



'''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  ,__UpperCAmelCase  ,__UpperCAmelCase=13  ,__UpperCAmelCase=30  ,__UpperCAmelCase=2  ,__UpperCAmelCase=3  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=32  ,__UpperCAmelCase=5  ,__UpperCAmelCase=4  ,__UpperCAmelCase=37  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=10  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=3  ,__UpperCAmelCase=0.6  ,__UpperCAmelCase=None  ,)   ->		Optional[int]:
      lowerCAmelCase__			:  Dict											= parent
      lowerCAmelCase__			:  Optional[int]											= batch_size
      lowerCAmelCase__			:  Union[str, Any]											= image_size
      lowerCAmelCase__			:  Union[str, Any]											= patch_size
      lowerCAmelCase__			:  Optional[Any]											= num_channels
      lowerCAmelCase__			:  List[Any]											= is_training
      lowerCAmelCase__			:  Union[str, Any]											= use_labels
      lowerCAmelCase__			:  Union[str, Any]											= hidden_size
      lowerCAmelCase__			:  Any											= num_hidden_layers
      lowerCAmelCase__			:  Tuple											= num_attention_heads
      lowerCAmelCase__			:  Optional[int]											= intermediate_size
      lowerCAmelCase__			:  Optional[int]											= hidden_act
      lowerCAmelCase__			:  Optional[int]											= hidden_dropout_prob
      lowerCAmelCase__			:  int											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Dict											= type_sequence_label_size
      lowerCAmelCase__			:  Dict											= initializer_range
      lowerCAmelCase__			:  Tuple											= mask_ratio
      lowerCAmelCase__			:  Tuple											= scope

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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  int											= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

      lowerCAmelCase__			:  str											= self.get_config()

      return config, pixel_values, labels




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      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=__UpperCAmelCase  ,initializer_range=self.initializer_range  ,mask_ratio=self.mask_ratio  ,)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		str:
      lowerCAmelCase__			:  List[Any]											= ViTMAEModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		str:
      lowerCAmelCase__			:  List[str]											= ViTMAEForPreTraining(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= (self.image_size // self.patch_size) ** 2
      lowerCAmelCase__			:  Dict											= self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, num_patches, expected_num_channels)				)

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






  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  int											= self.prepare_config_and_inputs()
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= config_and_inputs
      lowerCAmelCase__			:  Tuple											= {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  __lowercase							:				List[str]           =				{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

  __lowercase							:				Any           =				False
  __lowercase							:				Any           =				False
  __lowercase							:				Optional[int]           =				False
  __lowercase							:				List[str]           =				False




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  Union[str, Any]											= ViTMAEModelTester(self				)
      lowerCAmelCase__			:  int											= ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase  ,hidden_size=37				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self.config_tester.run_common_tests()




  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""				)
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__				,       lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs_for_common()

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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__			:  Optional[int]											= ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
      # make masks reproducible
      np.random.seed(2				)

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

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

      super().check_pt_tf_models(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__			:  Union[str, Any]											= outputs[0].cpu().numpy()
          lowerCAmelCase__			:  Union[str, Any]											= 0

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

              # Make sure we don't have nans
              lowerCAmelCase__			:  List[str]											= after_outputs[0].cpu().numpy()
              lowerCAmelCase__			:  List[str]											= 0
              lowerCAmelCase__			:  Any											= np.amax(np.abs(out_a - out_a				)				)
              self.assertLessEqual(__UpperCAmelCase  ,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				)   ->		List[Any]:
      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:
      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				)   ->		Optional[int]:
      pass




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




  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__			:  Union[str, Any]											= ViTMAEModel.from_pretrained(__UpperCAmelCase				)
          self.assertIsNotNone(__UpperCAmelCase				)







def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  int											= 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				)   ->		List[Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""				) if is_vision_available() else None






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		str:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2				)

      lowerCAmelCase__			:  Dict											= ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= self.default_image_processor
      lowerCAmelCase__			:  Union[str, Any]											= prepare_img()
      lowerCAmelCase__			:  str											= image_processor(images=__UpperCAmelCase  ,return_tensors="""pt"""				).to(__UpperCAmelCase				)

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

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

      # verify the logits
      lowerCAmelCase__			:  List[str]											= torch.Size((1, 196, 768)				)
      self.assertEqual(outputs.logits.shape  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= torch.tensor(
          [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]				)

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







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 _lowerCAmelCase										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


