"""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


__A			   = random.Random()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=1.0       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None )  ->  Dict:

   if rng is None:
      lowercase__:   Optional[int]					=      global_rng

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

   return values


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=400    ,			_UpperCAmelCase=2000    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=16000    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			):
       lowercase__:   Tuple					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[int]					=      min_seq_length
       lowercase__:   List[str]					=      max_seq_length
       lowercase__:   Optional[Any]					=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       lowercase__:   str					=      feature_size
       lowercase__:   Any					=      padding_value
       lowercase__:   Union[str, Any]					=      sampling_rate
       lowercase__:   List[Any]					=      return_attention_mask
       lowercase__:   Optional[Any]					=      do_normalize






    def _snake_case			(   self							):
       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    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=False							):
       def _flatten(_UpperCAmelCase							):
          return list(itertools.chain(*SCREAMING_SNAKE_CASE__							)							)

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

       return speech_inputs



class     UpperCAmelCase						(UpperCAmelCase__    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:str						    = WavaVecaFeatureExtractor






    def _snake_case			(   self							):
       lowercase__:   Dict					=      WavaVecaFeatureExtractionTester(self							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE__    ,			axis=0							) < 1e-3							)							)
       self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE__    ,			axis=0							) - 1							) < 1e-3							)							)






    def _snake_case			(   self							):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       lowercase__:   List[str]					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       # create three inputs of length 800, 1000, and 1200
       lowercase__:   Dict					=      [floats_list((1, x)							)[0] for x in range(800    ,			1400    ,			200							)]
       lowercase__:   Dict					=      [np.asarray(SCREAMING_SNAKE_CASE__							) for speech_input in speech_inputs]

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

       # Test batched
       lowercase__:   Any					=      feat_extract(SCREAMING_SNAKE_CASE__    ,			return_tensors='''np'''							).input_values
       lowercase__:   Tuple					=      feat_extract(SCREAMING_SNAKE_CASE__    ,			return_tensors='''np'''							).input_values
       for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							):
          self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			atol=1e-3							)							)

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






    def _snake_case			(   self							):
       lowercase__:   Dict					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       lowercase__:   Optional[Any]					=      [floats_list((1, x)							)[0] for x in range(800    ,			1400    ,			200							)]

       lowercase__:   Any					=      ['''longest''', '''max_length''', '''do_not_pad''']
       lowercase__:   List[str]					=      [None, 1600, None]
       for max_length, padding in zip(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							):
          lowercase__:   Tuple					=      feat_extract(SCREAMING_SNAKE_CASE__    ,			padding=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__    ,			return_tensors='''np'''							)
          lowercase__:   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][:1000]							)
          self.assertTrue(input_values[0][1000:].sum() < 1e-6							)
          self._check_zero_mean_unit_variance(input_values[2][:1200]							)






    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       lowercase__:   int					=      range(800    ,			1400    ,			200							)
       lowercase__:   int					=      [floats_list((1, x)							)[0] for x in lengths]

       lowercase__:   List[Any]					=      ['''longest''', '''max_length''', '''do_not_pad''']
       lowercase__:   Optional[int]					=      [None, 1600, None]

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

       lowercase__:   str					=      [floats_list((1, x)							)[0] for x in range(800    ,			1400    ,			200							)]
       lowercase__:   Optional[Any]					=      feat_extract(
           SCREAMING_SNAKE_CASE__    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=2000    ,			padding='''longest'''    ,			return_tensors='''np'''							)
       lowercase__:   List[str]					=      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 _snake_case			(   self							):
       import torch

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

       for inputs in [py_speech_inputs, np_speech_inputs]:
          lowercase__:   Any					=      feature_extractor.pad([{'''input_values''': inputs}]    ,			return_tensors='''np'''							)
          self.assertTrue(np_processed.input_values.dtype == np.floataa							)
          lowercase__:   Optional[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							):
       # 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:
          lowercase__:   int					=      WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
          lowercase__:   str					=      WavaVecaFeatureExtractor.from_pretrained(SCREAMING_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'''							)

'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 @staticmethod
 @abstractmethod
 def a					(      SCREAMING_SNAKE_CASE__   :	ArgumentParser							)  ->    Tuple:
  raise NotImplementedError()





 @abstractmethod
 def a					(      self   :	int							)  ->    Union[str, Any]:
  raise NotImplementedError()






