






import numpy as np

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					int	):
		UpperCamelCase     :int          =	(0, 0)
		UpperCamelCase     :List[str]          =	None
		UpperCamelCase     :Union[str, Any]          =	0
		UpperCamelCase     :Optional[Any]          =	0
		UpperCamelCase     :Optional[Any]          =	0





	def __eq__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					int	):
		return self.position == cell.position





	def 						_A (		self				:					Optional[Any]	):
		print(self.position	)

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Any=(5, 5)	):
		UpperCamelCase     :Tuple          =	np.zeros(__lowerCamelCase	)
		UpperCamelCase     :str          =	world_size[0]
		UpperCamelCase     :List[str]          =	world_size[1]





	def 						_A (		self				:					List[Any]	):
		print(self.w	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					str	):
		UpperCamelCase     :str          =	[
		    (-1, -1),
		    (-1, 0),
		    (-1, 1),
		    (0, -1),
		    (0, 1),
		    (1, -1),
		    (1, 0),
		    (1, 1),
		]
		UpperCamelCase     :Optional[int]          =	cell.position[0]
		UpperCamelCase     :Tuple          =	cell.position[1]
		UpperCamelCase     :Optional[Any]          =	[]
		for n in neughbour_cord:
			UpperCamelCase     :int          =	current_x + n[0]
			UpperCamelCase     :Optional[Any]          =	current_y + n[1]
			if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
				UpperCamelCase     :List[str]          =	Cell()
				UpperCamelCase     :str          =	(x, y)
				UpperCamelCase     :int          =	cell
				neighbours.append(__lowerCamelCase	)
		return neighbours

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  Optional[int]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	[]
	UpperCamelCase     :List[str]          =	[]
	_open.append(__magic_name__					)

	while _open:
		UpperCamelCase     :Optional[Any]          =	np.argmin([n.f for n in _open]					)
		UpperCamelCase     :Optional[Any]          =	_open[min_f]
		_closed.append(_open.pop(__magic_name__					)					)
		if current == goal:
			break
		for n in world.get_neigbours(__magic_name__					):
			for c in _closed:
				if c == n:
					continue
			UpperCamelCase     :List[Any]          =	current.g + 1
			UpperCamelCase     ,      UpperCamelCase     :Dict          =	n.position
			UpperCamelCase     ,      UpperCamelCase     :List[str]          =	goal.position
			UpperCamelCase     :Any          =	(ya - ya) ** 2 + (xa - xa) ** 2
			UpperCamelCase     :Optional[int]          =	n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(__magic_name__					)
	UpperCamelCase     :int          =	[]
	while current.parent is not None:
		path.append(current.position					)
		UpperCamelCase     :Optional[int]          =	current.parent
	path.append(current.position					)
	return path[::-1]


if __name__ == "__main__":
	UpperCAmelCase_ :						List[Any]      =      Gridworld()
	# Start position and goal
	UpperCAmelCase_ :						List[str]      =      Cell()
	UpperCAmelCase_ :						str      =      (0, 0)
	UpperCAmelCase_ :						Tuple      =      Cell()
	UpperCAmelCase_ :						int      =      (4, 4)
	print(F'''path from {start.position} to {goal.position}''')
	UpperCAmelCase_ :						Tuple      =      astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		UpperCAmelCase_ :						Union[str, Any]      =      1
	print(world.w)





'''simple docstring'''




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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCamelCase							:Any				    =					logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				super().__init__()
				self.register_modules(
				    vae=lowercase       ,      text_encoder=lowercase       ,      tokenizer=lowercase       ,      unet=lowercase       ,      scheduler=lowercase       ,      safety_checker=lowercase       ,      feature_extractor=lowercase       ,      )


	def     _a	(self       ,      lowercase = "auto"							):
				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							A_	:						int	=     self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase							)


	def     _a	(self							):
				self.enable_attention_slicing(lowercase							)


	@torch.no_grad()
	def __call__(self       ,      lowercase       ,      lowercase = 512       ,      lowercase = 512       ,      lowercase = 50       ,      lowercase = 7.5       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = 0.0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = "pil"       ,      lowercase = True       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = None       ,      **lowercase       ,      ):

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     1
				elif isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(lowercase       ,      lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

				# get prompt text embeddings
				A_	:						Optional[Any]	=     self.tokenizer(
				    lowercase       ,      padding="""max_length"""       ,      max_length=self.tokenizer.model_max_length       ,      return_tensors="""pt"""       ,      )
				A_	:						Dict	=     text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_	:						Union[str, Any]	=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
							A_	:						Dict	=     text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
							A_	:						Any	=     self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				A_,    A_,    A_	:						Tuple	=     text_embeddings.shape
				A_	:						Optional[Any]	=     text_embeddings.repeat(1       ,      lowercase       ,      1							)
				A_	:						Any	=     text_embeddings.view(bs_embed * num_images_per_prompt       ,      lowercase       ,      -1							)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				A_	:						List[str]	=     guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							A_	:						List[str]
							if negative_prompt is None:
										A_	:						Optional[int]	=     [""""""]
							elif type(lowercase							) is not type(lowercase							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase							)} !='
										    F' {type(lowercase							)}.'							)
							elif isinstance(lowercase       ,      lowercase							):
										A_	:						Dict	=     [negative_prompt]
							elif batch_size != len(lowercase							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
							else:
										A_	:						Dict	=     negative_prompt

							A_	:						int	=     text_input_ids.shape[-1]
							A_	:						List[Any]	=     self.tokenizer(
							    lowercase       ,      padding="""max_length"""       ,      max_length=lowercase       ,      truncation=lowercase       ,      return_tensors="""pt"""       ,      )
							A_	:						str	=     self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_	:						Optional[Any]	=     uncond_embeddings.shape[1]
							A_	:						str	=     uncond_embeddings.repeat(lowercase       ,      lowercase       ,      1							)
							A_	:						List[str]	=     uncond_embeddings.view(batch_size * num_images_per_prompt       ,      lowercase       ,      -1							)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A_	:						Dict	=     torch.cat([uncond_embeddings, text_embeddings]							)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				A_	:						int	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				A_	:						Dict	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				A_	:						Dict	=     text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not exist on mps
										A_	:						Tuple	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(self.device							)
										A_	:						int	=     torch.randn(lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(
										    self.device							)
							else:
										A_	:						int	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
										A_	:						str	=     torch.randn(lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
				else:
							if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							A_	:						str	=     latents_reference.to(self.device							)
							A_	:						Tuple	=     latents.to(self.device							)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				A_	:						Optional[int]	=     (latents_shape[3] - latents_shape_reference[3]) // 2
				A_	:						Optional[int]	=     (latents_shape[2] - latents_shape_reference[2]) // 2
				A_	:						Optional[int]	=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				A_	:						int	=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				A_	:						Optional[Any]	=     0 if dx < 0 else dx
				A_	:						Optional[Any]	=     0 if dy < 0 else dy
				A_	:						Optional[int]	=     max(-dx       ,      0							)
				A_	:						List[str]	=     max(-dy       ,      0							)
				# import pdb
				# pdb.set_trace()
				A_	:						str	=     latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(lowercase							)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				A_	:						Any	=     self.scheduler.timesteps.to(self.device							)

				# scale the initial noise by the standard deviation required by the scheduler
				A_	:						Tuple	=     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_	:						Dict	=     """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
				A_	:						Any	=     {}
				if accepts_eta:
							A_	:						Optional[int]	=     eta

				for i, t in enumerate(self.progress_bar(lowercase							)							):
							# expand the latents if we are doing classifier free guidance
							A_	:						Tuple	=     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
							A_	:						Tuple	=     self.scheduler.scale_model_input(lowercase       ,      lowercase							)

							# predict the noise residual
							A_	:						List[str]	=     self.unet(lowercase       ,      lowercase       ,      encoder_hidden_states=lowercase							).sample

							# perform guidance
							if do_classifier_free_guidance:
										A_,    A_	:						str	=     noise_pred.chunk(2							)
										A_	:						Optional[Any]	=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A_	:						List[str]	=     self.scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
										callback(lowercase       ,      lowercase       ,      lowercase							)

				A_	:						List[str]	=     1 / 0.1_82_15 * latents
				A_	:						List[str]	=     self.vae.decode(lowercase							).sample

				A_	:						Optional[int]	=     (image / 2 + 0.5).clamp(0       ,      1							)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				A_	:						Optional[int]	=     image.cpu().permute(0       ,      2       ,      3       ,      1							).float().numpy()

				if self.safety_checker is not None:
							A_	:						Union[str, Any]	=     self.feature_extractor(self.numpy_to_pil(lowercase							)       ,      return_tensors="""pt"""							).to(
							    self.device							)
							A_,    A_	:						Optional[int]	=     self.safety_checker(
							    images=lowercase       ,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
				else:
							A_	:						Tuple	=     None

				if output_type == "pil":
							A_	:						Tuple	=     self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowercase       ,      nsfw_content_detected=lowercase							)