






'''simple docstring'''
import heapq
import sys

import numpy as np

UpperCamelCase__ =			tuple[int, int]


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     []
       lowercase_	:    List[str]           =     set()




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       return len(self.elements			) == 0




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       List[Any]  ,     a  :       Tuple			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]			):






       '''simple docstring'''






       if item in self.set:
          self.set.remove(a			)
          lowercase_	:    Optional[Any]           =     []
          (lowercase_)	:    List[Any]           =     heapq.heappop(self.elements			)
          while x != item:
             temp.append((pro, x)			)
             (lowercase_)	:    Dict           =     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  :       Optional[int]			):






       '''simple docstring'''






       (lowercase_)	:    Optional[Any]           =     heapq.heappop(self.elements			)
       self.set.remove(a			)
       return (priority, item)




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     np.array(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     np.array(_UpperCamelCase							)
   return np.linalg.norm(a - b							)




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return consistent_heuristic(_UpperCamelCase						,		_UpperCamelCase							) // t




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     g_function[start] + Wa * heuristics[i](_UpperCamelCase						,		_UpperCamelCase							)
   return ans




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     np.chararray((n, n)							)
   for i in range(_UpperCamelCase							):
      for j in range(_UpperCamelCase							):
         lowercase_	:    List[str]           =     "*"

   for i in range(_UpperCamelCase							):
      for j in range(_UpperCamelCase							):
         if (j, (n - 1) - i) in blocks:
            lowercase_	:    int           =     "#"

   lowercase_	:    Optional[int]           =     "-"
   lowercase_	:    List[Any]           =     back_pointer[goal]
   while x != start:
      (lowercase_)	:    Any           =     x
      # print(x)
      lowercase_	:    Dict           =     "-"
      lowercase_	:    str           =     back_pointer[x]
   lowercase_	:    Any           =     "-"

   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:-"							)
   lowercase_	:    Dict           =     back_pointer[goal]
   while x != start:
      print(_UpperCamelCase						,		end=" "							)
      lowercase_	:    Dict           =     back_pointer[x]
   print(_UpperCamelCase							)
   sys.exit()




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase							):


   """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 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		):


   """simple docstring"""




   for itera in range(_UpperCamelCase							):
      open_list[itera].remove_element(_UpperCamelCase							)
   # print("s", s)
   # print("j", j)
   (lowercase_)	:    Dict           =     s
   lowercase_	:    Union[str, Any]           =     (x - 1, y)
   lowercase_	:    Tuple           =     (x + 1, y)
   lowercase_	:    str           =     (x, y + 1)
   lowercase_	:    Dict           =     (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							)
            lowercase_	:    Union[str, Any]           =     -1
            lowercase_	:    List[str]           =     float("inf"							)

         if valid(_UpperCamelCase							) and g_function[neighbours] > g_function[s] + 1:
            lowercase_	:    Dict           =     g_function[s] + 1
            lowercase_	:    Optional[Any]           =     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 		__SCREAMING_SNAKE_CASE					(    ):


   """simple docstring"""




   lowercase_	:    Any           =     []
   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


UpperCamelCase__ =			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

UpperCamelCase__ =			[
    (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),
]
UpperCamelCase__ =			make_common_ground()


UpperCamelCase__ =			blocks_blk
# hyper parameters
UpperCamelCase__ =			1
UpperCamelCase__ =			1
UpperCamelCase__ =			20
UpperCamelCase__ =			3  # one consistent and two other inconsistent

# start and end destination
UpperCamelCase__ =			(0, 0)
UpperCamelCase__ =			(n - 1, n - 1)

UpperCamelCase__ =			1




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     {start: 0, goal: float("inf"							)}
   lowercase_	:    Any           =     {start: -1, goal: -1}
   lowercase_	:    List[Any]           =     []
   lowercase_	:    Dict           =     set()

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

   lowercase_	:    list[int]           =     []
   lowercase_	:    list[int]           =     []
   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:
               lowercase_	:    int           =     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:
               lowercase_	:    List[str]           =     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)












'''simple docstring'''
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




    @property
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return 1_0_0




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     CLIPTextConfig(
           bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
       return CLIPTextModelWithProjection(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size  ,     image_size=2_2_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1_4  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






       if str(a			).startswith("mps"			):
          lowercase_	:    int           =     torch.manual_seed(a			)
       else:
          lowercase_	:    Optional[Any]           =     torch.Generator(device=a			).manual_seed(a			)
       lowercase_	:    Any           =     {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

       self._test_inference_batch_single_identical(
           test_max_difference=a  ,     relax_max_difference=a  ,     test_mean_pixel_difference=a  ,     )





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

       self._test_attention_slicing_forward_pass(
           test_max_difference=a  ,     test_mean_pixel_difference=a  ,     )




