





"""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  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=1_3					,      snake_case_=3_0					,      snake_case_=2					,      snake_case_=3					,      snake_case_=True					,      snake_case_=True					,      snake_case_=3_2					,      snake_case_=5					,      snake_case_=4					,      snake_case_=3_7					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=1_0					,      snake_case_=0.02					,      snake_case_=3					,      snake_case_=0.6					,      snake_case_=None					,      ):

        """simple docstring"""

        A_  :       Optional[Any]										=  parent
        A_  :       Union[str, Any]										=  batch_size
        A_  :       str										=  image_size
        A_  :       int										=  patch_size
        A_  :       Any										=  num_channels
        A_  :       List[Any]										=  is_training
        A_  :       Tuple										=  use_labels
        A_  :       List[Any]										=  hidden_size
        A_  :       Optional[Any]										=  num_hidden_layers
        A_  :       int										=  num_attention_heads
        A_  :       Optional[int]										=  intermediate_size
        A_  :       int										=  hidden_act
        A_  :       Tuple										=  hidden_dropout_prob
        A_  :       Optional[Any]										=  attention_probs_dropout_prob
        A_  :       Tuple										=  type_sequence_label_size
        A_  :       int										=  initializer_range
        A_  :       str										=  mask_ratio
        A_  :       int										=  scope

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        A_  :       Union[str, Any]										=  None
        if self.use_labels:
          A_  :       Tuple										=  ids_tensor([self.batch_size]					,      self.type_sequence_label_size  )

        A_  :       Any										=  self.get_config()

        return config, pixel_values, labels

      def 					lowerCamelCase_			(     self  ):

        """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=snake_case_					,      initializer_range=self.initializer_range					,      mask_ratio=self.mask_ratio					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[int]										=  ViTMAEModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       int										=  model(snake_case_  )
        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Any										=  ViTMAEForPreTraining(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       int										=  model(snake_case_  )
        A_  :       List[Any]										=  (self.image_size // self.patch_size) ** 2
        A_  :       Optional[int]										=  self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)  )

        # test greyscale images
        A_  :       Tuple										=  1
        A_  :       Any										=  ViTMAEForPreTraining(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       List[Any]										=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        A_  :       int										=  model(snake_case_  )
        A_  :       Optional[Any]										=  self.patch_size**2
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.prepare_config_and_inputs()
        A_						,       A_						,       A_  :       int										=  config_and_inputs
        A_  :       int										=  {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      lowercase_      : Tuple	    =   {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

      lowercase_      : List[str]	    =   False
      lowercase_      : Optional[int]	    =   False
      lowercase_      : Union[str, Any]	    =   False
      lowercase_      : Optional[Any]	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  ViTMAEModelTester(self  )
        A_  :       str										=  ConfigTester(self					,      config_class=snake_case_					,      has_text_modality=snake_case_					,      hidden_size=3_7  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      @unittest.skip(reason='ViTMAE does not use inputs_embeds'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Union[str, Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       List[Any]										=  model_class(snake_case_  )
          self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)  )
          A_  :       Any										=  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(snake_case_					,      nn.Linear  )  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Optional[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

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

          A_  :       List[str]										=  ['pixel_values']
          self.assertListEqual(arg_names[:1]					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        np.random.seed(2  )

        A_  :       Dict										=  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
        A_  :       Any										=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
        A_  :       Tuple										=  torch.from_numpy(snake_case_  )

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

        super().check_pt_tf_models(snake_case_					,      snake_case_					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       List[str]										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       Dict										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          # make random mask reproducible
          torch.manual_seed(2  )
          with torch.no_grad():
            A_  :       Optional[Any]										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )

          A_  :       Optional[Any]										=  outputs[0].cpu().numpy()
          A_  :       List[Any]										=  0

          with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(snake_case_  )
            A_  :       Any										=  model_class.from_pretrained(snake_case_  )
            model.to(snake_case_  )
            # make random mask reproducible
            torch.manual_seed(2  )
            with torch.no_grad():
              A_  :       List[str]										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )

            # Make sure we don't have nans
            A_  :       Union[str, Any]										=  after_outputs[0].cpu().numpy()
            A_  :       Union[str, Any]										=  0
            A_  :       int										=  np.amax(np.abs(out_a - out_a  )  )
            self.assertLessEqual(snake_case_					,      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 					lowerCamelCase_			(     self  ):

        """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 					lowerCamelCase_			(     self  ):

        """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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :       int										=  ViTMAEModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       List[Any]										=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image





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



      '''simple docstring'''

      @cached_property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        np.random.seed(2  )

        A_  :       List[str]										=  ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'  ).to(snake_case_  )

        A_  :       Tuple										=  self.default_image_processor
        A_  :       Optional[Any]										=  prepare_img()
        A_  :       List[str]										=  image_processor(images=snake_case_					,      return_tensors='pt'  ).to(snake_case_  )

        # 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)
        A_  :       List[str]										=  ViTMAEConfig()
        A_  :       Optional[Any]										=  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
        A_  :       List[Any]										=  np.random.uniform(size=(1, num_patches)  )

        # forward pass
        with torch.no_grad():
          A_  :       int										=  model(**snake_case_					,      noise=torch.from_numpy(snake_case_  ).to(device=snake_case_  )  )

        # verify the logits
        A_  :       Optional[Any]										=  torch.Size((1, 1_9_6, 7_6_8)  )
        self.assertEqual(outputs.logits.shape					,      snake_case_  )

        A_  :       int										=  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(snake_case_  )					,      atol=1E-4  )  )






"""simple docstring"""




from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   CustomTokenizer
      pass