





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			)		->						int:






				"""simple docstring"""


				if rng is None:
								snake_case_						:   Tuple           =  global_rng

				snake_case_						:   List[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  __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 ,							)					->       Dict:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       Tuple:


							'''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[int]:


							'''simple docstring'''



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

							if equal_length:
											snake_case_						:   Dict           =  [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_						:   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:
											snake_case_						:   Optional[int]           =  [np.asarray(__magic_name__   ) for x in speech_inputs]
							return speech_inputs




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






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  WhisperFeatureExtractionTester(self   )






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


							'''simple docstring'''



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

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

							snake_case_						:   List[str]           =  feat_extract_first.to_dict()
							snake_case_						:   Tuple           =  feat_extract_second.to_dict()
							snake_case_						:   Any           =  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   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  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_						:   Any           =  self.feature_extraction_class.from_json_file(__magic_name__   )

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

							# Test feature size
							snake_case_						:   int           =  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_						:   Any           =  feature_extractor(speech_inputs[0] ,							return_tensors='''np'''   ).input_features
							snake_case_						:   List[str]           =  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_						:   List[str]           =  feature_extractor(__magic_name__ ,							return_tensors='''np'''   ).input_features
							snake_case_						:   Optional[int]           =  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_						:   Dict           =  [floats_list((1, x)   )[0] for x in (800, 800, 800)]
							snake_case_						:   Optional[Any]           =  np.asarray(__magic_name__   )
							snake_case_						:   str           =  feature_extractor(__magic_name__ ,							return_tensors='''np'''   ).input_features
							snake_case_						:   List[str]           =  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_						:   Dict           =  [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_						:   str           =  [x[: feature_extractor.n_samples] for x in speech_inputs]
							snake_case_						:   Dict           =  [np.asarray(__magic_name__   ) for speech_input in speech_inputs_truncated]

							snake_case_						:   Tuple           =  feature_extractor(__magic_name__ ,							return_tensors='''np'''   ).input_features
							snake_case_						:   int           =  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   )					->       Union[str, Any]:


							'''simple docstring'''



							import torch

							snake_case_						:   Dict           =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							snake_case_						:   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_						:   List[Any]           =  feature_extractor.pad([{'''input_features''': inputs}] ,							return_tensors='''np'''   )
											self.assertTrue(np_processed.input_features.dtype == np.floataa   )
											snake_case_						:   List[str]           =  feature_extractor.pad([{'''input_features''': inputs}] ,							return_tensors='''pt'''   )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



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

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






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Dict           =  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_						:   Optional[Any]           =  self._load_datasamples(1   )
							snake_case_						:   List[str]           =  WhisperFeatureExtractor()
							snake_case_						:   List[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   )					->       Any:


							'''simple docstring'''



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







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				snake_case_						:   List[Any]           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   Tuple           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Dict           =  1
								for i in range(0			,    len(_UpperCamelCase			)			):
												total *= numbers[i]

								snake_case_						:   str           =  str(_UpperCamelCase			)

								steps += 1
				return steps






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   List[str]           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Optional[int]           =  0
								for i in range(0			,    len(_UpperCamelCase			)			):
												total += numbers[i]

								snake_case_						:   Tuple           =  str(_UpperCamelCase			)

								steps += 1
				return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()
