






'''simple docstring'''







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import WhisperFeatureExtractor

if is_torch_available():
			import torch

_A	:			Union[str, Any]         =random.Random()





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=1.0				,			UpperCamelCase=None				,			UpperCamelCase=None      )      ->       int:
							if rng is None:
														lowerCamelCase__					:	Dict         =       global_rng

							lowerCamelCase__					:	str         =       []
							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 _lowercase	(				unittest.TestCase		):




							def __init__(							self:				int			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Tuple=7			,							UpperCamelCase__:				List[str]=400			,							UpperCamelCase__:				Union[str, Any]=2_000			,							UpperCamelCase__:				Optional[Any]=10			,							UpperCamelCase__:				Any=160			,							UpperCamelCase__:				Union[str, Any]=8			,							UpperCamelCase__:				int=0.0			,							UpperCamelCase__:				Optional[Any]=4_000			,							UpperCamelCase__:				List[str]=False			,							UpperCamelCase__:				str=True			,							):
														lowerCamelCase__					:	List[Any]         =       parent
														lowerCamelCase__					:	Union[str, Any]         =       batch_size
														lowerCamelCase__					:	str         =       min_seq_length
														lowerCamelCase__					:	List[Any]         =       max_seq_length
														lowerCamelCase__					:	Union[str, Any]         =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														lowerCamelCase__					:	List[Any]         =       padding_value
														lowerCamelCase__					:	Any         =       sampling_rate
														lowerCamelCase__					:	Dict         =       return_attention_mask
														lowerCamelCase__					:	Union[str, Any]         =       do_normalize
														lowerCamelCase__					:	Optional[int]         =       feature_size
														lowerCamelCase__					:	Union[str, Any]         =       chunk_length
														lowerCamelCase__					:	Dict         =       hop_length




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return {
														    "feature_size": self.feature_size,
														    "hop_length": self.hop_length,
														    "chunk_length": self.chunk_length,
														    "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:				List[Any]			,							UpperCamelCase__:				List[Any]=False			,							UpperCamelCase__:				int=False     ):
														def _flatten(UpperCamelCase__:				List[str]     ):
																					return list(itertools.chain(*UpperCamelCase__     )     )

														if equal_length:
																					lowerCamelCase__					:	List[str]         =       [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
														else:
																					# make sure that inputs increase in size
																					lowerCamelCase__					:	Union[str, Any]         =       [
																					    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__					:	Any         =       [np.asarray(UpperCamelCase__     ) for x in speech_inputs]
														return speech_inputs



@require_torch
@require_torchaudio
class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       WhisperFeatureExtractor if is_speech_available() else None




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Union[str, Any]         =       WhisperFeatureExtractionTester(self     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Dict         =       self.feature_extraction_class(**self.feat_extract_dict     )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					lowerCamelCase__					:	List[Any]         =       feat_extract_first.save_pretrained(UpperCamelCase__     )[0]
																					check_json_file_has_correct_format(UpperCamelCase__     )
																					lowerCamelCase__					:	Dict         =       self.feature_extraction_class.from_pretrained(UpperCamelCase__     )

														lowerCamelCase__					:	int         =       feat_extract_first.to_dict()
														lowerCamelCase__					:	List[str]         =       feat_extract_second.to_dict()
														lowerCamelCase__					:	List[str]         =       feat_extract_first.mel_filters
														lowerCamelCase__					:	Dict         =       feat_extract_second.mel_filters
														self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__     )     )
														self.assertEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Tuple         =       self.feature_extraction_class(**self.feat_extract_dict     )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					lowerCamelCase__					:	Tuple         =       os.path.join(UpperCamelCase__			,							"""feat_extract.json"""     )
																					feat_extract_first.to_json_file(UpperCamelCase__     )
																					lowerCamelCase__					:	int         =       self.feature_extraction_class.from_json_file(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       feat_extract_first.to_dict()
														lowerCamelCase__					:	Dict         =       feat_extract_second.to_dict()
														lowerCamelCase__					:	Union[str, Any]         =       feat_extract_first.mel_filters
														lowerCamelCase__					:	Tuple         =       feat_extract_second.mel_filters
														self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__     )     )
														self.assertEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# Tests that all call wrap to encode_plus and batch_encode_plus
														lowerCamelCase__					:	Any         =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
														# create three inputs of length 800, 1000, and 1200
														lowerCamelCase__					:	int         =       [floats_list((1, x)     )[0] for x in range(800			,							1_400			,							200     )]
														lowerCamelCase__					:	str         =       [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

														# Test feature size
														lowerCamelCase__					:	Dict         =       feature_extractor(UpperCamelCase__			,							padding="""max_length"""			,							return_tensors="""np"""     ).input_features
														self.assertTrue(input_features.ndim == 3     )
														self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames     )
														self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size     )

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

														# Test batched
														lowerCamelCase__					:	List[Any]         =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""     ).input_features
														lowerCamelCase__					:	Optional[Any]         =       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.
														lowerCamelCase__					:	List[str]         =       [floats_list((1, x)     )[0] for x in (800, 800, 800)]
														lowerCamelCase__					:	str         =       np.asarray(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""     ).input_features
														lowerCamelCase__					:	List[str]         =       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 truncation required
														lowerCamelCase__					:	Union[str, Any]         =       [floats_list((1, x)     )[0] for x in range(200			,							(feature_extractor.n_samples + 500)			,							200     )]
														lowerCamelCase__					:	int         =       [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

														lowerCamelCase__					:	Dict         =       [x[: feature_extractor.n_samples] for x in speech_inputs]
														lowerCamelCase__					:	Dict         =       [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs_truncated]

														lowerCamelCase__					:	str         =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""     ).input_features
														lowerCamelCase__					:	Union[str, Any]         =       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:				Optional[Any]     ):
														import torch

														lowerCamelCase__					:	int         =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
														lowerCamelCase__					:	Dict         =       np.random.rand(100			,							32     ).astype(np.floataa     )
														lowerCamelCase__					:	Optional[int]         =       np_speech_inputs.tolist()

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




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	List[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 speech_samples]




							def 		lowerCamelCase_				(							self:				str     ):
														# fmt: off
														lowerCamelCase__					:	Any         =       torch.tensor(
														    [
														        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
														        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
														        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
														        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
														    ]     )
														# fmt: on

														lowerCamelCase__					:	Optional[int]         =       self._load_datasamples(1     )
														lowerCamelCase__					:	Optional[Any]         =       WhisperFeatureExtractor()
														lowerCamelCase__					:	Dict         =       feature_extractor(UpperCamelCase__			,							return_tensors="""pt"""     ).input_features
														self.assertEqual(input_features.shape			,							(1, 80, 3_000)     )
														self.assertTrue(torch.allclose(input_features[0, 0, :30]			,							UpperCamelCase__			,							atol=1e-4     )     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Dict         =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
														lowerCamelCase__					:	Dict         =       self._load_datasamples(1     )[0]
														lowerCamelCase__					:	List[str]         =       ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
														lowerCamelCase__					:	Any         =       feat_extract.zero_mean_unit_var_norm([audio]			,							attention_mask=UpperCamelCase__     )[0]

														self.assertTrue(np.all(np.mean(UpperCamelCase__     ) < 1e-3     )     )
														self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ) - 1     ) < 1e-3     )     )









'''simple docstring'''







from collections.abc import Sequence





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       float:
							return sum(c * (x**i) for i, c in enumerate(UpperCamelCase      )      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       float:
							lowerCamelCase__					:	str         =       0.0
							for coeff in reversed(UpperCamelCase      ):
														lowerCamelCase__					:	Optional[int]         =       result * x + coeff
							return result


if __name__ == "__main__":
			_A	:			Any         =(0.0, 0.0, 5.0, 9.3, 7.0)
			_A	:			Optional[Any]         =10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))

