




'''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      SCREAMING_SNAKE_CASE    :






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


           '''simple docstring'''

           a_     :							str     						=  parent
           a_     :							Tuple     						=  batch_size
           a_     :							Any     						=  image_size
           a_     :							Union[str, Any]     						=  patch_size
           a_     :							Any     						=  num_channels
           a_     :							List[Any]     						=  is_training
           a_     :							List[str]     						=  use_labels
           a_     :							Any     						=  hidden_size
           a_     :							Any     						=  num_hidden_layers
           a_     :							Tuple     						=  num_attention_heads
           a_     :							Union[str, Any]     						=  intermediate_size
           a_     :							Optional[int]     						=  hidden_act
           a_     :							Optional[Any]     						=  hidden_dropout_prob
           a_     :							List[str]     						=  attention_probs_dropout_prob
           a_     :							int     						=  type_sequence_label_size
           a_     :							List[Any]     						=  initializer_range
           a_     :							Union[str, Any]     						=  mask_ratio
           a_     :							Optional[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_     :							int     						=  (image_size // patch_size) ** 2
           a_     :							Optional[int]     						=  int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           a_     :							Optional[Any]     						=  None
           if self.use_labels:
                a_     :							int     						=  ids_tensor([self.batch_size]						,						self.type_sequence_label_size       )

           a_     :							Dict     						=  self.get_config()

           return config, pixel_values, labels






      def 							lowercase_						( self   :  Any       ):


           '''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=lowercase__						,						initializer_range=self.initializer_range						,						mask_ratio=self.mask_ratio						,						)






      def 							lowercase_						( self   :  int						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  ViTMAEModel(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							int     						=  model(lowercase__       )
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							Tuple     						=  ViTMAEForPreTraining(lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							List[Any]     						=  model(lowercase__       )
           a_     :							int     						=  (self.image_size // self.patch_size) ** 2
           a_     :							List[Any]     						=  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_     :							Optional[Any]     						=  1
           a_     :							Dict     						=  ViTMAEForPreTraining(lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Any     						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
           a_     :							Any     						=  model(lowercase__       )
           a_     :							Union[str, Any]     						=  self.patch_size**2
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, num_patches, expected_num_channels)       )






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


           '''simple docstring'''

           a_     :							int     						=  self.prepare_config_and_inputs()
           a_						,     a_						,     a_     :							Optional[Any]     						=  config_and_inputs
           a_     :							Dict     						=  {"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Tuple   							=					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      __magic_name__  :       List[str]   							=					{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

      __magic_name__  :       Optional[Any]   							=					False
      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       int   							=					False






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[str]     						=  ViTMAEModelTester(self       )
           a_     :							List[Any]     						=  ConfigTester(self						,						config_class=lowercase__						,						has_text_modality=lowercase__						,						hidden_size=37       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""       )
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_						,     a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs_for_common()

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






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_						,     a_     :							str     						=  self.model_tester.prepare_config_and_inputs_for_common()

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

                a_     :							Tuple     						=  ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]						,						lowercase__       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*lowercase__       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  str						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           np.random.seed(2       )

           a_     :							int     						=  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
           a_     :							Union[str, Any]     						=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
           a_     :							Union[str, Any]     						=  torch.from_numpy(lowercase__       )

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           a_     :							Optional[Any]     						=  pt_noise

           super().check_pt_tf_models(lowercase__						,						lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

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

           for model_class in self.all_model_classes:
                a_     :							List[str]     						=  model_class(lowercase__       )
                model.to(lowercase__       )
                model.eval()
                # make random mask reproducible
                torch.manual_seed(2       )
                with torch.no_grad():
                     a_     :							Dict     						=  model(**self._prepare_for_class(lowercase__						,						lowercase__       )       )

                a_     :							Dict     						=  outputs[0].cpu().numpy()
                a_     :							Optional[Any]     						=  0

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

                     # Make sure we don't have nans
                     a_     :							Tuple     						=  after_outputs[0].cpu().numpy()
                     a_     :							int     						=  0
                     a_     :							str     						=  np.amax(np.abs(out_a - out_a       )       )
                     self.assertLessEqual(lowercase__						,						1e-5       )






      @unittest.skip(
          reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""       )
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           pass






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


           '''simple docstring'''

           pass






      @unittest.skip(
          reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""       )
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           pass






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


           '''simple docstring'''

           pass






      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           pass






      @slow
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							Tuple     						=  ViTMAEModel.from_pretrained(lowercase__       )
                self.assertIsNotNone(lowercase__       )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Tuple     						=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     return image







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






      @cached_property
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

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






      @slow
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           np.random.seed(2       )

           a_     :							str     						=  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""       ).to(lowercase__       )

           a_     :							str     						=  self.default_image_processor
           a_     :							List[Any]     						=  prepare_img()
           a_     :							Tuple     						=  image_processor(images=lowercase__						,						return_tensors="""pt"""       ).to(lowercase__       )

           # 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_     :							str     						=  ViTMAEConfig()
           a_     :							Dict     						=  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
           a_     :							int     						=  np.random.uniform(size=(1, num_patches)       )

           # forward pass
           with torch.no_grad():
                a_     :							Any     						=  model(**lowercase__						,						noise=torch.from_numpy(lowercase__       ).to(device=lowercase__       )       )

           # verify the logits
           a_     :							List[Any]     						=  torch.Size((1, 196, 768)       )
           self.assertEqual(outputs.logits.shape						,						lowercase__       )

           a_     :							int     						=  torch.tensor(
               [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
