





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  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    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       snake_case_(		self	)		->       List[str]:
        torch.manual_seed(0	)
        _SCREAMING_SNAKE_CASE     =      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			,		)
        _SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=A	)
        torch.manual_seed(0	)
        _SCREAMING_SNAKE_CASE     =      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	)
        _SCREAMING_SNAKE_CASE     =      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			,		)
        _SCREAMING_SNAKE_CASE     =      PaintByExampleImageEncoder(A			,		proj_size=32	)
        _SCREAMING_SNAKE_CASE     =      CLIPImageProcessor(crop_size=32			,		size=32	)

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


    def       snake_case_(		self			,		A	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      np.array(image.convert("""RGB"""	)	)
        _SCREAMING_SNAKE_CASE     =      image[None].transpose(0			,		3			,		1			,		2	)
        _SCREAMING_SNAKE_CASE     =      torch.from_numpy(A	).to(dtype=torch.floataa	) / 127.5 - 1.0
        return image


    def       snake_case_(		self			,		A="cpu"			,		A=0	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)			,		rng=random.Random(A	)	).to(A	)
        _SCREAMING_SNAKE_CASE     =      image.cpu().permute(0			,		2			,		3			,		1	)[0]
        _SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(A	)	).convert("""RGB"""	).resize((64, 64)	)
        _SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4	)	).convert("""RGB"""	).resize((64, 64)	)
        _SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(A	)	).convert("""RGB"""	).resize((32, 32)	)

        if str(A	).startswith("""mps"""	):
            _SCREAMING_SNAKE_CASE     =      torch.manual_seed(A	)
        else:
            _SCREAMING_SNAKE_CASE     =      torch.Generator(device=A	).manual_seed(A	)
        _SCREAMING_SNAKE_CASE     =      {
            """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	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      self.get_dummy_components()

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

        _SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs()
        _SCREAMING_SNAKE_CASE     =      pipe(**A	)
        _SCREAMING_SNAKE_CASE     =      output.images

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

        assert image.shape == (1, 64, 64, 3)
        _SCREAMING_SNAKE_CASE     =      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	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      """cpu"""
        _SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs()
        inputs.pop("""mask_image"""	)
        _SCREAMING_SNAKE_CASE     =      self.convert_to_pt(inputs.pop("""image"""	)	)
        _SCREAMING_SNAKE_CASE     =      image.clamp(0			,		1	) / 2

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

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

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

        _SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(A	)	).convert("""RGB"""	)
        _SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(A	)	).convert("""RGB"""	)

        _SCREAMING_SNAKE_CASE     =      pipe(**self.get_dummy_inputs()	)
        _SCREAMING_SNAKE_CASE     =      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	)		->       str:
        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	)		->       Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





    def       snake_case_(		self	)		->       int:
        _SCREAMING_SNAKE_CASE     =      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/dog_in_bucket.png"""	)
        _SCREAMING_SNAKE_CASE     =      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/mask.png"""	)
        _SCREAMING_SNAKE_CASE     =      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/panda.jpg"""	)

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

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

        _SCREAMING_SNAKE_CASE     =      output.images

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

        assert image.shape == (1, 512, 512, 3)
        _SCREAMING_SNAKE_CASE     =      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










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__lowerCamelCase   ) - 1:
        return True

    _SCREAMING_SNAKE_CASE     =      [(0, 1), (0, -1), (-1, 0), (1, 0)]
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for direction in traverts_directions:
        _SCREAMING_SNAKE_CASE     =      row + direction[0]
        _SCREAMING_SNAKE_CASE     =      column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        _SCREAMING_SNAKE_CASE     =      get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
        if key in visited_points_set:
            continue

        visited_points_set.add(__lowerCamelCase   )
        if exits_word(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	word_index + 1 ,	__lowerCamelCase   ):
            return True

        visited_points_set.remove(__lowerCamelCase   )

    return False





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(__lowerCamelCase   )

    for row in board:
        if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
            raise ValueError(__lowerCamelCase   )

        for item in row:
            if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) != 1:
                raise ValueError(__lowerCamelCase   )

    # Validate word
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(
            """The word parameter should be a string of length greater than 0."""   )

    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for i in range(__lowerCamelCase   ):
        for j in range(__lowerCamelCase   ):
            if exits_word(
                __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	0 ,	{get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )}   ):
                return True

    return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



