


'''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


UpperCAmelCase_			:  List[str]										=   random.Random()


def       _lowercase							(					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Tuple=1.0,					UpperCamelCase__							:       List[Any]=None,					UpperCamelCase__							:       Dict=None      ):


 if rng is None:
  __A	:		int	  =      global_rng

 __A	:		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     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=7   ,   __lowercase=400   ,   __lowercase=2_000   ,   __lowercase=1   ,   __lowercase=0.0   ,   __lowercase=16_000   ,   __lowercase=True   ,   __lowercase=True   ,   ):

  """simple docstring"""







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


 def 			snake_case__ (   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 			snake_case__ (   self   ,   __lowercase=False   ,   __lowercase=False    ):

  """simple docstring"""







  def _flatten(__lowercase    ):
   return list(itertools.chain(*_UpperCAmelCase    )    )

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

  return speech_inputs


class     _lowerCamelCase       (     __lowerCAmelCase     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    WavaVecaFeatureExtractor


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      WavaVecaFeatureExtractionTester(self    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

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

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

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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  __A	:		Optional[Any]	  =      ['longest', 'max_length', 'do_not_pad']
  __A	:		int	  =      [None, 1_600, None]
  for max_length, padding in zip(_UpperCAmelCase   ,   _UpperCAmelCase    ):
   __A	:		int	  =      feat_extract(_UpperCAmelCase   ,   padding=_UpperCAmelCase   ,   max_length=_UpperCAmelCase   ,   return_tensors='np'    )
   __A	:		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 			snake_case__ (   self    ):

  """simple docstring"""







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

  __A	:		Dict	  =      ['longest', 'max_length', 'do_not_pad']
  __A	:		List[Any]	  =      [None, 1_600, None]

  for max_length, padding in zip(_UpperCAmelCase   ,   _UpperCAmelCase    ):
   __A	:		Optional[int]	  =      feat_extract(_UpperCAmelCase   ,   max_length=_UpperCAmelCase   ,   padding=_UpperCAmelCase    )
   __A	:		Optional[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][:1_200]    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  __A	:		Union[str, Any]	  =      [floats_list((1, x)    )[0] for x in range(800   ,   1_400   ,   200    )]
  __A	:		List[str]	  =      feat_extract(
      _UpperCAmelCase   ,   truncation=_UpperCAmelCase   ,   max_length=1_000   ,   padding='max_length'   ,   return_tensors='np'    )
  __A	:		Dict	  =      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 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  __A	:		Dict	  =      [floats_list((1, x)    )[0] for x in range(800   ,   1_400   ,   200    )]
  __A	:		List[str]	  =      feat_extract(
      _UpperCAmelCase   ,   truncation=_UpperCAmelCase   ,   max_length=1_000   ,   padding='longest'   ,   return_tensors='np'    )
  __A	:		List[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 max_length
  self.assertTrue(input_values.shape == (3, 1_000)    )

  __A	:		Tuple	  =      [floats_list((1, x)    )[0] for x in range(800   ,   1_400   ,   200    )]
  __A	:		Dict	  =      feat_extract(
      _UpperCAmelCase   ,   truncation=_UpperCAmelCase   ,   max_length=2_000   ,   padding='longest'   ,   return_tensors='np'    )
  __A	:		Tuple	  =      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 			snake_case__ (   self    ):

  """simple docstring"""







  import torch

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

  for inputs in [py_speech_inputs, np_speech_inputs]:
   __A	:		Optional[Any]	  =      feature_extractor.pad([{'input_values': inputs}]   ,   return_tensors='np'    )
   self.assertTrue(np_processed.input_values.dtype == np.floataa    )
   __A	:		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 			snake_case__ (   self    ):

  """simple docstring"""







  for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
   __A	:		int	  =      WavaVecaConfig.from_pretrained(_UpperCAmelCase    )
   __A	:		Optional[int]	  =      WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase    )

   # 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"""

import math






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * power_factor






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

