





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							:







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






     '''simple docstring'''





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

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







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





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

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

     lowercase__			       =  self.get_config()

     return config, pixel_values, labels







  def   UpperCAmelCase					(      self  :Dict ):






     '''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   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :int   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





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

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







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.prepare_config_and_inputs()
     lowercase__       ,							lowercase__       ,							lowercase__			       =  config_and_inputs
     lowercase__			       =  {"pixel_values": pixel_values}
     return config, inputs_dict





@require_torch
class  lowerCAmelCase							(       lowercase_							,							lowercase_							,							unittest.TestCase ):
  __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] ):






     '''simple docstring'''





     lowercase__			       =  ViTMAEModelTester(self )
     lowercase__			       =  ConfigTester(self   ,							config_class=_lowercase   ,							has_text_modality=_lowercase   ,							hidden_size=37 )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     self.config_tester.run_common_tests()







  @unittest.skip(reason="ViTMAE does not use inputs_embeds" )
  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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







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






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

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







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Any ):






     '''simple docstring'''





     np.random.seed(2 )

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

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

     super().check_pt_tf_models(_lowercase   ,							_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

           # Make sure we don't have nans
           lowercase__			       =  after_outputs[0].cpu().numpy()
           lowercase__			       =  0
           lowercase__			       =  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\n    to get deterministic results." )
  def   UpperCAmelCase					(      self  :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   UpperCAmelCase					(      self  :Any ):






     '''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   UpperCAmelCase					(      self  :Optional[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  :Dict ):






     '''simple docstring'''





     pass







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






     '''simple docstring'''





     pass







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






     '''simple docstring'''





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


def _A       (   ):
   lowercase__			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
   return image







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







  @cached_property
  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





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







  @slow
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     np.random.seed(2 )

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

     lowercase__			       =  self.default_image_processor
     lowercase__			       =  prepare_img()
     lowercase__			       =  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)
     lowercase__			       =  ViTMAEConfig()
     lowercase__			       =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
     lowercase__			       =  np.random.uniform(size=(1, num_patches) )

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

     # verify the logits
     lowercase__			       =  torch.Size((1, 1_96, 7_68) )
     self.assertEqual(outputs.logits.shape   ,							_lowercase )

     lowercase__			       =  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 ) )













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case           =			{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  _snake_case           =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






