




"""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 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   Tuple=1.0							,    _snake_case				:   List[Any]=None							,    _snake_case				:   Dict=None				)       ->   Optional[int]:






      '''simple docstring'''


      if rng is None:
            _A     =  global_rng

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

      return values





class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def __init__(					self			:			List[str]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			List[str]=7       ,				_UpperCAmelCase			:			List[str]=400       ,				_UpperCAmelCase			:			Optional[Any]=2_000       ,				_UpperCAmelCase			:			int=1       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Any=16_000       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Any=True       ,				):
       _A     =  parent
       _A     =  batch_size
       _A     =  min_seq_length
       _A     =  max_seq_length
       _A     =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _A     =  feature_size
       _A     =  padding_value
       _A     =  sampling_rate
       _A     =  return_attention_mask
       _A     =  do_normalize

 def      lowerCAmelCase_				(					self			:			str						):
       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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Optional[int]=False       ,				_UpperCAmelCase			:			Dict=False						):
       def _flatten(_UpperCAmelCase			:			Optional[int]						):
             return list(itertools.chain(*_UpperCAmelCase						)						)

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

       return speech_inputs





class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



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

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  WavaVecaFeatureExtractionTester(self						)

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Union[str, Any]						):
       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      lowerCAmelCase_				(					self			:			Optional[int]						):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       # create three inputs of length 800, 1000, and 1200
       _A     =  [floats_list((1, x)						)[0] for x in range(800       ,				1_400       ,				200						)]
       _A     =  [np.asarray(_UpperCAmelCase						) for speech_input in speech_inputs]

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

       # Test batched
       _A     =  feat_extract(_UpperCAmelCase       ,				return_tensors='np'						).input_values
       _A     =  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     =  [floats_list((1, x)						)[0] for x in (800, 800, 800)]
       _A     =  np.asarray(_UpperCAmelCase						)
       _A     =  feat_extract(_UpperCAmelCase       ,				return_tensors='np'						).input_values
       _A     =  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      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       _A     =  [floats_list((1, x)						)[0] for x in range(800       ,				1_400       ,				200						)]

       _A     =  ['longest', 'max_length', 'do_not_pad']
       _A     =  [None, 1_600, None]
       for max_length, padding in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             _A     =  feat_extract(_UpperCAmelCase       ,				padding=_UpperCAmelCase       ,				max_length=_UpperCAmelCase       ,				return_tensors='np'						)
             _A     =  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      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       _A     =  range(800       ,				1_400       ,				200						)
       _A     =  [floats_list((1, x)						)[0] for x in lengths]

       _A     =  ['longest', 'max_length', 'do_not_pad']
       _A     =  [None, 1_600, None]

       for max_length, padding in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             _A     =  feat_extract(_UpperCAmelCase       ,				max_length=_UpperCAmelCase       ,				padding=_UpperCAmelCase						)
             _A     =  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      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       _A     =  [floats_list((1, x)						)[0] for x in range(800       ,				1_400       ,				200						)]
       _A     =  feat_extract(
           _UpperCAmelCase       ,				truncation=_UpperCAmelCase       ,				max_length=1_000       ,				padding='max_length'       ,				return_tensors='np'						)
       _A     =  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      lowerCAmelCase_				(					self			:			int						):
       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       _A     =  [floats_list((1, x)						)[0] for x in range(800       ,				1_400       ,				200						)]
       _A     =  feat_extract(
           _UpperCAmelCase       ,				truncation=_UpperCAmelCase       ,				max_length=1_000       ,				padding='longest'       ,				return_tensors='np'						)
       _A     =  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     =  [floats_list((1, x)						)[0] for x in range(800       ,				1_400       ,				200						)]
       _A     =  feat_extract(
           _UpperCAmelCase       ,				truncation=_UpperCAmelCase       ,				max_length=2_000       ,				padding='longest'       ,				return_tensors='np'						)
       _A     =  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      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       import torch

       _A     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       _A     =  np.random.rand(100						).astype(np.floataa						)
       _A     =  np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             _A     =  feature_extractor.pad([{'input_values': inputs}]       ,				return_tensors='np'						)
             self.assertTrue(np_processed.input_values.dtype == np.floataa						)
             _A     =  feature_extractor.pad([{'input_values': inputs}]       ,				return_tensors='pt'						)
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)



 @slow
 @require_torch
 def      lowerCAmelCase_				(					self			:			str						):
       # 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:
             _A     =  WavaVecaConfig.from_pretrained(_UpperCAmelCase						)
             _A     =  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"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

