





from __future__ import annotations

_UpperCAmelCase				:       str	   =				list[tuple[int, int]]

_UpperCAmelCase				:       int	   =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_UpperCAmelCase				:       Tuple	   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      float			,     UpperCAmelCase  :      Node | None			,     )			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		pos_x
     lowerCamelCase__    :      List[Any]					 =		pos_y
     lowerCamelCase__    :      Union[str, Any]					 =		(pos_y, pos_x)
     lowerCamelCase__    :      Optional[Any]					 =		goal_x
     lowerCamelCase__    :      Optional[int]					 =		goal_y
     lowerCamelCase__    :      str					 =		g_cost
     lowerCamelCase__    :      int					 =		parent
     lowerCamelCase__    :      Union[str, Any]					 =		self.calculate_heuristic()
 def      A_	(      self  :      Optional[Any]	)			->	float:
     lowerCamelCase__    :      Dict					 =		abs(self.pos_x - self.goal_x	)
     lowerCamelCase__    :      int					 =		abs(self.pos_y - self.goal_y	)
     return dx + dy







 def __lt__(      self  :      int			,     UpperCAmelCase  :      int	)			->	bool:
     return self.f_cost < other.f_cost


class    lowerCAmelCase							:
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      tuple[int, int]			,     UpperCAmelCase  :      tuple[int, int]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Tuple					 =		Node(start[1]			,     start[0]			,     goal[1]			,     goal[0]			,     0			,     UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		Node(goal[1]			,     goal[0]			,     goal[1]			,     goal[0]			,     99999			,     UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		[self.start]
     lowerCamelCase__    :      list[Node]					 =		[]

     lowerCamelCase__    :      Tuple					 =		False
 def      A_	(      self  :      Any	)			->	Path | None:
     while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         lowerCamelCase__    :      Optional[Any]					 =		self.open_nodes.pop(0	)

         if current_node.pos == self.target.pos:
             lowerCamelCase__    :      Optional[int]					 =		True
             return self.retrace_path(UpperCAmelCase	)

         self.closed_nodes.append(UpperCAmelCase	)
         lowerCamelCase__    :      List[Any]					 =		self.get_successors(UpperCAmelCase	)

         for child_node in successors:
             if child_node in self.closed_nodes:
                 continue

             if child_node not in self.open_nodes:
                 self.open_nodes.append(UpperCAmelCase	)
             else:
                 # retrieve the best current path
                 lowerCamelCase__    :      Optional[Any]					 =		self.open_nodes.pop(self.open_nodes.index(UpperCAmelCase	)	)

                 if child_node.g_cost < better_node.g_cost:
                     self.open_nodes.append(UpperCAmelCase	)
                 else:
                     self.open_nodes.append(UpperCAmelCase	)

     if not self.reached:
         return [self.start.pos]
     return None
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Node	)			->	list[Node]:
     lowerCamelCase__    :      List[Any]					 =		[]
     for action in delta:
         lowerCamelCase__    :      Union[str, Any]					 =		parent.pos_x + action[1]
         lowerCamelCase__    :      Any					 =		parent.pos_y + action[0]

         if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(UpperCAmelCase	) - 1):
             continue

         if grid[pos_y][pos_x] != 0:
             continue

         successors.append(
             Node(
                 UpperCAmelCase			,     UpperCAmelCase			,     self.target.pos_y			,     self.target.pos_x			,     parent.g_cost + 1			,     UpperCAmelCase			,     )	)
     return successors







 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Node | None	)			->	Path:
     lowerCamelCase__    :      Optional[Any]					 =		node
     lowerCamelCase__    :      Any					 =		[]
     while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)	)
         lowerCamelCase__    :      Optional[int]					 =		current_node.parent
     path.reverse()
     return path


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				(0, 0)
    _UpperCAmelCase				:       Tuple	   =				(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print("""------""")

    _UpperCAmelCase				:       str	   =				GreedyBestFirst(init, goal)
    _UpperCAmelCase				:       int	   =				greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            _UpperCAmelCase				:       int	   =				2

        for elem in grid:
            print(elem)










import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      KandinskyVaaControlnetPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: Dict					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Dict					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim * 4






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)

        A :       Any          =   {
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A :       List[str]          =   UNetaDConditionModel(**lowerCamelCase__		)
        return model






    @property
    def 			_lowerCAmelCase					(       self		):
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       List[Any]          =   VQModel(**self.dummy_movq_kwargs		)
        return model






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.dummy_unet
        A :       Tuple          =   self.dummy_movq

        A :       List[Any]          =   DDIMScheduler(
            num_train_timesteps=1000,		beta_schedule="""linear""",		beta_start=0.0_0085,		beta_end=0.012,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		steps_offset=1,		prediction_type="""epsilon""",		thresholding=lowerCamelCase__,		)

        A :       int          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       Optional[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A :       List[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            lowerCamelCase__		)

        # create hint
        A :       int          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)

        if str(lowerCamelCase__		).startswith("""mps"""		):
            A :       Optional[Any]          =   torch.manual_seed(lowerCamelCase__		)
        else:
            A :       str          =   torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A :       List[str]          =   {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   """cpu"""

        A :       List[str]          =   self.get_dummy_components()

        A :       Dict          =   self.pipeline_class(**lowerCamelCase__		)
        A :       Optional[Any]          =   pipe.to(lowerCamelCase__		)

        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

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

        A :       str          =   pipe(
            **self.get_dummy_inputs(lowerCamelCase__		),		return_dict=lowerCamelCase__,		)[0]

        A :       Optional[int]          =   image[0, -3:, -3:, -1]
        A :       Any          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A :       Dict          =   np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]		)

        assert (
            np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

        A :       Any          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""		)
        A :       Optional[Any]          =   torch.from_numpy(np.array(lowerCamelCase__		)		).float() / 255.0
        A :       List[str]          =   hint.permute(2,		0,		1		).unsqueeze(0		)

        A :       Union[str, Any]          =   KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior""",		torch_dtype=torch.floataa		)
        pipe_prior.to(lowerCamelCase__		)

        A :       Tuple          =   KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(lowerCamelCase__		)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[Any]          =   """A robot, 4k photo"""

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A     ,							A :       int          =   pipe_prior(
            lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=5,		negative_prompt="""""",		).to_tuple()

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A :       int          =   pipeline(
            image_embeds=lowerCamelCase__,		negative_image_embeds=lowerCamelCase__,		hint=lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=100,		output_type="""np""",		)

        A :       Optional[int]          =   output.images[0]

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

        assert_mean_pixel_difference(lowerCamelCase__,		lowerCamelCase__		)


