

'''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 						lowerCAmelCase__   :



 """simple docstring"""



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


      """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       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """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       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[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=__SCREAMING_SNAKE_CASE					,							initializer_range=self.initializer_range					,							mask_ratio=self.mask_ratio					,							)
 def       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ViTMAEModel(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)			)
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ViTMAEForPreTraining(__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      __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(__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.patch_size**2
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, num_patches, expected_num_channels)			)


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, Any]:


      """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 						lowerCAmelCase__   (      a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 lowerCAmelCase__  					=      {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ViTMAEModelTester(self			)
      __SCREAMING_SNAKE_CASE     =      ConfigTester(self					,							config_class=__SCREAMING_SNAKE_CASE					,							has_text_modality=__SCREAMING_SNAKE_CASE					,							hidden_size=37			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> int:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      pass
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> 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(__SCREAMING_SNAKE_CASE			)
           self.assertIsInstance(model.get_input_embeddings()					,							(nn.Module)			)
           __SCREAMING_SNAKE_CASE     =      model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE					,							nn.Linear			)			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> 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(__SCREAMING_SNAKE_CASE			)
           __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]					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> List[Any]:


      """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(__SCREAMING_SNAKE_CASE			)

      # 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(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """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(__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           # make random mask reproducible
           torch.manual_seed(2			)
           with torch.no_grad():
                __SCREAMING_SNAKE_CASE     =      model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

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

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

                # 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(__SCREAMING_SNAKE_CASE					,							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       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[str]:


      """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       UpperCAmelCase__		(		self					:       Any			)  -> Dict:


      """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       UpperCAmelCase__		(		self					:       Dict			)  -> int:


      """simple docstring"""




      pass
 @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      pass
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Dict:


      """simple docstring"""




      pass


 @slow
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Dict:


      """simple docstring"""




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







def    a__       (  ):




     """simple docstring"""


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







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



 """simple docstring"""



 @cached_property
 def       UpperCAmelCase__		(		self					:       str			)  -> Dict:


      """simple docstring"""




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


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


      """simple docstring"""




      np.random.seed(2			)

      __SCREAMING_SNAKE_CASE     =      ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""			).to(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.default_image_processor
      __SCREAMING_SNAKE_CASE     =      prepare_img()
      __SCREAMING_SNAKE_CASE     =      image_processor(images=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).to(__SCREAMING_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)
      __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(**__SCREAMING_SNAKE_CASE					,							noise=torch.from_numpy(__SCREAMING_SNAKE_CASE			).to(device=__SCREAMING_SNAKE_CASE			)			)

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

      __SCREAMING_SNAKE_CASE     =      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(__SCREAMING_SNAKE_CASE			)					,							atol=1E-4			)			)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






