


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_ ( UpperCamelCase		,			unittest.TestCase   ):

   '''simple docstring'''




   __A			:				int							=		PaintByExamplePipeline
   __A			:				Dict							=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   __A			:				List[str]							=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __A			:				Tuple							=		frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






   def _snake_case   (		self			):






     """simple docstring"""





     torch.manual_seed(0			)
     lowerCamelCase			: Tuple   =    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						,    )
     lowerCamelCase			: int   =    PNDMScheduler(skip_prk_steps=__A			)
     torch.manual_seed(0			)
     lowerCamelCase			: int   =    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			)
     lowerCamelCase			: List[Any]   =    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						,    )
     lowerCamelCase			: Union[str, Any]   =    PaintByExampleImageEncoder(__A						,    proj_size=32			)
     lowerCamelCase			: Optional[int]   =    CLIPImageProcessor(crop_size=32						,    size=32			)

     lowerCamelCase			: Dict   =    {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "image_encoder": image_encoder,
         "safety_checker": None,
         "feature_extractor": feature_extractor,
     }
     return components






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    np.array(image.convert("RGB"			)			)
     lowerCamelCase			: int   =    image[None].transpose(0						,    3						,    1						,    2			)
     lowerCamelCase			: str   =    torch.from_numpy(__A			).to(dtype=torch.floataa			) / 127.5 - 1.0
     return image






   def _snake_case   (		self						,    __A="cpu"						,    __A=0			):






     """simple docstring"""





     lowerCamelCase			: int   =    floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__A			)			).to(__A			)
     lowerCamelCase			: str   =    image.cpu().permute(0						,    2						,    3						,    1			)[0]
     lowerCamelCase			: Optional[Any]   =    Image.fromarray(np.uinta(__A			)			).convert("RGB"			).resize((64, 64)			)
     lowerCamelCase			: int   =    Image.fromarray(np.uinta(image + 4			)			).convert("RGB"			).resize((64, 64)			)
     lowerCamelCase			: Union[str, Any]   =    Image.fromarray(np.uinta(__A			)			).convert("RGB"			).resize((32, 32)			)

     if str(__A			).startswith("mps"			):
       lowerCamelCase			: List[str]   =    torch.manual_seed(__A			)
     else:
       lowerCamelCase			: Any   =    torch.Generator(device=__A			).manual_seed(__A			)
     lowerCamelCase			: Any   =    {
         "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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     lowerCamelCase			: List[Any]   =    PaintByExamplePipeline(**__A			)
     lowerCamelCase			: int   =    pipe.to("cpu"			)
     pipe.set_progress_bar_config(disable=__A			)

     lowerCamelCase			: int   =    self.get_dummy_inputs()
     lowerCamelCase			: Union[str, Any]   =    pipe(**__A			)
     lowerCamelCase			: int   =    output.images

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

     assert image.shape == (1, 64, 64, 3)
     lowerCamelCase			: Union[str, Any]   =    np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]			)

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






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[Any]   =    "cpu"
     lowerCamelCase			: List[Any]   =    self.get_dummy_inputs()
     inputs.pop("mask_image"			)
     lowerCamelCase			: Dict   =    self.convert_to_pt(inputs.pop("image"			)			)
     lowerCamelCase			: List[Any]   =    image.clamp(0						,    1			) / 2

     # make sure here that pndm scheduler skips prk
     lowerCamelCase			: Optional[int]   =    PaintByExamplePipeline(**self.get_dummy_components()			)
     lowerCamelCase			: str   =    pipe.to(__A			)
     pipe.set_progress_bar_config(disable=__A			)

     lowerCamelCase			: int   =    pipe(image=__A						,    mask_image=mask_image[:, 0]						,    **__A			)
     lowerCamelCase			: List[str]   =    output.images

     lowerCamelCase			: Dict   =    image.cpu().permute(0						,    2						,    3						,    1			)[0]
     lowerCamelCase			: Optional[Any]   =    mask_image.cpu().permute(0						,    2						,    3						,    1			)[0]

     lowerCamelCase			: List[str]   =    Image.fromarray(np.uinta(__A			)			).convert("RGB"			)
     lowerCamelCase			: Dict   =    Image.fromarray(np.uinta(__A			)			).convert("RGB"			)

     lowerCamelCase			: Optional[int]   =    pipe(**self.get_dummy_inputs()			)
     lowerCamelCase			: Any   =    output.images

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






   def _snake_case   (		self			):






     """simple docstring"""





     super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/dog_in_bucket.png"			)
     lowerCamelCase			: Any   =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/mask.png"			)
     lowerCamelCase			: str   =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/panda.jpg"			)

     lowerCamelCase			: Tuple   =    PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"			)
     lowerCamelCase			: List[Any]   =    pipe.to(__A			)
     pipe.set_progress_bar_config(disable=__A			)

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

     lowerCamelCase			: List[Any]   =    output.images

     lowerCamelCase			: Tuple   =    image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase			: Tuple   =    np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]			)

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







def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  sequence.append(SCREAMING_SNAKE_CASE_     )

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

  sequence.pop()

  if index == len(SCREAMING_SNAKE_CASE_     ):
    return None

  return index


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

   _snake_case					=					input('''Enter a single number to be found in the list:\n''')
   _snake_case					=					int(target_input)
   _snake_case					=					sentinel_linear_search(sequence, target)
   if result is not None:
      print(f'''{target} found at positions: {result}''')
   else:
      print('''Not found''')



