"""simple docstring"""




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
     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 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=30				,     a__=2				,     a__=3				,     a__=True				,     a__=True				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=10				,     a__=0.0_2				,     a__=None				,     a__=2				,     ):
       _lowerCAmelCase						:       List[Any]  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Tuple  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       int  			=     num_channels
       _lowerCAmelCase						:       List[str]  			=     is_training
       _lowerCAmelCase						:       Tuple  			=     use_labels
       _lowerCAmelCase						:       Any  			=     hidden_size
       _lowerCAmelCase						:       List[Any]  			=     num_hidden_layers
       _lowerCAmelCase						:       List[str]  			=     num_attention_heads
       _lowerCAmelCase						:       Optional[Any]  			=     intermediate_size
       _lowerCAmelCase						:       Any  			=     hidden_act
       _lowerCAmelCase						:       Tuple  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Optional[Any]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Any  			=     type_sequence_label_size
       _lowerCAmelCase						:       int  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     scope
       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_stride

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       _lowerCAmelCase						:       int  			=     (image_size // patch_size) ** 2
       _lowerCAmelCase						:       Optional[Any]  			=     num_patches + 1






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       _lowerCAmelCase						:       Dict  			=     self.get_config()

       return config, pixel_values, labels






     def     __A  (		self  ):
       return ViTConfig(
           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=a__				,     initializer_range=self.initializer_range				,     encoder_stride=self.encoder_stride				,     )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     ViTModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     model(a__  )
       self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     ViTForMaskedImageModeling(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[str]  			=     model(a__  )
       self.parent.assertEqual(
           result.reconstruction.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

       # test greyscale images
       _lowerCAmelCase						:       Union[str, Any]  			=     1
       _lowerCAmelCase						:       Union[str, Any]  			=     ViTForMaskedImageModeling(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       Any  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Dict  			=     model(a__  )
       self.parent.assertEqual(result.reconstruction.shape				,     (self.batch_size, 1, self.image_size, self.image_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.type_sequence_label_size
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       int  			=     model(a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )

       # test greyscale images
       _lowerCAmelCase						:       int  			=     1
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       List[str]  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Tuple  			=     model(a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       List[str]  			=     config_and_inputs
       _lowerCAmelCase						:       Optional[Any]  			=     {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			int							=		(
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Tuple							=		(
         {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Dict							=		True

     _UpperCamelCase							:			str							=		False
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			List[str]							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     has_text_modality=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     @unittest.skip(reason="""ViT does not use inputs_embeds"""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs_for_common()

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






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Optional[Any]  			=     model_class(a__  )
         _lowerCAmelCase						:       str  			=     inspect.signature(model.forward  )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						:       List[str]  			=     [*signature.parameters.keys()]

         _lowerCAmelCase						:       List[str]  			=     ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       List[str]  			=     ViTModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE     (			)       ->      List[str]:
  _lowerCAmelCase						:       Union[str, Any]  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image






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






     @cached_property
     def     __A  (		self  ):
       return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     self.default_image_processor
       _lowerCAmelCase						:       str  			=     prepare_img()
       _lowerCAmelCase						:       Tuple  			=     image_processor(images=a__				,     return_tensors="""pt"""  ).to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(**a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape				,     a__  )

       _lowerCAmelCase						:       List[Any]  			=     torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]  ).to(a__  )

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






     @slow
     def     __A  (		self  ):
       # ViT models have an `interpolate_pos_encoding` argument in their forward method,
       # allowing to interpolate the pre-trained position embeddings in order to use
       # the model on higher resolutions. The DINO model by Facebook AI leverages this
       # to visualize self-attention on higher resolution images.
       _lowerCAmelCase						:       Optional[Any]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""				,     size=480  )
       _lowerCAmelCase						:       Any  			=     prepare_img()
       _lowerCAmelCase						:       Dict  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     inputs.pixel_values.to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       str  			=     model(a__				,     interpolate_pos_encoding=a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 3601, 384)  )
       self.assertEqual(outputs.last_hidden_state.shape				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor(
           [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]  ).to(a__  )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,     a__				,     atol=1e-4  )  )






     @slow
     @require_accelerate
     @require_torch_gpu
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""				,     torch_dtype=torch.floataa				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.default_image_processor

       _lowerCAmelCase						:       Dict  			=     prepare_img()
       _lowerCAmelCase						:       List[str]  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Any  			=     inputs.pixel_values.to(a__  )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(a__  )





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        SCREAMING_SNAKE_CASE_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        assert (
            np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any      )     ->		Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


