'''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


SCREAMING_SNAKE_CASE_:					Optional[Any]  =random.Random()





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			List[Any]=1.0 ,   snake_case_	:			Tuple=None ,   snake_case_	:			str=None      )   ->				Any:


     '''simple docstring'''




     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 	__A				(			unittest.TestCase     ):



    def __init__(self   :   str       ,      __a   :   int       ,      __a   :   Any=7       ,      __a   :   Optional[Any]=400       ,      __a   :   List[str]=2000       ,      __a   :   Union[str, Any]=1       ,      __a   :   Union[str, Any]=0.0       ,      __a   :   Optional[int]=16000       ,      __a   :   List[Any]=True       ,      __a   :   Any=True       ,      ):
         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  _lowercase						(self   :   Dict			):
         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  _lowercase						(self   :   Tuple       ,      __a   :   Optional[int]=False       ,      __a   :   str=False			):
         def _flatten(__a   :   List[str]			):
              return list(itertools.chain(*__a			)			)

         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(__a			) for x in speech_inputs]

         return speech_inputs




class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						List[Any]	       =  WavaVecaFeatureExtractor



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       WavaVecaFeatureExtractionTester(self			)



    def  _lowercase						(self   :   Any       ,      __a   :   List[str]			):
         self.assertTrue(np.all(np.mean(__a       ,      axis=0			) < 1E-3			)			)
         self.assertTrue(np.all(np.abs(np.var(__a       ,      axis=0			) - 1			) < 1E-3			)			)



    def  _lowercase						(self   :   List[str]			):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         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(800       ,      1400       ,      200			)]
         UpperCAmelCase_	   =       [np.asarray(__a			) 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(__a       ,      __a       ,      atol=1E-3			)			)

         # Test batched
         UpperCAmelCase_	   =       feat_extract(__a       ,      return_tensors="np"			).input_values
         UpperCAmelCase_	   =       feat_extract(__a       ,      return_tensors="np"			).input_values
         for enc_seq_a, enc_seq_a in zip(__a       ,      __a			):
              self.assertTrue(np.allclose(__a       ,      __a       ,      atol=1E-3			)			)

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



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in range(800       ,      1400       ,      200			)]

         UpperCAmelCase_	   =       ["longest", "max_length", "do_not_pad"]
         UpperCAmelCase_	   =       [None, 1600, None]
         for max_length, padding in zip(__a       ,      __a			):
              UpperCAmelCase_	   =       feat_extract(__a       ,      padding=__a       ,      max_length=__a       ,      return_tensors="np"			)
              UpperCAmelCase_	   =       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][:1000]			)
              self.assertTrue(input_values[0][1000:].sum() < 1E-6			)
              self._check_zero_mean_unit_variance(input_values[2][:1200]			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       range(800       ,      1400       ,      200			)
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in lengths]

         UpperCAmelCase_	   =       ["longest", "max_length", "do_not_pad"]
         UpperCAmelCase_	   =       [None, 1600, None]

         for max_length, padding in zip(__a       ,      __a			):
              UpperCAmelCase_	   =       feat_extract(__a       ,      max_length=__a       ,      padding=__a			)
              UpperCAmelCase_	   =       processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]			)
              self._check_zero_mean_unit_variance(input_values[1][:1000]			)
              self._check_zero_mean_unit_variance(input_values[2][:1200]			)



    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in range(800       ,      1400       ,      200			)]
         UpperCAmelCase_	   =       feat_extract(
             __a       ,      truncation=__a       ,      max_length=1000       ,      padding="max_length"       ,      return_tensors="np"			)
         UpperCAmelCase_	   =       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  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in range(800       ,      1400       ,      200			)]
         UpperCAmelCase_	   =       feat_extract(
             __a       ,      truncation=__a       ,      max_length=1000       ,      padding="longest"       ,      return_tensors="np"			)
         UpperCAmelCase_	   =       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :800]			)
         self._check_zero_mean_unit_variance(input_values[1, :1000]			)
         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, 1000)			)

         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in range(800       ,      1400       ,      200			)]
         UpperCAmelCase_	   =       feat_extract(
             __a       ,      truncation=__a       ,      max_length=2000       ,      padding="longest"       ,      return_tensors="np"			)
         UpperCAmelCase_	   =       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :800]			)
         self._check_zero_mean_unit_variance(input_values[1, :1000]			)
         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, 1200)			)



    @require_torch
    def  _lowercase						(self   :   List[str]			):
         import torch

         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       np.random.rand(100			).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  _lowercase						(self   :   Tuple			):
         # this test makes sure that models that are using
         # group norm don't have their feature extractor return the
         # attention_mask
         for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
              UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(__a			)
              UpperCAmelCase_	   =       WavaVecaFeatureExtractor.from_pretrained(__a			)

              # 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      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     if start < end:
          UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       a[end]
          UpperCAmelCase_	   =       a[pivot]
          UpperCAmelCase_	   =       temp

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       _in_place_partition(snake_case_ ,   snake_case_ ,   snake_case_      )
          count += _in_place_quick_sort(snake_case_ ,   snake_case_ ,   p - 1      )
          count += _in_place_quick_sort(snake_case_ ,   p + 1 ,   snake_case_      )
     return count





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       a[pivot]
     UpperCAmelCase_	   =       temp
     UpperCAmelCase_	   =       start - 1
     for index in range(snake_case_ ,   snake_case_      ):
          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


SCREAMING_SNAKE_CASE_:					List[str]  =TemporaryFile()
SCREAMING_SNAKE_CASE_:					int  =1_00  # 1000 elements are to be sorted


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


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

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






print(z)




