





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 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			PaintByExamplePipeline
       __snake_case       	=			IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       __snake_case       	=			IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __snake_case       	=			frozenset([]			)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							UNetaDConditionModel(
                block_out_channels=(3_2, 6_4)			,  layers_per_block=2			,  sample_size=3_2			,  in_channels=9			,  out_channels=4			,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,  cross_attention_dim=3_2			,  )
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							PNDMScheduler(skip_prk_steps=__lowerCamelCase							)
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							AutoencoderKL(
                block_out_channels=[3_2, 6_4]			,  in_channels=3			,  out_channels=3			,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,  latent_channels=4			,  )
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							CLIPVisionConfig(
                hidden_size=3_2			,  projection_dim=3_2			,  intermediate_size=3_7			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  image_size=3_2			,  patch_size=4			,  )
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							PaintByExampleImageEncoder(__lowerCamelCase			,  proj_size=3_2							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							CLIPImageProcessor(crop_size=3_2			,  size=3_2							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							{
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "image_encoder": image_encoder,
                "safety_checker": None,
                "feature_extractor": feature_extractor,
            }
            return components




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		str  			=							np.array(image.convert("RGB"							)							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							image[None].transpose(0			,  3			,  1			,  2							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							torch.from_numpy(__lowerCamelCase							).to(dtype=torch.floataa							) / 127.5 - 1.0
            return image




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase="cpu"			,  __lowerCamelCase=0							)	->   Optional[int]:
            # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
            _SCREAMING_SNAKE_CASE				:		Any  			=							floats_tensor((1, 3, 3_2, 3_2)			,  rng=random.Random(__lowerCamelCase							)							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		int  			=							image.cpu().permute(0			,  2			,  3			,  1							)[0]
            _SCREAMING_SNAKE_CASE				:		str  			=							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							).resize((6_4, 6_4)							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							Image.fromarray(np.uinta(image + 4							)							).convert("RGB"							).resize((6_4, 6_4)							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							).resize((3_2, 3_2)							)

            if str(__lowerCamelCase							).startswith("mps"							):
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							torch.manual_seed(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							{
                "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							)	->   List[str]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_dummy_components()

            # make sure here that pndm scheduler skips prk
            _SCREAMING_SNAKE_CASE				:		int  			=							PaintByExamplePipeline(**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							pipe.to("cpu"							)
            pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_dummy_inputs()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							pipe(**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							output.images

            _SCREAMING_SNAKE_CASE				:		Any  			=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"cpu"
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.get_dummy_inputs()
            inputs.pop("mask_image"							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.convert_to_pt(inputs.pop("image"							)							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							image.clamp(0			,  1							) / 2

            # make sure here that pndm scheduler skips prk
            _SCREAMING_SNAKE_CASE				:		str  			=							PaintByExamplePipeline(**self.get_dummy_components()							)
            _SCREAMING_SNAKE_CASE				:		str  			=							pipe.to(__lowerCamelCase							)
            pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Tuple  			=							pipe(image=__lowerCamelCase			,  mask_image=mask_image[:, 0]			,  **__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		int  			=							output.images

            _SCREAMING_SNAKE_CASE				:		int  			=							image.cpu().permute(0			,  2			,  3			,  1							)[0]
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							mask_image.cpu().permute(0			,  2			,  3			,  1							)[0]

            _SCREAMING_SNAKE_CASE				:		Tuple  			=							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							)

            _SCREAMING_SNAKE_CASE				:		int  			=							pipe(**self.get_dummy_inputs()							)
            _SCREAMING_SNAKE_CASE				:		str  			=							output.images

            assert out_a.shape == (1, 6_4, 6_4, 3)
            assert np.abs(out_a.flatten() - out_a.flatten()							).max() < 5E-2







       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            super().test_inference_batch_single_identical(expected_max_diff=3E-3							)





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






       '''simple docstring'''


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







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

            _SCREAMING_SNAKE_CASE				:		Any  			=							PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							pipe.to(__lowerCamelCase							)
            pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							torch.manual_seed(3_2_1							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							pipe(
                image=__lowerCamelCase			,  mask_image=__lowerCamelCase			,  example_image=__lowerCamelCase			,  generator=__lowerCamelCase			,  guidance_scale=5.0			,  num_inference_steps=5_0			,  output_type="np"			,  )

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							output.images

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							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






from typing import Literal

UpperCamelCase__          ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							LETTERS if mode == "decrypt" else key
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							key if mode == "decrypt" else LETTERS
     _SCREAMING_SNAKE_CASE				:		Dict  			=							""
     # loop through each symbol in the message
     for symbol in message:
          if symbol.upper() in chars_a:
               # encrypt/decrypt the symbol
               _SCREAMING_SNAKE_CASE				:		List[str]  			=							chars_a.find(symbol.upper()		)
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               # symbol is not in LETTERS, just add it
               translated += symbol
     return translated






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     return translate_message(__lowerCamelCase,				__lowerCamelCase,				"encrypt"		)






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     return translate_message(__lowerCamelCase,				__lowerCamelCase,				"decrypt"		)






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		str  			=							"Hello World"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"QWERTYUIOPASDFGHJKLZXCVBNM"
     _SCREAMING_SNAKE_CASE				:		Any  			=							"decrypt"  # set to 'encrypt' or 'decrypt'

     if mode == "encrypt":
          _SCREAMING_SNAKE_CASE				:		int  			=							encrypt_message(__lowerCamelCase,				__lowerCamelCase		)
     elif mode == "decrypt":
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							decrypt_message(__lowerCamelCase,				__lowerCamelCase		)
     print(f"""Using the key {key}, the {mode}ed message is: {translated}"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()