



'''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


_lowerCamelCase	:       Tuple      	=	random.Random()







def 				__lowerCamelCase				(   A__    ,						A__=1.0    ,						A__=None    ,						A__=None    )       ->	Union[str, 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  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[Any]=7  ,						UpperCamelCase__      :					List[str]=4_0_0  ,						UpperCamelCase__      :					str=2_0_0_0  ,						UpperCamelCase__      :					Any=1  ,						UpperCamelCase__      :					Optional[Any]=0.0  ,						UpperCamelCase__      :					Tuple=1_6_0_0_0  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					Dict=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  A       (  self      :					Optional[int] ):


       """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  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[int]=False  ,						UpperCamelCase__      :					Union[str, Any]=False ):


       """simple docstring"""







       def _flatten(UpperCamelCase__      :					Optional[Any] ):
          return list(itertools.chain(*UpperCamelCase__ ) )

       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(UpperCamelCase__ ) for x in speech_inputs]

       return speech_inputs

class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  WavaVecaFeatureExtractor






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  WavaVecaFeatureExtractionTester(self )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					List[str] ):


       """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  A       (  self      :					Optional[int] ):


       """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(UpperCamelCase__ ) 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(UpperCamelCase__  ,						UpperCamelCase__  ,						atol=1E-3 ) )

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


       """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(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  feat_extract(UpperCamelCase__  ,						padding=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						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  A       (  self      :					Optional[Any] ):


       """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(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  feat_extract(UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						padding=UpperCamelCase__ )
          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  A       (  self      :					str ):


       """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(
           UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						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  A       (  self      :					Tuple ):


       """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(
           UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						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(
           UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						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  A       (  self      :					Optional[Any] ):


       """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  A       (  self      :					Any ):


       """simple docstring"""







       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          UpperCamelCase					=  WavaVecaConfig.from_pretrained(UpperCamelCase__ )
          UpperCamelCase					=  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 importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



