





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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__lowerCamelCase       :     Optional[int]   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       super().__init__()

       if safety_checker is None:
        logger.warning(
            F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
            " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
            " results in services or applications open to the public. Both the diffusers team and Hugging Face"
            " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
            " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
            " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."   )

       self.register_modules(
           speech_model=A_       ,							speech_processor=A_       ,							vae=A_       ,							text_encoder=A_       ,							tokenizer=A_       ,							unet=A_       ,							scheduler=A_       ,							feature_extractor=A_       ,							)





      def        __UpperCamelCase( self       ,							A_ = "auto"   ):
       '''simple docstring'''




       if slice_size == "auto":
        UpperCamelCase	:					int       	=   self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.enable_attention_slicing(A_   )





      @torch.no_grad()
      def __call__( self       ,							A_       ,							A_=1_6000       ,							A_ = 512       ,							A_ = 512       ,							A_ = 50       ,							A_ = 7.5       ,							A_ = None       ,							A_ = 1       ,							A_ = 0.0       ,							A_ = None       ,							A_ = None       ,							A_ = "pil"       ,							A_ = True       ,							A_ = None       ,							A_ = 1       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.speech_processor.feature_extractor(
           A_       ,							return_tensors="pt"       ,							sampling_rate=A_   ).input_features.to(self.device   )
       UpperCamelCase	:					Union[str, Any]       	=   self.speech_model.generate(A_       ,							max_length=48_0000   )

       UpperCamelCase	:					List[str]       	=   self.speech_processor.tokenizer.batch_decode(A_       ,							skip_special_tokens=A_       ,							normalize=A_   )[
           0
       ]

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

       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(A_       ,							A_   ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            F""" {type(A_   )}."""   )

       # get prompt text embeddings
       UpperCamelCase	:					str       	=   self.tokenizer(
           A_       ,							padding="max_length"       ,							max_length=self.tokenizer.model_max_length       ,							return_tensors="pt"       ,							)
       UpperCamelCase	:					Any       	=   text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        UpperCamelCase	:					Optional[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}"""   )
        UpperCamelCase	:					str       	=   text_input_ids[:, : self.tokenizer.model_max_length]
       UpperCamelCase	:					Union[str, Any]       	=   self.text_encoder(text_input_ids.to(self.device   )   )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					int       	=   text_embeddings.shape
       UpperCamelCase	:					Any       	=   text_embeddings.repeat(1       ,							A_       ,							1   )
       UpperCamelCase	:					str       	=   text_embeddings.view(bs_embed * num_images_per_prompt       ,							A_       ,							-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.
       UpperCamelCase	:					Optional[Any]       	=   guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        UpperCamelCase	:					List[str]
        if negative_prompt is None:
         UpperCamelCase	:					Dict       	=   [""] * batch_size
        elif type(A_   ) is not type(A_   ):
         raise TypeError(
             F"""`negative_prompt` should be the same type to `prompt`, but got {type(A_   )} !="""
             F""" {type(A_   )}."""   )
        elif isinstance(A_       ,							A_   ):
         UpperCamelCase	:					Any       	=   [negative_prompt]
        elif batch_size != len(A_   ):
         raise ValueError(
             F"""`negative_prompt`: {negative_prompt} has batch size {len(A_   )}, but `prompt`:"""
             F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             " the batch size of `prompt`."   )
        else:
         UpperCamelCase	:					int       	=   negative_prompt

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

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase	:					Tuple       	=   uncond_embeddings.shape[1]
        UpperCamelCase	:					List[str]       	=   uncond_embeddings.repeat(1       ,							A_       ,							1   )
        UpperCamelCase	:					Dict       	=   uncond_embeddings.view(batch_size * num_images_per_prompt       ,							A_       ,							-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
        UpperCamelCase	:					Tuple       	=   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`.
       UpperCamelCase	:					Optional[Any]       	=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase	:					List[str]       	=   text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         UpperCamelCase	:					Optional[Any]       	=   torch.randn(A_       ,							generator=A_       ,							device="cpu"       ,							dtype=A_   ).to(
             self.device   )
        else:
         UpperCamelCase	:					Dict       	=   torch.randn(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )
       else:
        if latents.shape != latents_shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""   )
        UpperCamelCase	:					str       	=   latents.to(self.device   )

       # set timesteps
       self.scheduler.set_timesteps(A_   )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCamelCase	:					List[str]       	=   self.scheduler.timesteps.to(self.device   )

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

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

        # predict the noise residual
        UpperCamelCase	:					str       	=   self.unet(A_       ,							A_       ,							encoder_hidden_states=A_   ).sample

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

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

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

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

       UpperCamelCase	:					str       	=   (image / 2 + 0.5).clamp(0       ,							1   )

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

       if output_type == "pil":
        UpperCamelCase	:					Optional[Any]       	=   self.numpy_to_pil(A_   )

       if not return_dict:
        return image

       return StableDiffusionPipelineOutput(images=A_       ,							nsfw_content_detected=A_   )







from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A    :					str =      0

A    :					Any =      [
    [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    :					Dict =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A    :					Union[str, Any] =      tuple[int, int]


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Node | None							,	)	->	None:







					"""simple docstring"""






					A__        =   pos_x
					A__        =   pos_y
					A__        =   (pos_y, pos_x)
					A__        =   goal_x
					A__        =   goal_y
					A__        =   g_cost
					A__        =   parent
					A__        =   self.calculate_heuristic()
					A__        =   self.g_cost + self.h_cost






			def    a_       (			self   :    Dict   )	->	float:







					"""simple docstring"""






					A__        =   self.pos_x - self.goal_x
					A__        =   self.pos_y - self.goal_y
					if HEURISTIC == 1:
							return abs(__lowerCAmelCase   ) + abs(__lowerCAmelCase   )
					else:
							return sqrt(dy**2 + dx**2   )







			def __lt__(			self   :    int							,	__lowerCAmelCase   :    Node   )	->	bool:







					"""simple docstring"""






					return self.f_cost < other.f_cost


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Union[str, Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	Tuple:







					"""simple docstring"""






					A__        =   Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	0							,	__lowerCAmelCase   )
					A__        =   Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	9_99_99							,	__lowerCAmelCase   )

					A__        =   [self.start]
					A__        =   []

					A__        =   False






			def    a_       (			self   :    List[str]   )	->	list[TPosition]:







					"""simple docstring"""






					while self.open_nodes:
							# Open Nodes are sorted using __lt__
							self.open_nodes.sort()
							A__        =   self.open_nodes.pop(0   )

							if current_node.pos == self.target.pos:
									return self.retrace_path(__lowerCAmelCase   )

							self.closed_nodes.append(__lowerCAmelCase   )
							A__        =   self.get_successors(__lowerCAmelCase   )

							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(__lowerCAmelCase   )
									else:
											# retrieve the best current path
											A__        =   self.open_nodes.pop(self.open_nodes.index(__lowerCAmelCase   )   )

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

					return [self.start.pos]






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Node   )	->	list[Node]:







					"""simple docstring"""






					A__        =   []
					for action in delta:
							A__        =   parent.pos_x + action[1]
							A__        =   parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__lowerCAmelCase   ) - 1):
									continue

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

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







			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Node | None   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   node
					A__        =   []
					while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)   )
							A__        =   current_node.parent
					path.reverse()
					return path


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	None:







					"""simple docstring"""






					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   False






			def    a_       (			self   :    int   )	->	list[TPosition]:







					"""simple docstring"""






					while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							A__        =   self.fwd_astar.open_nodes.pop(0   )
							A__        =   self.bwd_astar.open_nodes.pop(0   )

							if current_bwd_node.pos == current_fwd_node.pos:
									return self.retrace_bidirectional_path(
									    __lowerCAmelCase							,	__lowerCAmelCase   )

							self.fwd_astar.closed_nodes.append(__lowerCAmelCase   )
							self.bwd_astar.closed_nodes.append(__lowerCAmelCase   )

							A__        =   current_bwd_node
							A__        =   current_fwd_node

							A__        =   {
							    self.fwd_astar: self.fwd_astar.get_successors(__lowerCAmelCase   ),
							    self.bwd_astar: self.bwd_astar.get_successors(__lowerCAmelCase   ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
									for child_node in successors[astar]:
											if child_node in astar.closed_nodes:
													continue

											if child_node not in astar.open_nodes:
													astar.open_nodes.append(__lowerCAmelCase   )
											else:
													# retrieve the best current path
													A__        =   astar.open_nodes.pop(
													    astar.open_nodes.index(__lowerCAmelCase   )   )

													if child_node.g_cost < better_node.g_cost:
															astar.open_nodes.append(__lowerCAmelCase   )
													else:
															astar.open_nodes.append(__lowerCAmelCase   )

					return [self.fwd_astar.start.pos]







			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    Node							,	__lowerCAmelCase   :    Node   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   self.fwd_astar.retrace_path(__lowerCAmelCase   )
					A__        =   self.bwd_astar.retrace_path(__lowerCAmelCase   )
					bwd_path.pop()
					bwd_path.reverse()
					A__        =   fwd_path + bwd_path
					return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					A    :					Optional[int] =      (0, 0)
					A    :					int =      (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					A    :					Dict =      time.time()
					A    :					Optional[Any] =      AStar(init, goal)
					A    :					Optional[int] =      a_star.search()
					A    :					Optional[int] =      time.time() - start_time
					print(F'''AStar execution time = {end_time:f} seconds''')

					A    :					Dict =      time.time()
					A    :					Tuple =      BidirectionalAStar(init, goal)
					A    :					List[Any] =      time.time() - bd_start_time
					print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')



