

"""simple docstring"""





from __future__ import annotations

import time

_a       =							list[tuple[int, int]]

_a       =							[
    [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],
]

_a       =							[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Node | None	):
    __lowercase								=						pos_x
    __lowercase								=						pos_y
    __lowercase								=						(pos_y, pos_x)
    __lowercase								=						goal_x
    __lowercase								=						goal_y
    __lowercase								=						parent







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			tuple[int, int],					UpperCAmelCase__							:			tuple[int, int]	):
    __lowercase								=						Node(start[1],					start[0],					goal[1],					goal[0],					UpperCAmelCase__	)
    __lowercase								=						Node(goal[1],					goal[0],					goal[1],					goal[0],					UpperCAmelCase__	)

    __lowercase								=						[self.start]
    __lowercase								=						False


 def 					_lowercase (			self							:			Optional[int]	):
    while self.node_queue:
       __lowercase								=						self.node_queue.pop(0	)

       if current_node.pos == self.target.pos:
          __lowercase								=						True
          return self.retrace_path(UpperCAmelCase__	)

       __lowercase								=						self.get_successors(UpperCAmelCase__	)

       for node in successors:
          self.node_queue.append(UpperCAmelCase__	)

    if not self.reached:
       return [self.start.pos]
    return None


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			Node	):
    __lowercase								=						[]
    for action in delta:
       __lowercase								=						parent.pos_x + action[1]
       __lowercase								=						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,					UpperCAmelCase__	)	)
    return successors



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Node | None	):
    __lowercase								=						node
    __lowercase								=						[]
    while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)	)
       __lowercase								=						current_node.parent
    path.reverse()
    return path







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						BreadthFirstSearch(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						BreadthFirstSearch(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						False


 def 					_lowercase (			self							:			List[Any]	):
    while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
       __lowercase								=						self.fwd_bfs.node_queue.pop(0	)
       __lowercase								=						self.bwd_bfs.node_queue.pop(0	)

       if current_bwd_node.pos == current_fwd_node.pos:
          __lowercase								=						True
          return self.retrace_bidirectional_path(
              UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						current_bwd_node
       __lowercase								=						current_fwd_node

       __lowercase								=						{
           self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__	),
           self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__	),
       }

       for bfs in [self.fwd_bfs, self.bwd_bfs]:
          for node in successors[bfs]:
             bfs.node_queue.append(UpperCAmelCase__	)

    if not self.reached:
       return [self.fwd_bfs.start.pos]
    return None



 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Node,					UpperCAmelCase__							:			Node	):
    __lowercase								=						self.fwd_bfs.retrace_path(UpperCAmelCase__	)
    __lowercase								=						self.bwd_bfs.retrace_path(UpperCAmelCase__	)
    bwd_path.pop()
    bwd_path.reverse()
    __lowercase								=						fwd_path + bwd_path
    return path


if __name__ == "__main__":
   # all coordinates are given in format [y,x]
   import doctest

   doctest.testmod()
   _a       =							(0, 0)
   _a       =							(len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   _a       =							time.time()
   _a       =							BreadthFirstSearch(init, goal)
   _a       =							bfs.search()
   _a       =							time.time() - start_bfs_time

   print('Unidirectional BFS computation time : ', bfs_time)

   _a       =							time.time()
   _a       =							BidirectionalBreadthFirstSearch(init, goal)
   _a       =							bd_bfs.search()
   _a       =							time.time() - start_bd_bfs_time

   print('Bidirectional BFS computation time : ', bd_bfs_time)







"""simple docstring"""





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort




class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "image": image,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 7.5,
        "output_type": "numpy",
    }
    return inputs


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1].flatten()

    # started as 128, should now be 512
    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]	)

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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]	)

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @property
 def 					_lowercase (			self							:			Tuple	):
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    # using the PNDM scheduler by default
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def 					_lowercase (			self							:			str	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"	)
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




