"""simple docstring"""







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE									=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if isinstance(__UpperCAmelCase     ,torch.Tensor			):
		return image
	elif isinstance(__UpperCAmelCase     ,PIL.Image.Image			):
		_lowercase :    Any							=							[image]

	_lowercase :    Dict							=							[trans(img.convert('RGB'			)			) for img in image]
	_lowercase :    Any							=							torch.stack(__UpperCAmelCase			)
	return image







class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Tuple			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_lowercase :    Union[str, Any]							=							DDIMScheduler.from_config(scheduler.config						)

					self.register_modules(unet=lowerCamelCase_			,					scheduler=lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					if strength < 0 or strength > 1:
						raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''						)







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							min(int(num_inference_steps * strength						)			,					lowerCamelCase_						)

					_lowercase :    Optional[int]							=							max(num_inference_steps - init_timestep			,					0						)
					_lowercase :    Dict							=							self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       int=None						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					(torch.Tensor, PIL.Image.Image, list)						):
						raise ValueError(
						    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase_						)}'''						)

					_lowercase :    Optional[Any]							=							image.to(device=lowerCamelCase_			,					dtype=lowerCamelCase_						)

					if isinstance(lowerCamelCase_			,					lowerCamelCase_						) and len(lowerCamelCase_						) != batch_size:
						raise ValueError(
						    F'''You have passed a list of generators of length {len(lowerCamelCase_						)}, but requested an effective batch'''
						    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

					_lowercase :    List[Any]							=							init_latents.shape
					_lowercase :    int							=							randn_tensor(lowerCamelCase_			,					generator=lowerCamelCase_			,					device=lowerCamelCase_			,					dtype=lowerCamelCase_						)

					# get latents
					print('add noise to latents at timestep'			,					lowerCamelCase_						)
					_lowercase :    List[str]							=							self.scheduler.add_noise(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    str							=							init_latents

					return latents







				@torch.no_grad()
				def __call__( self    :       str			,					lowerCamelCase_    :       Union[torch.FloatTensor, PIL.Image.Image] = None			,					lowerCamelCase_    :       float = 0.8			,					lowerCamelCase_    :       int = 1			,					lowerCamelCase_    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None			,					lowerCamelCase_    :       float = 0.0			,					lowerCamelCase_    :       int = 5_0			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[str] = "pil"			,					lowerCamelCase_    :       bool = True			,					):

					"""simple docstring"""






					self.check_inputs(lowerCamelCase_						)

					# 2. Preprocess image
					_lowercase :    Union[str, Any]							=							preprocess(lowerCamelCase_						)

					# 3. set timesteps
					self.scheduler.set_timesteps(lowerCamelCase_			,					device=self.device						)
					_lowercase				,      _lowercase :    str							=							self.get_timesteps(lowerCamelCase_			,					lowerCamelCase_			,					self.device						)
					_lowercase :    List[Any]							=							timesteps[:1].repeat(lowerCamelCase_						)

					# 4. Prepare latent variables
					_lowercase :    Dict							=							self.prepare_latents(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					self.unet.dtype			,					self.device			,					lowerCamelCase_						)
					_lowercase :    str							=							latents

					# 5. Denoising loop
					for t in self.progress_bar(lowerCamelCase_						):
						# 1. predict noise model_output
						_lowercase :    str							=							self.unet(lowerCamelCase_			,					lowerCamelCase_						).sample

						# 2. predict previous mean of image x_t-1 and add variance depending on eta
						# eta corresponds to η in paper and should be between [0, 1]
						# do x_t -> x_t-1
						_lowercase :    Optional[int]							=							self.scheduler.step(
						    lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					eta=lowerCamelCase_			,					use_clipped_model_output=lowerCamelCase_			,					generator=lowerCamelCase_			,					).prev_sample

					_lowercase :    Optional[Any]							=							(image / 2 + 0.5).clamp(0			,					1						)
					_lowercase :    Optional[Any]							=							image.cpu().permute(0			,					2			,					3			,					1						).numpy()
					if output_type == "pil":
						_lowercase :    Optional[int]							=							self.numpy_to_pil(lowerCamelCase_						)

					if not return_dict:
						return (image, latent_timestep.item())

					return ImagePipelineOutput(images=lowerCamelCase_						)




"""simple docstring"""







from __future__ import annotations

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



