
'''simple docstring'''


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


snake_case_ :  Dict     =					random.Random()



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       str=1.0,      _UpperCAmelCase					:       Dict=None,      _UpperCAmelCase					:       Any=None):
   if rng is None:
      UpperCamelCase       =  global_rng

   UpperCamelCase       =  []
   for batch_idx in range(shape[0]):
      values.append([])
      for _ in range(shape[1]):
         values[-1].append(rng.random() * scale)

   return values


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=2_0_0_0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=1_6_0_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  min_seq_length
      UpperCamelCase       =  max_seq_length
      UpperCamelCase       =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      UpperCamelCase       =  feature_size
      UpperCamelCase       =  padding_value
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  return_attention_mask
      UpperCamelCase       =  do_normalize







   def       UpperCAmelCase				(			self   ):

      '''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       UpperCAmelCase				(			self ,   lowerCamelCase__=False ,   lowerCamelCase__=False   ):

      '''simple docstring'''







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

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

      return speech_inputs




class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							WavaVecaFeatureExtractor







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  WavaVecaFeatureExtractionTester(self   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      # create three inputs of length 800, 1000, and 1200
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  [np.asarray(lowerCamelCase__   ) for speech_input in speech_inputs]

      # Test not batched input
      UpperCamelCase       =  feat_extract(speech_inputs[0] ,   return_tensors='''np'''   ).input_values
      UpperCamelCase       =  feat_extract(np_speech_inputs[0] ,   return_tensors='''np'''   ).input_values
      self.assertTrue(np.allclose(lowerCamelCase__ ,   lowerCamelCase__ ,   atol=1e-3   )   )

      # Test batched
      UpperCamelCase       =  feat_extract(lowerCamelCase__ ,   return_tensors='''np'''   ).input_values
      UpperCamelCase       =  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.
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
      UpperCamelCase       =  np.asarray(lowerCamelCase__   )
      UpperCamelCase       =  feat_extract(lowerCamelCase__ ,   return_tensors='''np'''   ).input_values
      UpperCamelCase       =  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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]

      UpperCamelCase       =  ['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase       =  [None, 1_6_0_0, None]
      for max_length, padding in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  feat_extract(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   return_tensors='''np'''   )
         UpperCamelCase       =  processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
         self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6   )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
         self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6   )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  range(8_0_0 ,   1_4_0_0 ,   2_0_0   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in lengths]

      UpperCamelCase       =  ['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase       =  [None, 1_6_0_0, None]

      for max_length, padding in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  feat_extract(lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   padding=lowerCamelCase__   )
         UpperCamelCase       =  processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feat_extract(
          lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=1_0_0_0 ,   padding='''max_length''' ,   return_tensors='''np'''   )
      UpperCamelCase       =  processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
      self._check_zero_mean_unit_variance(input_values[1]   )
      self._check_zero_mean_unit_variance(input_values[2]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feat_extract(
          lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=1_0_0_0 ,   padding='''longest''' ,   return_tensors='''np'''   )
      UpperCamelCase       =  processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
      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_0_0_0)   )

      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feat_extract(
          lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=2_0_0_0 ,   padding='''longest''' ,   return_tensors='''np'''   )
      UpperCamelCase       =  processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
      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_2_0_0)   )







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      import torch

      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  np.random.rand(1_0_0   ).astype(np.floataa   )
      UpperCamelCase       =  np_speech_inputs.tolist()

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







   @slow
   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
         UpperCamelCase       =  WavaVecaConfig.from_pretrained(lowerCamelCase__   )
         UpperCamelCase       =  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'''


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   if start < end:
      UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
      UpperCamelCase       =  a[end]
      UpperCamelCase       =  a[pivot]
      UpperCamelCase       =  temp

      UpperCamelCase     , UpperCamelCase       =  _in_place_partition(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
      count += _in_place_quick_sort(_UpperCAmelCase,      _UpperCAmelCase,      p - 1)
      count += _in_place_quick_sort(_UpperCAmelCase,      p + 1,      _UpperCAmelCase)
   return count



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  a[pivot]
   UpperCamelCase       =  temp
   UpperCamelCase       =  start - 1
   for index in range(_UpperCAmelCase,      _UpperCAmelCase):
      count += 1
      if a[index] < a[end]:  # check if current val is less than pivot value
         UpperCamelCase       =  new_pivot_index + 1
         UpperCamelCase       =  a[new_pivot_index]
         UpperCamelCase       =  a[index]
         UpperCamelCase       =  temp

   UpperCamelCase       =  a[new_pivot_index + 1]
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  temp
   return new_pivot_index + 1, count


snake_case_ :  List[Any]     =					TemporaryFile()
snake_case_ :  Optional[Any]     =					100  # 1000 elements are to be sorted


snake_case_  , snake_case_ :  List[str]     =					0, 1  # mean and standard deviation
snake_case_ :  Any     =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
snake_case_ :  Optional[int]     =					np.load(outfile)
snake_case_ :  Optional[int]     =					len(M) - 1
snake_case_ :  List[str]     =					_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)





