


"""simple docstring"""
import heapq
import sys

import numpy as np

UpperCAmelCase				=						tuple[int, int]




class 	lowercase__				:





   def __init__(							self)  ->  Tuple:
      _lowerCamelCase	:					List[Any]   =		[]
      _lowerCamelCase	:					str   =		set()





   def 		UpperCamelCase_  (							self)  ->  Any:
      if not self.empty():
         return self.elements[0][0]
      else:
         return float("""inf""")





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      return len(self.elements) == 0





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      if item not in self.set:
         heapq.heappush(self.elements      ,   (priority, item))
         self.set.add(SCREAMING_SNAKE_CASE)
      else:
         # update
         # print("update", item)
         _lowerCamelCase	:					Tuple   =		[]
         ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					Dict   =		heapq.heappop(self.elements)
         while x != item:
            temp.append((pri, x))
            ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					List[str]   =		heapq.heappop(self.elements)
         temp.append((priority, item))
         for pro, xxx in temp:
            heapq.heappush(self.elements      ,   (pro, xxx))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      if item in self.set:
         self.set.remove(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Dict   =		[]
         ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					List[Any]   =		heapq.heappop(self.elements)
         while x != item:
            temp.append((pro, x))
            ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					Dict   =		heapq.heappop(self.elements)
         for prito, yyy in temp:
            heapq.heappush(self.elements      ,   (prito, yyy))





   def 		UpperCamelCase_  (							self)  ->  Any:
      return self.elements[0][1]





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					int   =		heapq.heappop(self.elements)
      self.set.remove(SCREAMING_SNAKE_CASE)
      return (priority, item)







def 				_snake_case      (   __snake_case							:      TPos						, __snake_case							:      TPos   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		np.array(__snake_case   )
   _lowerCamelCase	:					str   =		np.array(__snake_case   )
   return np.linalg.norm(a - b   )

def 				_snake_case      (   __snake_case							:      TPos						, __snake_case							:      TPos   ):




   """simple docstring"""







   return consistent_heuristic(__snake_case						, __snake_case   ) // t

def 				_snake_case      (   __snake_case							:      TPos						, __snake_case							:      TPos   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TPos						, __snake_case							:      int						, __snake_case							:      TPos						, __snake_case							:      dict[TPos, float]   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      Optional[int]						, __snake_case							:      List[str]						, __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		np.chararray((n, n)   )
   for i in range(__snake_case   ):
      for j in range(__snake_case   ):
         _lowerCamelCase	:					Any   =		"""*"""

   for i in range(__snake_case   ):
      for j in range(__snake_case   ):
         if (j, (n - 1) - i) in blocks:
            _lowerCamelCase	:					Optional[int]   =		"""#"""

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

   for i in range(__snake_case   ):
      for j in range(__snake_case   ):
         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:-"""   )
   _lowerCamelCase	:					Dict   =		back_pointer[goal]
   while x != start:
      print(__snake_case						, end=""" """   )
      _lowerCamelCase	:					Tuple   =		back_pointer[x]
   print(__snake_case   )
   sys.exit()

def 				_snake_case      (   __snake_case							:      TPos   ):




   """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 				_snake_case      (   __snake_case							:      Dict						, __snake_case							:      Any						, __snake_case							:      str						, __snake_case							:      Dict						, __snake_case							:      Optional[int]						, __snake_case							:      Dict						, __snake_case							:      Any						, __snake_case							:      int						, ):




   """simple docstring"""







   for itera in range(__snake_case   ):
      open_list[itera].remove_element(__snake_case   )
   # print("s", s)
   # print("j", j)
   ((_lowerCamelCase)   ,    (_lowerCamelCase))	:					Any   =		s
   _lowerCamelCase	:					Any   =		(x - 1, y)
   _lowerCamelCase	:					Optional[int]   =		(x + 1, y)
   _lowerCamelCase	:					Union[str, Any]   =		(x, y + 1)
   _lowerCamelCase	:					int   =		(x, y - 1)

   for neighbours in [left, right, up, down]:
      if neighbours not in blocks:
         if valid(__snake_case   ) and neighbours not in visited:
            # print("neighbour", neighbours)
            visited.add(__snake_case   )
            _lowerCamelCase	:					Optional[Any]   =		-1
            _lowerCamelCase	:					Tuple   =		float("""inf"""   )

         if valid(__snake_case   ) and g_function[neighbours] > g_function[s] + 1:
            _lowerCamelCase	:					Dict   =		g_function[s] + 1
            _lowerCamelCase	:					List[str]   =		s
            if neighbours not in close_list_anchor:
               open_list[0].put(__snake_case						, key(__snake_case						, 0						, __snake_case						, __snake_case   )   )
               if neighbours not in close_list_inad:
                  for var in range(1						, __snake_case   ):
                     if key(__snake_case						, __snake_case						, __snake_case						, __snake_case   ) <= Wa * key(
                         __snake_case						, 0						, __snake_case						, __snake_case   ):
                        open_list[j].put(
                            __snake_case						, key(__snake_case						, __snake_case						, __snake_case						, __snake_case   )   )

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		[]
   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 				_snake_case      (   __snake_case							:      TPos						, __snake_case							:      TPos						, __snake_case							:      int   ):




   """simple docstring"""







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

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

   _lowerCamelCase	:					list[int]   =		[]
   _lowerCamelCase	:					list[int]   =		[]
   while open_list[0].minkey() < float("""inf"""   ):
      for i in range(1						, __snake_case   ):
         # 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(__snake_case						, __snake_case						, __snake_case   )
            else:
               _lowerCamelCase   ,    _lowerCamelCase	:					int   =		open_list[i].top_show()
               visited.add(__snake_case   )
               expand_state(
                   __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, )
               close_list_inad.append(__snake_case   )
         else:
            if g_function[goal] <= open_list[0].minkey():
               if g_function[goal] < float("""inf"""   ):
                  do_something(__snake_case						, __snake_case						, __snake_case   )
            else:
               _lowerCamelCase	:					List[Any]   =		open_list[0].top_show()
               visited.add(__snake_case   )
               expand_state(
                   __snake_case						, 0						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, )
               close_list_anchor.append(__snake_case   )
   print("""No path found to goal"""   )
   print()
   for i in range(n - 1						, -1						, -1   ):
      for j in range(__snake_case   ):
         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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					StableDiffusionSAGPipeline
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      torch.manual_seed(0)
      _lowerCamelCase	:					Dict   =		UNetaDConditionModel(
          block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,   cross_attention_dim=32      ,   )
      _lowerCamelCase	:					int   =		DDIMScheduler(
          beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   beta_schedule="""scaled_linear"""      ,   clip_sample=SCREAMING_SNAKE_CASE      ,   set_alpha_to_one=SCREAMING_SNAKE_CASE      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		AutoencoderKL(
          block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,   latent_channels=4      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		CLIPTextConfig(
          bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   )
      _lowerCamelCase	:					List[Any]   =		CLIPTextModel(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      _lowerCamelCase	:					List[Any]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  List[Any]:
      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[str]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					List[str]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		{
          """prompt""": """.""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 1.0,
          """sag_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
      _lowerCamelCase	:					Union[str, Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		"""."""
      _lowerCamelCase	:					int   =		torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Dict   =		output.images

      _lowerCamelCase	:					Tuple   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Optional[Any]   =		np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[Any]   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Dict   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		"""."""
      _lowerCamelCase	:					List[str]   =		torch.manual_seed(0)
      _lowerCamelCase	:					int   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Any   =		output.images

      _lowerCamelCase	:					List[str]   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Any   =		np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					int   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Optional[Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		"""."""
      _lowerCamelCase	:					Union[str, Any]   =		torch.manual_seed(0)
      _lowerCamelCase	:					Optional[int]   =		sag_pipe(
          [prompt]      ,   width=768      ,   height=512      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		output.images

      assert image.shape == (1, 512, 768, 3)

