





import heapq
import sys

import numpy as np

__lowercase  :   str						      =tuple[int, int]

class      A       :



      def __init__(	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											=set()



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						str       )						->    Dict:
         '''simple docstring'''

         return len(self.elements       ) == 0



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Dict       )						->    Dict:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         if item in self.set:
            self.set.remove(lowercase__       )
            UpperCAmelCase_											=[]
            (UpperCAmelCase_)											=heapq.heappop(self.elements       )
            while x != item:
               temp.append((pro, x)       )
               (UpperCAmelCase_)											=heapq.heappop(self.elements       )
            for prito, yyy in temp:
               heapq.heappush(self.elements		,  (prito, yyy)       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         return self.elements[0][1]






      def 	lowerCAmelCase__    (	self:						Any       )						->    List[Any]:
         '''simple docstring'''

         (UpperCAmelCase_)											=heapq.heappop(self.elements       )
         self.set.remove(lowercase__       )
         return (priority, item)




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=np.array(SCREAMING_SNAKE_CASE__  )
   UpperCAmelCase_											=np.array(SCREAMING_SNAKE_CASE__  )
   return np.linalg.norm(a - b  )




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return consistent_heuristic(SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ) // t




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=g_function[start] + Wa * heuristics[i](SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  )
   return ans




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=np.chararray((n, n)  )
   for i in range(SCREAMING_SNAKE_CASE__  ):
      for j in range(SCREAMING_SNAKE_CASE__  ):
         UpperCAmelCase_											="""*"""

   for i in range(SCREAMING_SNAKE_CASE__  ):
      for j in range(SCREAMING_SNAKE_CASE__  ):
         if (j, (n - 1) - i) in blocks:
            UpperCAmelCase_											="""#"""

   UpperCAmelCase_											="""-"""
   UpperCAmelCase_											=back_pointer[goal]
   while x != start:
      (UpperCAmelCase_)											=x
      # print(x)
      UpperCAmelCase_											="""-"""
      UpperCAmelCase_											=back_pointer[x]
   UpperCAmelCase_											="""-"""

   for i in range(SCREAMING_SNAKE_CASE__  ):
      for j in range(SCREAMING_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:-"  )
   UpperCAmelCase_											=back_pointer[goal]
   while x != start:
      print(SCREAMING_SNAKE_CASE__  ,    end=" "  )
      UpperCAmelCase_											=back_pointer[x]
   print(SCREAMING_SNAKE_CASE__  )
   sys.exit()




def     a__ (					lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    ):






   '''simple docstring'''






   for itera in range(SCREAMING_SNAKE_CASE__  ):
      open_list[itera].remove_element(SCREAMING_SNAKE_CASE__  )
   # print("s", s)
   # print("j", j)
   (UpperCAmelCase_)											=s
   UpperCAmelCase_											=(x - 1, y)
   UpperCAmelCase_											=(x + 1, y)
   UpperCAmelCase_											=(x, y + 1)
   UpperCAmelCase_											=(x, y - 1)

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

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




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for x in range(1  ,    5  ):
      for y in range(1  ,    6  ):
         some_list.append((x, y)  )

   for x in range(1_5  ,    2_0  ):
      some_list.append((x, 1_7)  )

   for x in range(1_0  ,    1_9  ):
      for y in range(1  ,    1_5  ):
         some_list.append((x, y)  )

    # L block
   for x in range(1  ,    4  ):
      for y in range(1_2  ,    1_9  ):
         some_list.append((x, y)  )
   for x in range(3  ,    1_3  ):
      for y in range(1_6  ,    1_9  ):
         some_list.append((x, y)  )
   return some_list


__lowercase  :   Dict						      ={0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__lowercase  :   Optional[int]						      =[
    (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),
]
__lowercase  :   Union[str, Any]						      =make_common_ground()


__lowercase  :   int						      =blocks_blk
# hyper parameters
__lowercase  :   Optional[int]						      =1
__lowercase  :   Dict						      =1
__lowercase  :   Tuple						      =20
__lowercase  :   List[str]						      =3  # one consistent and two other inconsistent

# start and end destination
__lowercase  :   Tuple						      =(0, 0)
__lowercase  :   Optional[Any]						      =(n - 1, n - 1)

__lowercase  :   Optional[int]						      =1




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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

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

   UpperCAmelCase_											=[]
   UpperCAmelCase_											=[]
   while open_list[0].minkey() < float("inf"  ):
      for i in range(1  ,    SCREAMING_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(SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  )
            else:
               UpperCAmelCase_											=open_list[i].top_show()
               visited.add(SCREAMING_SNAKE_CASE__  )
               expand_state(
                   SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    )
               close_list_inad.append(SCREAMING_SNAKE_CASE__  )
         else:
            if g_function[goal] <= open_list[0].minkey():
               if g_function[goal] < float("inf"  ):
                  do_something(SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  )
            else:
               UpperCAmelCase_											=open_list[0].top_show()
               visited.add(SCREAMING_SNAKE_CASE__  )
               expand_state(
                   SCREAMING_SNAKE_CASE__  ,    0  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    )
               close_list_anchor.append(SCREAMING_SNAKE_CASE__  )
   print("No path found to goal"  )
   print()
   for i in range(n - 1  ,    -1  ,    -1  ):
      for j in range(SCREAMING_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)











import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =DanceDiffusionPipeline
      _snake_case							     =UNCONDITIONAL_AUDIO_GENERATION_PARAMS
      _snake_case							     =PipelineTesterMixin.required_optional_params - {
          '''callback''',
          '''latents''',
          '''callback_steps''',
          '''output_type''',
          '''num_images_per_prompt''',
      }
      _snake_case							     =UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
      _snake_case							     =False
      _snake_case							     =False



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Tuple:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=UNetaDModel(
             block_out_channels=(32, 32, 64)		,  extra_in_channels=16		,  sample_size=512		,  sample_rate=1_6000		,  in_channels=2		,  out_channels=2		,  flip_sin_to_cos=_lowerCAmelCase		,  use_timestep_embedding=_lowerCAmelCase		,  time_embedding_type="fourier"		,  mid_block_type="UNetMidBlock1D"		,  down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,  up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,  )
         UpperCAmelCase_											=IPNDMScheduler()

         UpperCAmelCase_											={
             "unet": unet,
             "scheduler": scheduler,
         }
         return components



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]=0       )						->    str:
         '''simple docstring'''

         if str(_lowerCAmelCase       ).startswith("mps"       ):
            UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         else:
            UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											={
             "batch_size": 1,
             "generator": generator,
             "num_inference_steps": 4,
         }
         return inputs



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_											=self.get_dummy_components()
         UpperCAmelCase_											=DanceDiffusionPipeline(**_lowerCAmelCase       )
         UpperCAmelCase_											=pipe.to(_lowerCAmelCase       )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=self.get_dummy_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=pipe(**_lowerCAmelCase       )
         UpperCAmelCase_											=output.audios

         UpperCAmelCase_											=audio[0, -3:, -3:]

         assert audio.shape == (1, 2, components["unet"].sample_size)
         UpperCAmelCase_											=np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]       )
         assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2



      @skip_mps
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Any:
         '''simple docstring'''

         return super().test_save_load_local()



      @skip_mps
      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )



      @skip_mps
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[int]:
         '''simple docstring'''

         return super().test_save_load_optional_components()



      @skip_mps
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         return super().test_attention_slicing_forward_pass()






      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

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



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=torch_device

         UpperCAmelCase_											=DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       )
         UpperCAmelCase_											=pipe.to(_lowerCAmelCase       )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=torch.manual_seed(0       )
         UpperCAmelCase_											=pipe(generator=_lowerCAmelCase		,  num_inference_steps=100		,  audio_length_in_s=4.0_96       )
         UpperCAmelCase_											=output.audios

         UpperCAmelCase_											=audio[0, -3:, -3:]

         assert audio.shape == (1, 2, pipe.unet.sample_size)
         UpperCAmelCase_											=np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]       )

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






      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=torch_device

         UpperCAmelCase_											=DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"		,  torch_dtype=torch.floataa       )
         UpperCAmelCase_											=pipe.to(_lowerCAmelCase       )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=torch.manual_seed(0       )
         UpperCAmelCase_											=pipe(generator=_lowerCAmelCase		,  num_inference_steps=100		,  audio_length_in_s=4.0_96       )
         UpperCAmelCase_											=output.audios

         UpperCAmelCase_											=audio[0, -3:, -3:]

         assert audio.shape == (1, 2, pipe.unet.sample_size)
         UpperCAmelCase_											=np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]       )

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




