



import heapq
import sys

import numpy as np

__snake_case		:List[Any]   	=  tuple[int, int]

class 					_A :



       def __init__(	self							:  str):


           '''simple docstring'''

           __a		 =       []
           __a		 =       set()



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           if not self.empty():
               return self.elements[0][0]
           else:
               return float('''inf''')



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return len(self.elements) == 0



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           if item not in self.set:
               heapq.heappush(self.elements  ,	(priority, item))
               self.set.add(__SCREAMING_SNAKE_CASE)
           else:
               # update
               # print("update", item)
               __a		 =       []
               ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
               while x != item:
                   temp.append((pri, x))
                   ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
               temp.append((priority, item))
               for pro, xxx in temp:
                   heapq.heappush(self.elements  ,	(pro, xxx))



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           if item in self.set:
               self.set.remove(__SCREAMING_SNAKE_CASE)
               __a		 =       []
               ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
               while x != item:
                   temp.append((pro, x))
                   ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
               for prito, yyy in temp:
                   heapq.heappush(self.elements  ,	(prito, yyy))



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return self.elements[0][1]





       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
           self.set.remove(__SCREAMING_SNAKE_CASE)
           return (priority, item)





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    # euclidean distance
    __a		 =       np.array(_UpperCAmelCase       )
    __a		 =       np.array(_UpperCAmelCase       )
    return np.linalg.norm(a - b       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    # integer division by time variable
    return consistent_heuristic(_UpperCAmelCase     ,				_UpperCAmelCase       ) // t





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    # manhattan distance
    return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       g_function[start] + Wa * heuristics[i](_UpperCAmelCase     ,				_UpperCAmelCase       )
    return ans





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       np.chararray((n, n)       )
    for i in range(_UpperCAmelCase       ):
        for j in range(_UpperCAmelCase       ):
            __a		 =       '''*'''

    for i in range(_UpperCAmelCase       ):
        for j in range(_UpperCAmelCase       ):
            if (j, (n - 1) - i) in blocks:
                __a		 =       '''#'''

    __a		 =       '''-'''
    __a		 =       back_pointer[goal]
    while x != start:
        ((__a) ,     (__a))		 =       x
        # print(x)
        __a		 =       '''-'''
        __a		 =       back_pointer[x]
    __a		 =       '''-'''

    for i in range(_UpperCAmelCase       ):
        for j in range(_UpperCAmelCase       ):
            if (i, j) == (0, n - 1):
                print(grid[i][j]     ,				end=''' '''       )
                print('''<-- End position'''     ,				end=''' '''       )
            else:
                print(grid[i][j]     ,				end=''' '''       )
        print()
    print('''^'''       )
    print('''Start position'''       )
    print()
    print('''# is an obstacle'''       )
    print('''- is the path taken by algorithm'''       )
    print('''PATH TAKEN BY THE ALGORITHM IS:-'''       )
    __a		 =       back_pointer[goal]
    while x != start:
        print(_UpperCAmelCase     ,				end=''' '''       )
        __a		 =       back_pointer[x]
    print(_UpperCAmelCase       )
    sys.exit()





def   __snake_case			(    _UpperCAmelCase       ):
    if p[0] < 0 or p[0] > n - 1:
        return False
    if p[1] < 0 or p[1] > n - 1:
        return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				):
    for itera in range(_UpperCAmelCase       ):
        open_list[itera].remove_element(_UpperCAmelCase       )
    # print("s", s)
    # print("j", j)
    ((__a) ,     (__a))		 =       s
    __a		 =       (x - 1, y)
    __a		 =       (x + 1, y)
    __a		 =       (x, y + 1)
    __a		 =       (x, y - 1)

    for neighbours in [left, right, up, down]:
        if neighbours not in blocks:
            if valid(_UpperCAmelCase       ) and neighbours not in visited:
                # print("neighbour", neighbours)
                visited.add(_UpperCAmelCase       )
                __a		 =       -1
                __a		 =       float('''inf'''       )

            if valid(_UpperCAmelCase       ) and g_function[neighbours] > g_function[s] + 1:
                __a		 =       g_function[s] + 1
                __a		 =       s
                if neighbours not in close_list_anchor:
                    open_list[0].put(_UpperCAmelCase     ,				key(_UpperCAmelCase     ,				0     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )       )
                    if neighbours not in close_list_inad:
                        for var in range(1     ,				_UpperCAmelCase       ):
                            if key(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ) <= Wa * key(
                                _UpperCAmelCase     ,				0     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
                                open_list[j].put(
                                    _UpperCAmelCase     ,				key(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )       )





def   __snake_case			(    ):
    __a		 =       []
    for x in range(1     ,				5       ):
        for y in range(1     ,				6       ):
            some_list.append((x, y)       )

    for x in range(15     ,				20       ):
        some_list.append((x, 17)       )

    for x in range(10     ,				19       ):
        for y in range(1     ,				15       ):
            some_list.append((x, y)       )

    # L block
    for x in range(1     ,				4       ):
        for y in range(12     ,				19       ):
            some_list.append((x, y)       )
    for x in range(3     ,				13       ):
        for y in range(16     ,				19       ):
            some_list.append((x, y)       )
    return some_list


__snake_case		:List[Any]   	=  {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__snake_case		:str   	=  [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
__snake_case		:Union[str, Any]   	=  make_common_ground()


__snake_case		:Any   	=  blocks_blk
# hyper parameters
__snake_case		:Union[str, Any]   	=  1
__snake_case		:List[Any]   	=  1
__snake_case		:List[Any]   	=  20
__snake_case		:List[Any]   	=  3  # one consistent and two other inconsistent

# start and end destination
__snake_case		:Union[str, Any]   	=  (0, 0)
__snake_case		:Tuple   	=  (n - 1, n - 1)

__snake_case		:Optional[int]   	=  1





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {start: 0, goal: float('''inf'''       )}
    __a		 =       {start: -1, goal: -1}
    __a		 =       []
    __a		 =       set()

    for i in range(_UpperCAmelCase       ):
        open_list.append(PriorityQueue()       )
        open_list[i].put(_UpperCAmelCase     ,				key(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )       )

    __a		 =       []
    __a		 =       []
    while open_list[0].minkey() < float('''inf'''       ):
        for i in range(1     ,				_UpperCAmelCase       ):
            # print(open_list[0].minkey(), open_list[i].minkey())
            if open_list[i].minkey() <= Wa * open_list[0].minkey():
                global t
                t += 1
                if g_function[goal] <= open_list[i].minkey():
                    if g_function[goal] < float('''inf'''       ):
                        do_something(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
                else:
                    __a ,     __a		 =       open_list[i].top_show()
                    visited.add(_UpperCAmelCase       )
                    expand_state(
                        _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				)
                    close_list_inad.append(_UpperCAmelCase       )
            else:
                if g_function[goal] <= open_list[0].minkey():
                    if g_function[goal] < float('''inf'''       ):
                        do_something(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
                else:
                    __a		 =       open_list[0].top_show()
                    visited.add(_UpperCAmelCase       )
                    expand_state(
                        _UpperCAmelCase     ,				0     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				)
                    close_list_anchor.append(_UpperCAmelCase       )
    print('''No path found to goal'''       )
    print()
    for i in range(n - 1     ,				-1     ,				-1       ):
        for j in range(_UpperCAmelCase       ):
            if (j, i) in blocks:
                print('''#'''     ,				end=''' '''       )
            elif (j, i) in back_pointer:
                if (j, i) == (n - 1, n - 1):
                    print('''*'''     ,				end=''' '''       )
                else:
                    print('''-'''     ,				end=''' '''       )
            else:
                print('''*'''     ,				end=''' '''       )
            if (j, i) == (n - 1, n - 1):
                print('''<-- End position'''     ,				end=''' '''       )
        print()
    print('''^'''       )
    print('''Start position'''       )
    print()
    print('''# is an obstacle'''       )
    print('''- is the path taken by algorithm'''       )


if __name__ == "__main__":
 multi_a_star(start, goal, n_heuristic)










import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def   __snake_case			(    _UpperCAmelCase       ):
    __a ,     __a		 =       image.size
    __a ,     __a		 =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __a		 =       image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']       )
    __a		 =       np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0
    __a		 =       image[None].transpose(0     ,				3     ,				1     ,				2       )
    __a		 =       torch.from_numpy(_UpperCAmelCase       )
    return 2.0 * image - 1.0

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
               DDIMScheduler,
               PNDMScheduler,
               LMSDiscreteScheduler,
               EulerDiscreteScheduler,
               EulerAncestralDiscreteScheduler,
               DPMSolverMultistepScheduler,
           ]  ,	):


           '''simple docstring'''

           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE)





       @torch.no_grad()
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.Tensor, PIL.Image.Image] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 100  ,	__SCREAMING_SNAKE_CASE							:  Optional[float] = 0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
               __a		 =       image.shape[0]
           else:
               raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}')

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       preprocess(__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           __a		 =       (batch_size, self.unet.config.in_channels // 2, height, width)
           __a		 =       next(self.unet.parameters()).dtype

           __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           __a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=self.device)
           __a		 =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           __a		 =       latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           __a		 =       '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
           __a		 =       {}
           if accepts_eta:
               __a		 =       eta

           for t in self.progress_bar(__SCREAMING_SNAKE_CASE):
               # concat latents and low resolution image in the channel dimension.
               __a		 =       torch.cat([latents, image]  ,	dim=1)
               __a		 =       self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               # predict the noise residual
               __a		 =       self.unet(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).sample
               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE).prev_sample

           # decode the image latents with the VQVAE
           __a		 =       self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
           __a		 =       torch.clamp(__SCREAMING_SNAKE_CASE  ,	-1.0  ,	1.0)
           __a		 =       image / 2 + 0.5
           __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





