
'''simple docstring'''





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	bool:







      """simple docstring"""
      return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()













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

lowerCAmelCase_    =				random.Random()






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=1.0 ,				_UpperCamelCase=None ,				_UpperCamelCase=None )		->						List[Any]:






				"""simple docstring"""


				if rng is None:
								snake_case_						:    str				=  global_rng

				snake_case_						:    Any				=  []
				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     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=400					,							__magic_name__=2000					,							__magic_name__=10					,							__magic_name__=160					,							__magic_name__=8					,							__magic_name__=0.0					,							__magic_name__=4000					,							__magic_name__=False					,							__magic_name__=True					,							)     ->   List[str]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							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					,							__magic_name__=False					,							__magic_name__=False    )     ->   Optional[Any]:


							'''simple docstring'''



							def _flatten(__magic_name__    ):
											return list(itertools.chain(*__magic_name__    )    )

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




@require_torch
@require_torchaudio
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      WhisperFeatureExtractor if is_speech_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    List[str]				=  WhisperFeatureExtractionTester(self    )






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    str				=  self.feature_extraction_class(**self.feat_extract_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_						:    Union[str, Any]				=  feat_extract_first.save_pretrained(__magic_name__    )[0]
											check_json_file_has_correct_format(__magic_name__    )
											snake_case_						:    List[Any]				=  self.feature_extraction_class.from_pretrained(__magic_name__    )

							snake_case_						:    Optional[int]				=  feat_extract_first.to_dict()
							snake_case_						:    Dict				=  feat_extract_second.to_dict()
							snake_case_						:    List[str]				=  feat_extract_first.mel_filters
							snake_case_						:    Union[str, Any]				=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__    )    )
							self.assertEqual(__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.feature_extraction_class(**self.feat_extract_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_						:    List[Any]				=  os.path.join(__magic_name__					,							'''feat_extract.json'''    )
											feat_extract_first.to_json_file(__magic_name__    )
											snake_case_						:    Optional[int]				=  self.feature_extraction_class.from_json_file(__magic_name__    )

							snake_case_						:    int				=  feat_extract_first.to_dict()
							snake_case_						:    Optional[int]				=  feat_extract_second.to_dict()
							snake_case_						:    Union[str, Any]				=  feat_extract_first.mel_filters
							snake_case_						:    str				=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__    )    )
							self.assertEqual(__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							# create three inputs of length 800, 1000, and 1200
							snake_case_						:    Any				=  [floats_list((1, x)    )[0] for x in range(800					,							1400					,							200    )]
							snake_case_						:    str				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs]

							# Test feature size
							snake_case_						:    str				=  feature_extractor(__magic_name__					,							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
							snake_case_						:    Dict				=  feature_extractor(speech_inputs[0]					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Optional[int]				=  feature_extractor(np_speech_inputs[0]					,							return_tensors='''np'''    ).input_features
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test batched
							snake_case_						:    int				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Union[str, Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test 2-D numpy arrays are batched.
							snake_case_						:    Union[str, Any]				=  [floats_list((1, x)    )[0] for x in (800, 800, 800)]
							snake_case_						:    List[str]				=  np.asarray(__magic_name__    )
							snake_case_						:    List[Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Dict				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test truncation required
							snake_case_						:    Any				=  [floats_list((1, x)    )[0] for x in range(200					,							(feature_extractor.n_samples + 500)					,							200    )]
							snake_case_						:    Union[str, Any]				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs]

							snake_case_						:    Tuple				=  [x[: feature_extractor.n_samples] for x in speech_inputs]
							snake_case_						:    Optional[Any]				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs_truncated]

							snake_case_						:    Any				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    List[Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							import torch

							snake_case_						:    str				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							snake_case_						:    Union[str, Any]				=  np.random.rand(100					,							32    ).astype(np.floataa    )
							snake_case_						:    Dict				=  np_speech_inputs.tolist()

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






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



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

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






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    str				=  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

							snake_case_						:    List[Any]				=  self._load_datasamples(1    )
							snake_case_						:    Union[str, Any]				=  WhisperFeatureExtractor()
							snake_case_						:    Union[str, Any]				=  feature_extractor(__magic_name__					,							return_tensors='''pt'''    ).input_features
							self.assertEqual(input_features.shape					,							(1, 80, 3000)    )
							self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__magic_name__					,							atol=1e-4    )    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							snake_case_						:    Optional[int]				=  self._load_datasamples(1    )[0]
							snake_case_						:    List[str]				=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
							snake_case_						:    Optional[Any]				=  feat_extract.zero_mean_unit_var_norm([audio]					,							attention_mask=__magic_name__    )[0]

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