





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


_UpperCAmelCase             =							random.Random()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Tuple=1.0   ,   SCREAMING_SNAKE_CASE		:Optional[int]=None   ,   SCREAMING_SNAKE_CASE		:str=None   )      ->   Dict:
 if rng is None:
  __lowerCAmelCase					:	Optional[Any]      =       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        snake_case_    (		unittest.TestCase						):






  def __init__(							self			:					List[str]       ,  _snake_case			:					str       ,  _snake_case			:					List[str]=7       ,  _snake_case			:					Dict=400       ,  _snake_case			:					List[Any]=2000       ,  _snake_case			:					List[Any]=1       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					str=16000       ,  _snake_case			:					Tuple=True       ,  _snake_case			:					Tuple=True       ,  )->Optional[Any]:






   '''simple docstring'''

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






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






   '''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			:					int       ,  _snake_case			:					Union[str, Any]=False       ,  _snake_case			:					List[str]=False      )->Tuple:






   '''simple docstring'''

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

   if equal_length:
    __lowerCAmelCase					:	int      =       floats_list((self.batch_size, self.max_seq_length)      )
   else:
    # make sure that inputs increase in size
    __lowerCAmelCase					:	Dict      =       [
        _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					:	List[str]      =       [np.asarray(_snake_case      ) for x in speech_inputs]

   return speech_inputs






class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   WavaVecaFeatureExtractor






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






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       WavaVecaFeatureExtractionTester(self      )






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					List[Any]      )->Any:






   '''simple docstring'''

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






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






   '''simple docstring'''

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

   # Test not batched input
   __lowerCAmelCase					:	Optional[int]      =       feat_extract(speech_inputs[0]       ,  return_tensors="""np"""      ).input_values
   __lowerCAmelCase					:	Optional[int]      =       feat_extract(np_speech_inputs[0]       ,  return_tensors="""np"""      ).input_values
   self.assertTrue(np.allclose(_snake_case       ,  _snake_case       ,  atol=1E-3      )      )

   # Test batched
   __lowerCAmelCase					:	int      =       feat_extract(_snake_case       ,  return_tensors="""np"""      ).input_values
   __lowerCAmelCase					:	Optional[Any]      =       feat_extract(_snake_case       ,  return_tensors="""np"""      ).input_values
   for enc_seq_a, enc_seq_a in zip(_snake_case       ,  _snake_case      ):
    self.assertTrue(np.allclose(_snake_case       ,  _snake_case       ,  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					:	str      =       np.asarray(_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       feat_extract(_snake_case       ,  return_tensors="""np"""      ).input_values
   __lowerCAmelCase					:	str      =       feat_extract(_snake_case       ,  return_tensors="""np"""      ).input_values
   for enc_seq_a, enc_seq_a in zip(_snake_case       ,  _snake_case      ):
    self.assertTrue(np.allclose(_snake_case       ,  _snake_case       ,  atol=1E-3      )      )






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       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       ,  1400       ,  200      )]

   __lowerCAmelCase					:	int      =       ["""longest""", """max_length""", """do_not_pad"""]
   __lowerCAmelCase					:	Union[str, Any]      =       [None, 1600, None]
   for max_length, padding in zip(_snake_case       ,  _snake_case      ):
    __lowerCAmelCase					:	Optional[int]      =       feat_extract(_snake_case       ,  padding=_snake_case       ,  max_length=_snake_case       ,  return_tensors="""np"""      )
    __lowerCAmelCase					:	List[str]      =       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 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

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

   __lowerCAmelCase					:	List[Any]      =       ["""longest""", """max_length""", """do_not_pad"""]
   __lowerCAmelCase					:	int      =       [None, 1600, None]

   for max_length, padding in zip(_snake_case       ,  _snake_case      ):
    __lowerCAmelCase					:	Optional[int]      =       feat_extract(_snake_case       ,  max_length=_snake_case       ,  padding=_snake_case      )
    __lowerCAmelCase					:	List[Any]      =       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 				UpperCAmelCase__						(							self			:					Optional[int]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
   __lowerCAmelCase					:	Any      =       [floats_list((1, x)      )[0] for x in range(800       ,  1400       ,  200      )]
   __lowerCAmelCase					:	Optional[int]      =       feat_extract(
       _snake_case       ,  truncation=_snake_case       ,  max_length=1000       ,  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 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
   __lowerCAmelCase					:	Dict      =       [floats_list((1, x)      )[0] for x in range(800       ,  1400       ,  200      )]
   __lowerCAmelCase					:	Optional[int]      =       feat_extract(
       _snake_case       ,  truncation=_snake_case       ,  max_length=1000       ,  padding="""longest"""       ,  return_tensors="""np"""      )
   __lowerCAmelCase					:	Tuple      =       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)      )

   __lowerCAmelCase					:	List[str]      =       [floats_list((1, x)      )[0] for x in range(800       ,  1400       ,  200      )]
   __lowerCAmelCase					:	Any      =       feat_extract(
       _snake_case       ,  truncation=_snake_case       ,  max_length=2000       ,  padding="""longest"""       ,  return_tensors="""np"""      )
   __lowerCAmelCase					:	Optional[int]      =       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 				UpperCAmelCase__						(							self			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

   import torch

   __lowerCAmelCase					:	List[str]      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
   __lowerCAmelCase					:	str      =       np.random.rand(100      ).astype(np.floataa      )
   __lowerCAmelCase					:	Tuple      =       np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowerCAmelCase					:	Optional[int]      =       feature_extractor.pad([{"""input_values""": inputs}]       ,  return_tensors="""np"""      )
    self.assertTrue(np_processed.input_values.dtype == np.floataa      )
    __lowerCAmelCase					:	Union[str, Any]      =       feature_extractor.pad([{"""input_values""": inputs}]       ,  return_tensors="""pt"""      )
    self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )






  @slow
  @require_torch
  def 				UpperCAmelCase__						(							self			:					Tuple      )->Any:






   '''simple docstring'''

   for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
    __lowerCAmelCase					:	Optional[Any]      =       WavaVecaConfig.from_pretrained(_snake_case      )
    __lowerCAmelCase					:	Any      =       WavaVecaFeatureExtractor.from_pretrained(_snake_case      )

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






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )