




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 						_A  (  UpperCamelCase__   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Tuple     =	KandinskyVaaInpaintPipeline
    UpperCAmelCase	:							List[str]     =	["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    UpperCAmelCase	:							Optional[Any]     =	[
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    UpperCAmelCase	:							Any     =	[
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase	:							List[Any]     =	False






    @property
    def      __snake_case							( self    :   List[str]):
      return 32






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      return 32






    @property
    def      __snake_case							( self    :   int):
      return self.time_input_dim






    @property
    def      __snake_case							( self    :   int):
      return self.time_input_dim * 4






    @property
    def      __snake_case							( self    :   int):
      return 100






    @property
    def      __snake_case							( self    :   List[Any]):
      torch.manual_seed(0)

      a						:		str											=      {
          "in_channels": 9,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image",
          "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,
      }

      a						:		List[Any]											=      UNetaDConditionModel(**__lowerCamelCase)
      return model






    @property
    def      __snake_case							( self    :   Tuple):
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["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",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def      __snake_case							( self    :   Any):
      torch.manual_seed(0)
      a						:		List[Any]											=      VQModel(**self.dummy_movq_kwargs)
      return model






    def      __snake_case							( self    :   Union[str, Any]):
      a						:		List[Any]											=      self.dummy_unet
      a						:		List[Any]											=      self.dummy_movq

      a						:		List[Any]											=      DDIMScheduler(
          num_train_timesteps=1000    ,			beta_schedule="linear"    ,			beta_start=0.00_085    ,			beta_end=0.012    ,			clip_sample=__lowerCamelCase    ,			set_alpha_to_one=__lowerCamelCase    ,			steps_offset=1    ,			prediction_type="epsilon"    ,			thresholding=__lowerCamelCase    ,			)

      a						:		Any											=      {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Tuple=0):
      a						:		str											=      floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(__lowerCamelCase)).to(__lowerCamelCase)
      a						:		int											=      floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(seed + 1)).to(
          __lowerCamelCase)
      # create init_image
      a						:		Any											=      floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(__lowerCamelCase)).to(__lowerCamelCase)
      a						:		Dict											=      image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
      a						:		str											=      Image.fromarray(np.uinta(__lowerCamelCase)).convert("RGB").resize((256, 256))
      # create mask
      a						:		List[str]											=      np.ones((64, 64)    ,			dtype=np.floataa)
      a						:		Optional[Any]											=      0

      if str(__lowerCamelCase).startswith("mps"):
        a						:		str											=      torch.manual_seed(__lowerCamelCase)
      else:
        a						:		int											=      torch.Generator(device=__lowerCamelCase).manual_seed(__lowerCamelCase)
      a						:		Union[str, Any]											=      {
          "image": init_image,
          "mask_image": mask,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "num_inference_steps": 2,
          "guidance_scale": 4.0,
          "output_type": "np",
      }
      return inputs






    def      __snake_case							( self    :   int):
      a						:		Optional[Any]											=      "cpu"

      a						:		Union[str, Any]											=      self.get_dummy_components()

      a						:		Dict											=      self.pipeline_class(**__lowerCamelCase)
      a						:		Any											=      pipe.to(__lowerCamelCase)

      pipe.set_progress_bar_config(disable=__lowerCamelCase)

      a						:		int											=      pipe(**self.get_dummy_inputs(__lowerCamelCase))
      a						:		Optional[int]											=      output.images

      a						:		Tuple											=      pipe(
          **self.get_dummy_inputs(__lowerCamelCase)    ,			return_dict=__lowerCamelCase    ,			)[0]

      a						:		str											=      image[0, -3:, -3:, -1]
      a						:		Union[str, Any]											=      image_from_tuple[0, -3:, -3:, -1]

      print(f'''image.shape {image.shape}''')

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

      a						:		Optional[Any]											=      np.array(
          [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848])

      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()}'''






    def      __snake_case							( self    :   Union[str, Any]):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






@slow
@require_torch_gpu
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def      __snake_case							( self    :   List[str]):
      a						:		Optional[int]											=      load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy")

      a						:		Any											=      load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
      a						:		Tuple											=      np.ones((768, 768)    ,			dtype=np.floataa)
      a						:		Optional[Any]											=      0

      a						:		Dict											=      "a hat"

      a						:		Optional[int]											=      KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"    ,			torch_dtype=torch.floataa)
      pipe_prior.to(__lowerCamelCase)

      a						:		List[Any]											=      KandinskyVaaInpaintPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-decoder-inpaint"    ,			torch_dtype=torch.floataa)
      a						:		Any											=      pipeline.to(__lowerCamelCase)
      pipeline.set_progress_bar_config(disable=__lowerCamelCase)

      a						:		Union[str, Any]											=      torch.Generator(device="cpu").manual_seed(0)
      a							,		a						:		Union[str, Any]											=      pipe_prior(
          __lowerCamelCase    ,			generator=__lowerCamelCase    ,			num_inference_steps=5    ,			negative_prompt=""    ,			).to_tuple()

      a						:		List[Any]											=      pipeline(
          image=__lowerCamelCase    ,			mask_image=__lowerCamelCase    ,			image_embeds=__lowerCamelCase    ,			negative_image_embeds=__lowerCamelCase    ,			generator=__lowerCamelCase    ,			num_inference_steps=100    ,			height=768    ,			width=768    ,			output_type="np"    ,			)

      a						:		Optional[int]											=      output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__lowerCamelCase    ,			__lowerCamelCase)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

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






