




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_										=       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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












import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   RePaintPipeline
      lowerCAmelCase__     :	Optional[int]      =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      lowerCAmelCase__     :	Tuple      =   PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      lowerCAmelCase__     :	List[str]      =   IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__     :	Dict      =   False







      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  UNetaDModel(
              block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=3	,    out_channels=3	,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,    )
          __UpperCamelCase											=  RePaintScheduler()
          __UpperCamelCase											=  {'''unet''': unet, '''scheduler''': scheduler}
          return components







      def  snake_case		(       self   :	int	,    snake_case   :	str	,    snake_case   :	List[Any]=0			):
          if str(snake_case			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(snake_case			)
          else:
              __UpperCamelCase											=  torch.Generator(device=snake_case			).manual_seed(snake_case			)
          __UpperCamelCase											=  np.random.RandomState(snake_case			).standard_normal((1, 3, 32, 32)			)
          __UpperCamelCase											=  torch.from_numpy(snake_case			).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  (image > 0).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  {
              '''image''': image,
              '''mask_image''': mask,
              '''generator''': generator,
              '''num_inference_steps''': 5,
              '''eta''': 0.0,
              '''jump_length''': 2,
              '''jump_n_sample''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs







      def  snake_case		(       self   :	str			):
          __UpperCamelCase											=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase											=  self.get_dummy_components()
          __UpperCamelCase											=  RePaintPipeline(**snake_case			)
          __UpperCamelCase											=  sd_pipe.to(snake_case			)
          sd_pipe.set_progress_bar_config(disable=snake_case			)

          __UpperCamelCase											=  self.get_dummy_inputs(snake_case			)
          __UpperCamelCase											=  sd_pipe(**snake_case			).images
          __UpperCamelCase											=  image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3







      @skip_mps
      def  snake_case		(       self   :	str			):
          return super().test_save_load_local()







      @unittest.skip('''non-deterministic pipeline'''			)
      def  snake_case		(       self   :	Any			):
          return super().test_inference_batch_single_identical()







      @skip_mps
      def  snake_case		(       self   :	List[Any]			):
          return super().test_dict_tuple_outputs_equivalent()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_save_load_optional_components()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      def  snake_case		(       self   :	Dict			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
          __UpperCamelCase											=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256_result.npy'''			)

          __UpperCamelCase											=  '''google/ddpm-ema-celebahq-256'''
          __UpperCamelCase											=  UNetaDModel.from_pretrained(snake_case			)
          __UpperCamelCase											=  RePaintScheduler.from_pretrained(snake_case			)

          __UpperCamelCase											=  RePaintPipeline(unet=snake_case	,    scheduler=snake_case			).to(snake_case			)
          repaint.set_progress_bar_config(disable=snake_case			)
          repaint.enable_attention_slicing()

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  repaint(
              snake_case	,    snake_case	,    num_inference_steps=250	,    eta=0.0	,    jump_length=10	,    jump_n_sample=10	,    generator=snake_case	,    output_type='''np'''	,    )
          __UpperCamelCase											=  output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image			).mean() < 1E-2






