




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class       _UpperCamelCase   (					lowerCAmelCase						,							unittest.TestCase		):
      UpperCAmelCase_          =		PaintByExamplePipeline
      UpperCAmelCase_          =		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_          =		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_          =		frozenset([]		)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
      def 						UpperCAmelCase_    (      self :Tuple   )  ->      Tuple:
       torch.manual_seed(0   )
       UpperCAmelCase__							=    UNetaDConditionModel(
           block_out_channels=(32, 64)			,   layers_per_block=2			,   sample_size=32			,   in_channels=9			,   out_channels=4			,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,   cross_attention_dim=32			,   )
       UpperCAmelCase__							=    PNDMScheduler(skip_prk_steps=lowerCamelCase   )
       torch.manual_seed(0   )
       UpperCAmelCase__							=    AutoencoderKL(
           block_out_channels=[32, 64]			,   in_channels=3			,   out_channels=3			,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,   latent_channels=4			,   )
       torch.manual_seed(0   )
       UpperCAmelCase__							=    CLIPVisionConfig(
           hidden_size=32			,   projection_dim=32			,   intermediate_size=37			,   layer_norm_eps=1e-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   image_size=32			,   patch_size=4			,   )
       UpperCAmelCase__							=    PaintByExampleImageEncoder(lowerCamelCase			,   proj_size=32   )
       UpperCAmelCase__							=    CLIPImageProcessor(crop_size=32			,   size=32   )

       UpperCAmelCase__							=    {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "image_encoder": image_encoder,
           "safety_checker": None,
           "feature_extractor": feature_extractor,
       }
       return components
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :List[str]   )  ->      int:
       UpperCAmelCase__							=    np.array(image.convert("RGB"   )   )
       UpperCAmelCase__							=    image[None].transpose(0			,   3			,   1			,   2   )
       UpperCAmelCase__							=    torch.from_numpy(lowerCamelCase   ).to(dtype=torch.floataa   ) / 1_27.5 - 1.0
       return image
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Optional[int]="cpu"			,   lowerCamelCase :Any=0   )  ->      Optional[int]:
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       UpperCAmelCase__							=    floats_tensor((1, 3, 32, 32)			,   rng=random.Random(lowerCamelCase   )   ).to(lowerCamelCase   )
       UpperCAmelCase__							=    image.cpu().permute(0			,   2			,   3			,   1   )[0]
       UpperCAmelCase__							=    Image.fromarray(np.uinta(lowerCamelCase   )   ).convert("RGB"   ).resize((64, 64)   )
       UpperCAmelCase__							=    Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
       UpperCAmelCase__							=    Image.fromarray(np.uinta(lowerCamelCase   )   ).convert("RGB"   ).resize((32, 32)   )

       if str(lowerCamelCase   ).startswith("mps"   ):
        UpperCAmelCase__							=    torch.manual_seed(lowerCamelCase   )
       else:
        UpperCAmelCase__							=    torch.Generator(device=lowerCamelCase   ).manual_seed(lowerCamelCase   )
       UpperCAmelCase__							=    {
           "example_image": example_image,
           "image": init_image,
           "mask_image": mask_image,
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
       }
       return inputs
      def 						UpperCAmelCase_    (      self :str   )  ->      Optional[Any]:
       UpperCAmelCase__							=    self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase__							=    PaintByExamplePipeline(**lowerCamelCase   )
       UpperCAmelCase__							=    pipe.to("cpu"   )
       pipe.set_progress_bar_config(disable=lowerCamelCase   )

       UpperCAmelCase__							=    self.get_dummy_inputs()
       UpperCAmelCase__							=    pipe(**lowerCamelCase   )
       UpperCAmelCase__							=    output.images

       UpperCAmelCase__							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase__							=    np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      def 						UpperCAmelCase_    (      self :int   )  ->      Optional[int]:
       UpperCAmelCase__							=    "cpu"
       UpperCAmelCase__							=    self.get_dummy_inputs()
       inputs.pop("mask_image"   )
       UpperCAmelCase__							=    self.convert_to_pt(inputs.pop("image"   )   )
       UpperCAmelCase__							=    image.clamp(0			,   1   ) / 2

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase__							=    PaintByExamplePipeline(**self.get_dummy_components()   )
       UpperCAmelCase__							=    pipe.to(lowerCamelCase   )
       pipe.set_progress_bar_config(disable=lowerCamelCase   )

       UpperCAmelCase__							=    pipe(image=lowerCamelCase			,   mask_image=mask_image[:, 0]			,   **lowerCamelCase   )
       UpperCAmelCase__							=    output.images

       UpperCAmelCase__							=    image.cpu().permute(0			,   2			,   3			,   1   )[0]
       UpperCAmelCase__							=    mask_image.cpu().permute(0			,   2			,   3			,   1   )[0]

       UpperCAmelCase__							=    Image.fromarray(np.uinta(lowerCamelCase   )   ).convert("RGB"   )
       UpperCAmelCase__							=    Image.fromarray(np.uinta(lowerCamelCase   )   ).convert("RGB"   )

       UpperCAmelCase__							=    pipe(**self.get_dummy_inputs()   )
       UpperCAmelCase__							=    output.images

       assert out_a.shape == (1, 64, 64, 3)
       assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2







      def 						UpperCAmelCase_    (      self :Dict   )  ->      Dict:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



@slow
@require_torch_gpu
class       _UpperCamelCase   (					unittest.TestCase		):
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Union[str, Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      List[Any]:
       # make sure here that pndm scheduler skips prk
       UpperCAmelCase__							=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/paint_by_example/dog_in_bucket.png"   )
       UpperCAmelCase__							=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/paint_by_example/mask.png"   )
       UpperCAmelCase__							=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/paint_by_example/panda.jpg"   )

       UpperCAmelCase__							=    PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
       UpperCAmelCase__							=    pipe.to(lowerCamelCase   )
       pipe.set_progress_bar_config(disable=lowerCamelCase   )

       UpperCAmelCase__							=    torch.manual_seed(321   )
       UpperCAmelCase__							=    pipe(
           image=lowerCamelCase			,   mask_image=lowerCamelCase			,   example_image=lowerCamelCase			,   generator=lowerCamelCase			,   guidance_scale=5.0			,   num_inference_steps=50			,   output_type="np"			,   )

       UpperCAmelCase__							=    output.images

       UpperCAmelCase__							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase__							=    np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2











def     lowerCAmelCase		(		_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: Tuple						):




 """simple docstring"""







 sequence.append(_lowerCAmelCase						)

 UpperCAmelCase__							=    0
 while sequence[index] != target:
  index += 1

 sequence.pop()

 if index == len(_lowerCAmelCase						):
  return None

 return index


if __name__ == "__main__":
  _lowerCAmelCase    : List[Any]		= input("Enter numbers separated by comma:\n").strip()
  _lowerCAmelCase    : Dict		= [int(item) for item in user_input.split(",")]

  _lowerCAmelCase    : Optional[Any]		= input("Enter a single number to be found in the list:\n")
  _lowerCAmelCase    : Tuple		= int(target_input)
  _lowerCAmelCase    : int		= sentinel_linear_search(sequence, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print("Not found")





