"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
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


UpperCAmelCase       :    Tuple									=			random.Random()



def 				__a       (			_lowercase							, _lowercase=1.0							, _lowercase=None							, _lowercase=None						):







					"""simple docstring"""





					if rng is None:
										lowerCamelCase__		:   List[str]  				=					global_rng

					lowerCamelCase__		:   Optional[int]  				=					[]
					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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				def __init__(    self    :str      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :List[str]=7      ,__UpperCAmelCase    :Dict=4_00      ,__UpperCAmelCase    :Any=20_00      ,__UpperCAmelCase    :Optional[int]=1      ,__UpperCAmelCase    :List[str]=0.0      ,__UpperCAmelCase    :List[Any]=2_40_00      ,__UpperCAmelCase    :Any=True      ,)      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					parent
									lowerCamelCase__		:   Tuple  				=					batch_size
									lowerCamelCase__		:   List[Any]  				=					min_seq_length
									lowerCamelCase__		:   Union[str, Any]  				=					max_seq_length
									lowerCamelCase__		:   List[str]  				=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									lowerCamelCase__		:   Optional[int]  				=					feature_size
									lowerCamelCase__		:   int  				=					padding_value
									lowerCamelCase__		:   Union[str, Any]  				=					sampling_rate
									lowerCamelCase__		:   List[Any]  				=					return_attention_mask







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}







				def   lowercase_		(    self    :List[Any]      ,__UpperCAmelCase    :int=False      ,__UpperCAmelCase    :Union[str, Any]=False  )      ->					List[Any]:




									"""simple docstring"""



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

									if equal_length:
														lowerCamelCase__		:   Any  				=					floats_list((self.batch_size, self.max_seq_length)  )
									else:
														# make sure that inputs increase in size
														lowerCamelCase__		:   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:
														lowerCamelCase__		:   Dict  				=					[np.asarray(__UpperCAmelCase  ) for x in audio_inputs]

									return audio_inputs





@require_torch
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      EncodecFeatureExtractor







				def   lowercase_		(    self    :Tuple  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					EnCodecFeatureExtractionTester(self  )







				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									lowerCamelCase__		:   Tuple  				=					[floats_list((1, x)  )[0] for x in range(8_00      ,14_00      ,2_00  )]
									lowerCamelCase__		:   Union[str, Any]  				=					[np.asarray(__UpperCAmelCase  ) for audio_input in audio_inputs]

									# Test not batched input
									lowerCamelCase__		:   List[Any]  				=					feat_extract(audio_inputs[0]      ,return_tensors='''np'''  ).input_values
									lowerCamelCase__		:   Optional[int]  				=					feat_extract(np_audio_inputs[0]      ,return_tensors='''np'''  ).input_values
									self.assertTrue(np.allclose(__UpperCAmelCase      ,__UpperCAmelCase      ,atol=1E-3  )  )

									# Test batched
									lowerCamelCase__		:   List[str]  				=					feat_extract(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''np'''  ).input_values
									lowerCamelCase__		:   str  				=					feat_extract(__UpperCAmelCase      ,padding=__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   lowercase_		(    self    :str  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									lowerCamelCase__		:   Optional[Any]  				=					np.random.rand(1_00  ).astype(np.floataa  )
									lowerCamelCase__		:   Any  				=					np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
														lowerCamelCase__		:   Optional[int]  				=					feature_extractor.pad([{'''input_values''': inputs}]      ,return_tensors='''np'''  )
														self.assertTrue(np_processed.input_values.dtype == np.floataa  )
														lowerCamelCase__		:   Union[str, Any]  				=					feature_extractor.pad([{'''input_values''': inputs}]      ,return_tensors='''pt'''  )
														self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]  )      ->					List[str]:




									"""simple docstring"""



									from datasets import load_dataset

									lowerCamelCase__		:   Union[str, Any]  				=					load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,'''clean'''      ,split='''validation'''  )
									# automatic decoding with librispeech
									lowerCamelCase__		:   List[Any]  				=					ds.sort('''id'''  ).select(range(__UpperCAmelCase  )  )[:num_samples]['''audio''']

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







				def   lowercase_		(    self    :Dict  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					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
									lowerCamelCase__		:   Any  				=					self._load_datasamples(1  )
									lowerCamelCase__		:   int  				=					EncodecFeatureExtractor()
									lowerCamelCase__		:   Tuple  				=					feature_extractor(__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(input_values.shape      ,(1, 1, 9_36_80)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,__UpperCAmelCase      ,atol=1E-6  )  )







				def   lowercase_		(    self    :str  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					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
									lowerCamelCase__		:   Optional[Any]  				=					self._load_datasamples(1  )
									lowerCamelCase__		:   Dict  				=					[np.tile(input_audio[0][None]      ,reps=(2, 1)  )]
									input_audio[0][1] *= 0.5
									lowerCamelCase__		:   int  				=					EncodecFeatureExtractor(feature_size=2  )
									lowerCamelCase__		:   Union[str, Any]  				=					feature_extractor(__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(input_values.shape      ,(1, 2, 9_36_80)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,__UpperCAmelCase      ,atol=1E-6  )  )
									self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,EXPECTED_INPUT_VALUES * 0.5      ,atol=1E-6  )  )







				def   lowercase_		(    self    :Dict  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self._load_datasamples(2  )
									# would be easier if the stride was like
									lowerCamelCase__		:   Optional[int]  				=					EncodecFeatureExtractor(feature_size=1      ,chunk_length_s=1      ,overlap=0.01  )

									# pad and trunc raise an error ?
									with self.assertRaisesRegex(
									    __UpperCAmelCase      ,'''^Both padding and truncation were set. Make sure you only set one.$'''      ,):
														lowerCamelCase__		:   Any  				=					feature_extractor(
														    __UpperCAmelCase      ,padding='''max_length'''      ,truncation=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values

									# truncate to chunk
									lowerCamelCase__		:   List[str]  				=					feature_extractor(__UpperCAmelCase      ,truncation=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(2, 1, 7_15_20)  )  # 2 chunks

									# force truncate to max_length
									lowerCamelCase__		:   Tuple  				=					feature_extractor(
									    __UpperCAmelCase      ,truncation=__UpperCAmelCase      ,max_length=4_80_00      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(2, 1, 4_80_00)  )

									# pad to chunk
									lowerCamelCase__		:   List[Any]  				=					feature_extractor(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(padded_outputs.shape      ,(2, 1, 9_52_80)  )

									# pad to chunk
									lowerCamelCase__		:   Optional[int]  				=					feature_extractor(__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(2, 1, 9_52_80)  )

									# force pad to max length
									lowerCamelCase__		:   List[str]  				=					feature_extractor(
									    __UpperCAmelCase      ,padding='''max_length'''      ,max_length=10_00_00      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(2, 1, 10_00_00)  )

									# force no pad
									with self.assertRaisesRegex(
									    __UpperCAmelCase      ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''      ,):
														lowerCamelCase__		:   Optional[Any]  				=					feature_extractor(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values

									lowerCamelCase__		:   Dict  				=					feature_extractor(input_audio[0]      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(1, 1, 9_36_80)  )

									# no pad if no chunk_length_s
									lowerCamelCase__		:   List[Any]  				=					None
									with self.assertRaisesRegex(
									    __UpperCAmelCase      ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''      ,):
														lowerCamelCase__		:   Optional[Any]  				=					feature_extractor(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values

									lowerCamelCase__		:   Union[str, Any]  				=					feature_extractor(input_audio[0]      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(1, 1, 9_36_80)  )

									# no pad if no overlap
									lowerCamelCase__		:   List[Any]  				=					2
									lowerCamelCase__		:   Tuple  				=					None
									with self.assertRaisesRegex(
									    __UpperCAmelCase      ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''      ,):
														lowerCamelCase__		:   str  				=					feature_extractor(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values

									lowerCamelCase__		:   Optional[int]  				=					feature_extractor(input_audio[0]      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  ).input_values
									self.assertEquals(truncated_outputs.shape      ,(1, 1, 9_36_80)  )







"""simple docstring"""




from collections.abc import Callable

UpperCAmelCase       :    Optional[int]									=			Callable[[float], float]  # type alias for a real -> real function



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, ):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					float(_lowercase						)
					while True:
										try:
															lowerCamelCase__		:   str  				=					prev_guess - function(_lowercase						) / derivative(_lowercase						)
										except ZeroDivisionError:
															raise ZeroDivisionError('''Could not find root'''						) from None
										if abs(prev_guess - next_guess						) < 10**-5:
															return next_guess
										lowerCamelCase__		:   List[str]  				=					next_guess



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return (x**3) - (2 * x) - 5



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))






