






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			:
     '''simple docstring'''


     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Any=13					, UpperCAmelCase_       :	str=30					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	Optional[Any]=3					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Dict=5					, UpperCAmelCase_       :	Union[str, Any]=4					, UpperCAmelCase_       :	str=37					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	int=10					, UpperCAmelCase_       :	int=0.02					, UpperCAmelCase_       :	List[str]=3					, UpperCAmelCase_       :	str=0.6					, UpperCAmelCase_       :	Tuple=None					, ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    parent
       SCREAMING_SNAKE_CASE       :      List[str]							=    batch_size
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_size
       SCREAMING_SNAKE_CASE       :      Any							=    patch_size
       SCREAMING_SNAKE_CASE       :      int							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    is_training
       SCREAMING_SNAKE_CASE       :      Dict							=    use_labels
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_size
       SCREAMING_SNAKE_CASE       :      List[str]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      str							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    type_sequence_label_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      str							=    mask_ratio
       SCREAMING_SNAKE_CASE       :      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)
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE       :      Dict							=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      List[str]							=    ids_tensor([self.batch_size]					, self.type_sequence_label_size      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_config()

       return config, pixel_values, labels


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


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






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      Dict							=    {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


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

     UpperCamelCase_							:		Tuple			=	False
     UpperCamelCase_							:		List[str]			=	False
     UpperCamelCase_							:		List[Any]			=	False
     UpperCamelCase_							:		Optional[int]			=	False


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ViTMAEModelTester(self      )
       SCREAMING_SNAKE_CASE       :      Dict							=    ConfigTester(self					, config_class=UpperCAmelCase_					, has_text_modality=UpperCAmelCase_					, hidden_size=37      )


     def 				_A       (				self       :	Optional[int]      ):
       self.config_tester.run_common_tests()


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


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.model_tester.prepare_config_and_inputs_for_common()

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


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ["pixel_values"]
         self.assertListEqual(arg_names[:1]					, UpperCAmelCase_      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_      )


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple      ):
       # make masks reproducible
       np.random.seed(2      )

       SCREAMING_SNAKE_CASE       :      Dict							=    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2      )
       SCREAMING_SNAKE_CASE       :      int							=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.from_numpy(UpperCAmelCase_      )

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

       super().check_pt_tf_models(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE       :      List[str]							=    outputs[0].cpu().numpy()
         SCREAMING_SNAKE_CASE       :      str							=    0

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

           # Make sure we don't have nans
           SCREAMING_SNAKE_CASE       :      List[Any]							=    after_outputs[0].cpu().numpy()
           SCREAMING_SNAKE_CASE       :      List[str]							=    0
           SCREAMING_SNAKE_CASE       :      Dict							=    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\n    to get deterministic results."      )
     def 				_A       (				self       :	Any      ):
       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 				_A       (				self       :	Optional[int]      ):
       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 				_A       (				self       :	str      ):
       pass


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


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






     @slow
     def 				_A       (				self       :	int      ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE       :      str							=    ViTMAEModel.from_pretrained(UpperCAmelCase_      )
         self.assertIsNotNone(UpperCAmelCase_      )





def   lowerCamelCase__			(     ):

  """simple docstring"""






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


@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @cached_property
     def 				_A       (				self       :	Tuple      ):
       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"      ) if is_vision_available() else None






     @slow
     def 				_A       (				self       :	Tuple      ):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"      ).to(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.default_image_processor
       SCREAMING_SNAKE_CASE       :      List[str]							=    prepare_img()
       SCREAMING_SNAKE_CASE       :      Dict							=    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)
       SCREAMING_SNAKE_CASE       :      str							=    ViTMAEConfig()
       SCREAMING_SNAKE_CASE       :      str							=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.random.uniform(size=(1, num_patches)      )

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

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

       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(
           [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]      )

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














from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .multicontrolnet import MultiControlNetModel
   from .pipeline_controlnet import StableDiffusionControlNetPipeline
   from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
   from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
   from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






