

'''simple docstring'''
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTMAEForPreTraining, ViTMAEModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor







class 							A__   :





  def __init__(		self				:     List[str]							, _a				:     Tuple							, _a				:     List[Any]=13							, _a				:     Optional[int]=30							, _a				:     Any=2							, _a				:     Dict=3							, _a				:     Optional[Any]=True							, _a				:     Optional[int]=True							, _a				:     List[Any]=32							, _a				:     Dict=5							, _a				:     Union[str, Any]=4							, _a				:     Optional[int]=37							, _a				:     List[str]="gelu"							, _a				:     Optional[int]=0.1							, _a				:     Any=0.1							, _a				:     Tuple=10							, _a				:     Tuple=0.02							, _a				:     List[str]=3							, _a				:     Any=0.6							, _a				:     List[Any]=None							, )     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =parent
       _SCREAMING_SNAKE_CASE							   =batch_size
       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =patch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =is_training
       _SCREAMING_SNAKE_CASE							   =use_labels
       _SCREAMING_SNAKE_CASE							   =hidden_size
       _SCREAMING_SNAKE_CASE							   =num_hidden_layers
       _SCREAMING_SNAKE_CASE							   =num_attention_heads
       _SCREAMING_SNAKE_CASE							   =intermediate_size
       _SCREAMING_SNAKE_CASE							   =hidden_act
       _SCREAMING_SNAKE_CASE							   =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							   =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							   =type_sequence_label_size
       _SCREAMING_SNAKE_CASE							   =initializer_range
       _SCREAMING_SNAKE_CASE							   =mask_ratio
       _SCREAMING_SNAKE_CASE							   =scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       _SCREAMING_SNAKE_CASE							   =(image_size // patch_size) ** 2
       _SCREAMING_SNAKE_CASE							   =int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)





  def 						A  (		self				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _SCREAMING_SNAKE_CASE							   =None
       if self.use_labels:
            _SCREAMING_SNAKE_CASE							   =ids_tensor([self.batch_size]							, self.type_sequence_label_size			)

       _SCREAMING_SNAKE_CASE							   =self.get_config()

       return config, pixel_values, labels





  def 						A  (		self				:     Dict			)     ->   str:





       '''simple docstring'''






       return ViTMAEConfig(
           image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, is_decoder=_a							, initializer_range=self.initializer_range							, mask_ratio=self.mask_ratio							, )





  def 						A  (		self				:     Union[str, Any]							, _a				:     Optional[int]							, _a				:     int							, _a				:     Dict			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ViTMAEModel(config=_a			)
       model.to(_a			)
       model.eval()
       _SCREAMING_SNAKE_CASE							   =model(_a			)
       self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)			)





  def 						A  (		self				:     Any							, _a				:     int							, _a				:     List[str]							, _a				:     Any			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ViTMAEForPreTraining(_a			)
       model.to(_a			)
       model.eval()
       _SCREAMING_SNAKE_CASE							   =model(_a			)
       _SCREAMING_SNAKE_CASE							   =(self.image_size // self.patch_size) ** 2
       _SCREAMING_SNAKE_CASE							   =self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape							, (self.batch_size, num_patches, expected_num_channels)			)

       # test greyscale images
       _SCREAMING_SNAKE_CASE							   =1
       _SCREAMING_SNAKE_CASE							   =ViTMAEForPreTraining(_a			)
       model.to(_a			)
       model.eval()
       _SCREAMING_SNAKE_CASE							   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       _SCREAMING_SNAKE_CASE							   =model(_a			)
       _SCREAMING_SNAKE_CASE							   =self.patch_size**2
       self.parent.assertEqual(result.logits.shape							, (self.batch_size, num_patches, expected_num_channels)			)





  def 						A  (		self				:     Dict			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =config_and_inputs
       _SCREAMING_SNAKE_CASE							   ={'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 							A__   (	A__					,       A__					,       unittest.TestCase  ):
  A__					=    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  A__					=    {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

  A__					=    False
  A__					=    False
  A__					=    False
  A__					=    False





  def 						A  (		self				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ViTMAEModelTester(self			)
       _SCREAMING_SNAKE_CASE							   =ConfigTester(self							, config_class=_a							, has_text_modality=_a							, hidden_size=37			)





  def 						A  (		self				:     str			)     ->   Dict:





       '''simple docstring'''






       self.config_tester.run_common_tests()





  @unittest.skip(reason='ViTMAE does not use inputs_embeds'			)
  def 						A  (		self				:     Union[str, Any]			)     ->   Dict:





       '''simple docstring'''






       pass





  def 						A  (		self				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)
            self.assertIsInstance(model.get_input_embeddings()							, (nn.Module)			)
            _SCREAMING_SNAKE_CASE							   =model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_a							, nn.Linear			)			)





  def 						A  (		self				:     Any			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)
            _SCREAMING_SNAKE_CASE							   =inspect.signature(model.forward			)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _SCREAMING_SNAKE_CASE							   =[*signature.parameters.keys()]

            _SCREAMING_SNAKE_CASE							   =['pixel_values']
            self.assertListEqual(arg_names[:1]							, _a			)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a			)





  def 						A  (		self				:     List[Any]			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*_a			)





  def 						A  (		self				:     List[str]							, _a				:     List[str]							, _a				:     Any							, _a				:     List[Any]			)     ->   Tuple:





       '''simple docstring'''






       np.random.seed(2			)

       _SCREAMING_SNAKE_CASE							   =int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
       _SCREAMING_SNAKE_CASE							   =np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
       _SCREAMING_SNAKE_CASE							   =torch.from_numpy(_a			)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       _SCREAMING_SNAKE_CASE							   =pt_noise

       super().check_pt_tf_models(_a							, _a							, _a			)





  def 						A  (		self				:     Optional[Any]			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)
            model.to(_a			)
            model.eval()
            # make random mask reproducible
            torch.manual_seed(2			)
            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE							   =model(**self._prepare_for_class(_a							, _a			)			)

            _SCREAMING_SNAKE_CASE							   =outputs[0].cpu().numpy()
            _SCREAMING_SNAKE_CASE							   =0

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(_a			)
                 _SCREAMING_SNAKE_CASE							   =model_class.from_pretrained(_a			)
                 model.to(_a			)
                 # make random mask reproducible
                 torch.manual_seed(2			)
                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE							   =model(**self._prepare_for_class(_a							, _a			)			)

                 # Make sure we don't have nans
                 _SCREAMING_SNAKE_CASE							   =after_outputs[0].cpu().numpy()
                 _SCREAMING_SNAKE_CASE							   =0
                 _SCREAMING_SNAKE_CASE							   =np.amax(np.abs(out_a - out_a			)			)
                 self.assertLessEqual(_a							, 1e-5			)





  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
  def 						A  (		self				:     Tuple			)     ->   Dict:





       '''simple docstring'''






       pass





  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
  def 						A  (		self				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






       pass





  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
  def 						A  (		self				:     Dict			)     ->   str:





       '''simple docstring'''






       pass





  @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'			)
  def 						A  (		self				:     int			)     ->   Union[str, Any]:





       '''simple docstring'''






       pass





  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
  def 						A  (		self				:     List[str]			)     ->   Any:





       '''simple docstring'''






       pass





  @slow
  def 						A  (		self				:     Union[str, Any]			)     ->   int:





       '''simple docstring'''






       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE							   =ViTMAEModel.from_pretrained(_a			)
            self.assertIsNotNone(_a			)


def 	_lowerCAmelCase (						)				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
     return image







@require_torch
@require_vision
class 							A__   (	unittest.TestCase  ):





  @cached_property
  def 						A  (		self				:     Tuple			)     ->   Dict:





       '''simple docstring'''






       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'			) if is_vision_available() else None





  @slow
  def 						A  (		self				:     Dict			)     ->   Tuple:





       '''simple docstring'''






       np.random.seed(2			)

       _SCREAMING_SNAKE_CASE							   =ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'			).to(_a			)

       _SCREAMING_SNAKE_CASE							   =self.default_image_processor
       _SCREAMING_SNAKE_CASE							   =prepare_img()
       _SCREAMING_SNAKE_CASE							   =image_processor(images=_a							, return_tensors='pt'			).to(_a			)

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       _SCREAMING_SNAKE_CASE							   =ViTMAEConfig()
       _SCREAMING_SNAKE_CASE							   =int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
       _SCREAMING_SNAKE_CASE							   =np.random.uniform(size=(1, num_patches)			)

       # forward pass
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							   =model(**_a							, noise=torch.from_numpy(_a			).to(device=_a			)			)

       # verify the logits
       _SCREAMING_SNAKE_CASE							   =torch.Size((1, 196, 768)			)
       self.assertEqual(outputs.logits.shape							, _a			)

       _SCREAMING_SNAKE_CASE							   =torch.tensor(
           [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]			)

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							, expected_slice.to(_a			)							, atol=1e-4			)			)



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 							A__   (	A__  ):
  A__					=    'MCTCTFeatureExtractor'
  A__					=    'AutoTokenizer'





  def __init__(		self				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       super().__init__(_a							, _a			)
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False





  def __call__(		self				:     Dict							, *_a				:     str							, **_a				:     Dict			)     ->   Dict:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor(*_a							, **_a			)

       if "raw_speech" in kwargs:
            warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('raw_speech'			)
       else:
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('audio'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('sampling_rate'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('text'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if audio is None and text is None:
            raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

       if audio is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor(_a							, *_a							, sampling_rate=_a							, **_a			)
       if text is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer(_a							, **_a			)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            _SCREAMING_SNAKE_CASE							   =encodings['input_ids']
            return inputs





  def 						A  (		self				:     Any							, *_a				:     List[str]							, **_a				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							, **_a			)





  def 						A  (		self				:     Dict							, *_a				:     Tuple							, **_a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor.pad(*_a							, **_a			)

       _SCREAMING_SNAKE_CASE							   =kwargs.pop('input_features'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('labels'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if input_features is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor.pad(_a							, *_a							, **_a			)
       if labels is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer.pad(_a							, **_a			)

       if labels is None:
            return input_features
       elif input_features is None:
            return labels
       else:
            _SCREAMING_SNAKE_CASE							   =labels['input_ids']
            return input_features





  def 						A  (		self				:     Tuple							, *_a				:     Dict							, **_a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.decode(*_a							, **_a			)





  @contextmanager
  def 						A  (		self				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your audio inputs, or in a separate call.'			)
       _SCREAMING_SNAKE_CASE							   =True
       _SCREAMING_SNAKE_CASE							   =self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False
