atol=1e-3      )      )

								# Test batched
								UpperCAmelCase         =     feature_extractor(UpperCamelCase__				,		return_tensors="np"      ).input_features
								UpperCAmelCase         =     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.
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
								UpperCAmelCase         =     np.asarray(UpperCamelCase__      )
								UpperCAmelCase         =     feature_extractor(UpperCamelCase__				,		return_tensors="np"      ).input_features
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]

								UpperCAmelCase         =     ["longest", "max_length", "do_not_pad"]
								UpperCAmelCase         =     [None, 16, None]
								for max_length, padding in zip(UpperCamelCase__				,		UpperCamelCase__      ):
														UpperCAmelCase         =     feature_extractor(
														    UpperCamelCase__				,		padding=UpperCamelCase__				,		max_length=UpperCamelCase__				,		return_attention_mask=UpperCamelCase__      )
														UpperCAmelCase         =     inputs.input_features
														UpperCAmelCase         =     inputs.attention_mask
														UpperCAmelCase         =     [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 SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]

								UpperCAmelCase         =     ["longest", "max_length", "do_not_pad"]
								UpperCAmelCase         =     [None, 16, None]
								for max_length, padding in zip(UpperCamelCase__				,		UpperCamelCase__      ):
														UpperCAmelCase         =     feature_extractor(
														    UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding=UpperCamelCase__				,		return_tensors="np"				,		return_attention_mask=UpperCamelCase__      )
														UpperCAmelCase         =     inputs.input_features
														UpperCAmelCase         =     inputs.attention_mask
														UpperCAmelCase         =     [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 SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feature_extractor(
								    UpperCamelCase__				,		padding="max_length"				,		max_length=4				,		truncation=UpperCamelCase__				,		return_tensors="np"				,		return_attention_mask=UpperCamelCase__				,		)
								UpperCAmelCase         =     inputs.input_features
								UpperCAmelCase         =     inputs.attention_mask
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feature_extractor(
								    UpperCamelCase__				,		padding="longest"				,		max_length=4				,		truncation=UpperCamelCase__				,		return_tensors="np"				,		return_attention_mask=UpperCamelCase__				,		)
								UpperCAmelCase         =     inputs.input_features
								UpperCAmelCase         =     inputs.attention_mask
								UpperCAmelCase         =     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)      )

								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feature_extractor(
								    UpperCamelCase__				,		padding="longest"				,		max_length=16				,		truncation=UpperCamelCase__				,		return_tensors="np"				,		return_attention_mask=UpperCamelCase__				,		)
								UpperCAmelCase         =     inputs.input_features
								UpperCAmelCase         =     inputs.attention_mask
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								import torch

								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     np.random.rand(1_00				,		32      ).astype(np.floataa      )
								UpperCAmelCase         =     np_speech_inputs.tolist()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Any      )				->					List[str]:
								'''simple docstring'''







								from datasets import load_dataset

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

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







		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     np.array([
								    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
								    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
								    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
								]      )
								# fmt: on

								UpperCAmelCase         =     self._load_datasamples(1      )
								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     feature_extractor(UpperCamelCase__				,		return_tensors="pt"      ).input_features
								self.assertEquals(input_features.shape				,		(1, 5_84, 24)      )
								self.assertTrue(np.allclose(input_features[0, 0, :30]				,		UpperCamelCase__				,		atol=1e-4      )      )








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


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



