
"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


a		:       Union[str, Any]     						=       logging.get_logger(__name__)  # pylint: disable=invalid-name

a		:       Tuple     						=       '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'



def     __magic_name__		(					UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		List[str]=8     )     ->						str:
    a__		=			h // scale_factor**2
    if h % scale_factor**2 != 0:
        new_h += 1
    a__		=			w // scale_factor**2
    if w % scale_factor**2 != 0:
        new_w += 1
    return new_h * scale_factor, new_w * scale_factor







class     lowercase(_lowerCamelCase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				)		->     Dict:







         """simple docstring"""



         super().__init__()

         self.register_modules(
             text_encoder=lowercase_     ,				tokenizer=lowercase_     ,				unet=lowercase_     ,				scheduler=lowercase_     ,				movq=lowercase_     ,				)
         a__		=			2 ** (len(self.movq.config.block_out_channels   ) - 1)



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         if latents is None:
             a__		=			randn_tensor(lowercase_     ,				generator=lowercase_     ,				device=lowercase_     ,				dtype=lowercase_   )
         else:
             if latents.shape != shape:
                 raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
             a__		=			latents.to(lowercase_   )

         a__		=			latents * scheduler.init_noise_sigma
         return latents



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None     ,				)		->     Optional[int]:







         """simple docstring"""



         a__		=			len(lowercase_   ) if isinstance(lowercase_     ,				lowercase_   ) else 1
         # get prompt text embeddings
         a__		=			self.tokenizer(
             lowercase_     ,				padding='max_length'     ,				truncation=lowercase_     ,				max_length=7_7     ,				return_attention_mask=lowercase_     ,				add_special_tokens=lowercase_     ,				return_tensors='pt'     ,				)

         a__		=			text_inputs.input_ids
         a__		=			self.tokenizer(lowercase_     ,				padding='longest'     ,				return_tensors='pt'   ).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase_     ,				lowercase_   ):
             a__		=			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
             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__		=			text_input_ids.to(lowercase_   )
         a__		=			text_inputs.attention_mask.to(lowercase_   )

         a__	,     a__		=			self.text_encoder(
             input_ids=lowercase_     ,				attention_mask=lowercase_   )

         a__		=			prompt_embeds.repeat_interleave(lowercase_     ,				dim=0   )
         a__		=			text_encoder_hidden_states.repeat_interleave(lowercase_     ,				dim=0   )
         a__		=			text_mask.repeat_interleave(lowercase_     ,				dim=0   )

         if do_classifier_free_guidance:
             a__		=			4_2
             if negative_prompt is None:
                 a__		=			[''] * batch_size
             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__		=			[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__		=			negative_prompt

             a__		=			self.tokenizer(
                 lowercase_     ,				padding='max_length'     ,				max_length=7_7     ,				truncation=lowercase_     ,				return_attention_mask=lowercase_     ,				add_special_tokens=lowercase_     ,				return_tensors='pt'     ,				)
             a__		=			uncond_input.input_ids.to(lowercase_   )
             a__		=			uncond_input.attention_mask.to(lowercase_   )

             a__	,     a__		=			self.text_encoder(
                 input_ids=lowercase_     ,				attention_mask=lowercase_   )

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             a__		=			negative_prompt_embeds.shape[1]
             a__		=			negative_prompt_embeds.repeat(1     ,				lowercase_   )
             a__		=			negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,				lowercase_   )

             a__		=			uncond_text_encoder_hidden_states.shape[1]
             a__		=			uncond_text_encoder_hidden_states.repeat(1     ,				lowercase_     ,				1   )
             a__		=			uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt     ,				lowercase_     ,				-1   )
             a__		=			uncond_text_mask.repeat_interleave(lowercase_     ,				dim=0   )

             # done duplicates

             # 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__		=			torch.cat([negative_prompt_embeds, prompt_embeds]   )
             a__		=			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

             a__		=			torch.cat([uncond_text_mask, text_mask]   )

         return prompt_embeds, text_encoder_hidden_states, text_mask



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0   )		->     Dict:







         """simple docstring"""



         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'   )

         a__		=			torch.device(f'cuda:{gpu_id}'   )

         a__		=			[
             self.unet,
             self.text_encoder,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(lowercase_     ,				lowercase_   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0   )		->     int:







         """simple docstring"""



         if is_accelerate_available() and is_accelerate_version('>='     ,				'0.17.0.dev0'   ):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

         a__		=			torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to('cpu'     ,				silence_dtype_warnings=lowercase_   )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         a__		=			None
         for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
             a__	,     a__		=			cpu_offload_with_hook(lowercase_     ,				lowercase_     ,				prev_module_hook=lowercase_   )

         if self.safety_checker is not None:
             a__	,     a__		=			cpu_offload_with_hook(self.safety_checker     ,				lowercase_     ,				prev_module_hook=lowercase_   )

         # We'll offload the last model manually.
         a__		=			hook



     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if not hasattr(self.unet     ,				'_hf_hook'   ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(lowercase_     ,				'_hf_hook'   )
                 and hasattr(module._hf_hook     ,				'execution_device'   )
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device   )
         return self.device




     @torch.no_grad()
     @replace_example_docstring(lowercase_   )
     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = 5_1_2     ,				__SCREAMING_SNAKE_CASE = 5_1_2     ,				__SCREAMING_SNAKE_CASE = 1_0_0     ,				__SCREAMING_SNAKE_CASE = 4.0     ,				__SCREAMING_SNAKE_CASE = 1     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "pil"     ,				__SCREAMING_SNAKE_CASE = True     ,				)		->     Dict:







         """simple docstring"""



         if isinstance(lowercase_     ,				lowercase_   ):
             a__		=			1
         elif isinstance(lowercase_     ,				lowercase_   ):
             a__		=			len(lowercase_   )
         else:
             raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}'   )

         a__		=			self._execution_device

         a__		=			batch_size * num_images_per_prompt
         a__		=			guidance_scale > 1.0

         a__	,     a__	,     a__		=			self._encode_prompt(
             lowercase_     ,				lowercase_     ,				lowercase_     ,				lowercase_     ,				lowercase_   )

         if isinstance(lowercase_     ,				lowercase_   ):
             a__		=			torch.cat(lowercase_     ,				dim=0   )
         if isinstance(lowercase_     ,				lowercase_   ):
             a__		=			torch.cat(lowercase_     ,				dim=0   )

         if do_classifier_free_guidance:
             a__		=			image_embeds.repeat_interleave(lowercase_     ,				dim=0   )
             a__		=			negative_image_embeds.repeat_interleave(lowercase_     ,				dim=0   )

         a__		=			torch.cat([negative_image_embeds, image_embeds]     ,				dim=0   ).to(
             dtype=prompt_embeds.dtype     ,				device=lowercase_   )

         self.scheduler.set_timesteps(lowercase_     ,				device=lowercase_   )
         a__		=			self.scheduler.timesteps

         a__		=			self.unet.config.in_channels

         a__	,     a__		=			get_new_h_w(lowercase_     ,				lowercase_     ,				self.movq_scale_factor   )

         # create initial latent
         a__		=			self.prepare_latents(
             (batch_size, num_channels_latents, height, width)     ,				text_encoder_hidden_states.dtype     ,				lowercase_     ,				lowercase_     ,				lowercase_     ,				self.scheduler     ,				)

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

             a__		=			{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
             a__		=			self.unet(
                 sample=lowercase_     ,				timestep=lowercase_     ,				encoder_hidden_states=lowercase_     ,				added_cond_kwargs=lowercase_     ,				return_dict=lowercase_     ,				)[0]

             if do_classifier_free_guidance:
                 a__	,     a__		=			noise_pred.split(latents.shape[1]     ,				dim=1   )
                 a__	,     a__		=			noise_pred.chunk(2   )
                 a__	,     a__		=			variance_pred.chunk(2   )
                 a__		=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 a__		=			torch.cat([noise_pred, variance_pred_text]     ,				dim=1   )

             if not (
                 hasattr(self.scheduler.config     ,				'variance_type'   )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                 a__	,     a__		=			noise_pred.split(latents.shape[1]     ,				dim=1   )

             # compute the previous noisy sample x_t -> x_t-1
             a__		=			self.scheduler.step(
                 lowercase_     ,				lowercase_     ,				lowercase_     ,				generator=lowercase_     ,				).prev_sample
         # post-processing
         a__		=			self.movq.decode(lowercase_     ,				force_not_quantize=lowercase_   )['sample']

         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

         if output_type in ["np", "pil"]:
             a__		=			image * 0.5 + 0.5
             a__		=			image.clamp(0     ,				1   )
             a__		=			image.cpu().permute(0     ,				2     ,				3     ,				1   ).float().numpy()

         if output_type == "pil":
             a__		=			self.numpy_to_pil(lowercase_   )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=lowercase_   )








'''simple docstring'''

import heapq
import sys

import numpy as np

a    :							Dict										=    tuple[int, int]






class 		a							:


				def __init__(						self       :   Dict   ):
									snake_case_		     =							[]
									snake_case_		     =							set()


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


				def     A_					(						self       :   List[str]   ):
									return len(self.elements   ) == 0


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									if item not in self.set:
														heapq.heappush(self.elements			,							(priority, item)   )
														self.set.add(lowercase_   )
									else:
														# update
														# print("update", item)
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pri, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														temp.append((priority, item)   )
														for pro, xxx in temp:
																			heapq.heappush(self.elements			,							(pro, xxx)   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									if item in self.set:
														self.set.remove(lowercase_   )
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pro, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														for prito, yyy in temp:
																			heapq.heappush(self.elements			,							(prito, yyy)   )


				def     A_					(						self       :   Optional[int]   ):
									return self.elements[0][1]


				def     A_					(						self       :   Tuple   ):
									((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
									self.set.remove(lowercase_   )
									return (priority, item)







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							np.array(__UpperCAmelCase      )
					snake_case_		     =							np.array(__UpperCAmelCase      )
					return np.linalg.norm(a - b      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return consistent_heuristic(__UpperCAmelCase, __UpperCAmelCase      ) // t


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							g_function[start] + Wa * heuristics[i](__UpperCAmelCase, __UpperCAmelCase      )
					return ans


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							np.chararray((n, n)      )
					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															snake_case_		     =							'''*'''

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

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

					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:-'''      )
					snake_case_		     =							back_pointer[goal]
					while x != start:
										print(__UpperCAmelCase, end=''' '''      )
										snake_case_		     =							back_pointer[x]
					print(__UpperCAmelCase      )
					sys.exit()


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   str:

					'''simple docstring'''

					for itera in range(__UpperCAmelCase      ):
										open_list[itera].remove_element(__UpperCAmelCase      )
					# print("s", s)
					# print("j", j)
					((snake_case_)     ,(snake_case_))		     =							s
					snake_case_		     =							(x - 1, y)
					snake_case_		     =							(x + 1, y)
					snake_case_		     =							(x, y + 1)
					snake_case_		     =							(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      )
																				snake_case_		     =							-1
																				snake_case_		     =							float('''inf'''      )

															if valid(__UpperCAmelCase      ) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_		     =							g_function[s] + 1
																				snake_case_		     =							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 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							[]
					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


a    :							Tuple										=    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a    :							Dict										=    [
    (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),
]
a    :							List[str]										=    make_common_ground()


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

# start and end destination
a    :							List[str]										=    (0, 0)
a    :							str										=    (n - 1, n - 1)

a    :							str										=    1


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							{start: 0, goal: float('''inf'''      )}
					snake_case_		     =							{start: -1, goal: -1}
					snake_case_		     =							[]
					snake_case_		     =							set()

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

					snake_case_		     =							[]
					snake_case_		     =							[]
					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:
																									snake_case_     ,snake_case_		     =							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:
																									snake_case_		     =							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)

