
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase__          =		random.Random()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=1.0       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None     )				->		str:
			if rng is None:
						lowerCAmelCase__      :      int         =		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







class      A__							(     unittest.TestCase	):






				def __init__(						self					:      Optional[int]    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]=7    ,     a					:      int=400    ,     a					:      int=2_000    ,     a					:      Dict=1    ,     a					:      int=0.0    ,     a					:      Optional[Any]=16_000    ,     a					:      int=True    ,     a					:      Tuple=True    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		parent
							lowerCAmelCase__      :      Optional[Any]         =		batch_size
							lowerCAmelCase__      :      str         =		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[Any]         =		feature_size
							lowerCAmelCase__      :      Optional[int]         =		padding_value
							lowerCAmelCase__      :      Union[str, Any]         =		sampling_rate
							lowerCAmelCase__      :      Dict         =		return_attention_mask
							lowerCAmelCase__      :      Optional[int]         =		do_normalize






				def 							_lowerCamelCase		(						self					:      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,
							    "do_normalize": self.do_normalize,
							}






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      Union[str, Any]=False    ,     a					:      str=False   ):






							'''simple docstring'''







							def _flatten(a					:      int   ):
										return list(itertools.chain(*_lowerCamelCase   )   )

							if equal_length:
										lowerCAmelCase__      :      List[Any]         =		floats_list((self.batch_size, self.max_seq_length)   )
							else:
										# make sure that inputs increase in size
										lowerCAmelCase__      :      List[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__      :      Tuple         =		[np.asarray(_lowerCamelCase   ) for x in speech_inputs]

							return speech_inputs







class      A__							(     A_						,   unittest.TestCase	):
				lowercase			    =  WavaVecaFeatureExtractor






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		WavaVecaFeatureExtractionTester(self   )






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      int   ):






							'''simple docstring'''







							self.assertTrue(np.all(np.mean(_lowerCamelCase    ,     axis=0   ) < 1E-3   )   )
							self.assertTrue(np.all(np.abs(np.var(_lowerCamelCase    ,     axis=0   ) - 1   ) < 1E-3   )   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase__      :      str         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      str         =		[np.asarray(_lowerCamelCase   ) for speech_input in speech_inputs]

							# Test not batched input
							lowerCAmelCase__      :      Union[str, Any]         =		feat_extract(speech_inputs[0]    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      int         =		feat_extract(np_speech_inputs[0]    ,     return_tensors='np'   ).input_values
							self.assertTrue(np.allclose(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1E-3   )   )

							# Test batched
							lowerCAmelCase__      :      Any         =		feat_extract(_lowerCamelCase    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Optional[int]         =		feat_extract(_lowerCamelCase    ,     return_tensors='np'   ).input_values
							for enc_seq_a, enc_seq_a in zip(_lowerCamelCase    ,     _lowerCamelCase   ):
										self.assertTrue(np.allclose(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1E-3   )   )

							# Test 2-D numpy arrays are batched.
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in (800, 800, 800)]
							lowerCAmelCase__      :      Optional[Any]         =		np.asarray(_lowerCamelCase   )
							lowerCAmelCase__      :      Any         =		feat_extract(_lowerCamelCase    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      List[Any]         =		feat_extract(_lowerCamelCase    ,     return_tensors='np'   ).input_values
							for enc_seq_a, enc_seq_a in zip(_lowerCamelCase    ,     _lowerCamelCase   ):
										self.assertTrue(np.allclose(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1E-3   )   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      Any         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]

							lowerCAmelCase__      :      Dict         =		['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__      :      Any         =		[None, 1_600, None]
							for max_length, padding in zip(_lowerCamelCase    ,     _lowerCamelCase   ):
										lowerCAmelCase__      :      str         =		feat_extract(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors='np'   )
										lowerCAmelCase__      :      Optional[Any]         =		processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]   )
										self.assertTrue(input_values[0][800:].sum() < 1E-6   )
										self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
										self.assertTrue(input_values[0][1_000:].sum() < 1E-6   )
										self._check_zero_mean_unit_variance(input_values[2][:1_200]   )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[Any]         =		range(800    ,     1_400    ,     200   )
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in lengths]

							lowerCAmelCase__      :      List[Any]         =		['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__      :      int         =		[None, 1_600, None]

							for max_length, padding in zip(_lowerCamelCase    ,     _lowerCamelCase   ):
										lowerCAmelCase__      :      str         =		feat_extract(_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     padding=_lowerCamelCase   )
										lowerCAmelCase__      :      List[str]         =		processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]   )
										self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
										self._check_zero_mean_unit_variance(input_values[2][:1_200]   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      str         =		feat_extract(
							    _lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=1_000    ,     padding='max_length'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      int         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1]   )
							self._check_zero_mean_unit_variance(input_values[2]   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[str]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      List[Any]         =		feat_extract(
							    _lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=1_000    ,     padding='longest'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      int         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1_000)   )

							lowerCAmelCase__      :      Optional[Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      List[Any]         =		feat_extract(
							    _lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=2_000    ,     padding='longest'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      Union[str, Any]         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1_200)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							import torch

							lowerCAmelCase__      :      str         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      Optional[int]         =		np.random.rand(100   ).astype(np.floataa   )
							lowerCAmelCase__      :      Any         =		np_speech_inputs.tolist()

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






				@slow
				@require_torch
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
										lowerCAmelCase__      :      Union[str, Any]         =		WavaVecaConfig.from_pretrained(_lowerCamelCase   )
										lowerCAmelCase__      :      str         =		WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase   )

										# only "layer" feature extraction norm should make use of
										# attention_mask
										self.assertEqual(feat_extract.return_attention_mask    ,     config.feat_extract_norm == 'layer'   )

"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       list ,					__lowerCamelCase   :       int = 0		)     ->					list:
  _snake_case					     =       length or len(__lowerCamelCase		)
  _snake_case					     =       False
  for i in range(length - 1		):
    if list_data[i] > list_data[i + 1]:
      _snake_case     ,					_snake_case					     =       list_data[i + 1], list_data[i]
      _snake_case					     =       True

  return list_data if not swapped else bubble_sort(__lowerCamelCase ,					length - 1		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



