

import gc
import unittest

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

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

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


enable_full_determinism()




class      UpperCamelCase (     __snake_case	,			unittest.TestCase   ):
    __UpperCamelCase         =LDMTextToImagePipeline
    __UpperCamelCase         =TEXT_TO_IMAGE_PARAMS - {
        "negative_prompt",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
        "prompt_embeds",
    }
    __UpperCamelCase         =PipelineTesterMixin.required_optional_params - {
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    __UpperCamelCase         =TEXT_TO_IMAGE_BATCH_PARAMS
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)							,    layers_per_block=2							,    sample_size=3_2							,    in_channels=4							,    out_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,    cross_attention_dim=3_2							,    )
     SCREAMING_SNAKE_CASE								=			DDIMScheduler(
         beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=_lowercase							,    set_alpha_to_one=_lowercase							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			AutoencoderKL(
         block_out_channels=(3_2, 6_4)							,    in_channels=3							,    out_channels=3							,    down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')							,    up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')							,    latent_channels=4							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			CLIPTextConfig(
         bos_token_id=0							,    eos_token_id=2							,    hidden_size=3_2							,    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							,    )
     SCREAMING_SNAKE_CASE								=			CLIPTextModel(_lowercase    )
     SCREAMING_SNAKE_CASE								=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

     SCREAMING_SNAKE_CASE								=			{
         """unet""": unet,
         """scheduler""": scheduler,
         """vqvae""": vae,
         """bert""": text_encoder,
         """tokenizer""": tokenizer,
     }
     return components

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      List[str]							,    snake_case__ :      List[str]=0    ):

     """simple docstring"""






     if str(_lowercase    ).startswith('mps'    ):
      SCREAMING_SNAKE_CASE								=			torch.manual_seed(_lowercase    )
     else:
      SCREAMING_SNAKE_CASE								=			torch.Generator(device=_lowercase    ).manual_seed(_lowercase    )
     SCREAMING_SNAKE_CASE								=			{
         """prompt""": """A painting of a squirrel eating a burger""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			LDMTextToImagePipeline(**_lowercase    )
     pipe.to(_lowercase    )
     pipe.set_progress_bar_config(disable=_lowercase    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(_lowercase    )
     SCREAMING_SNAKE_CASE								=			pipe(**_lowercase    ).images
     SCREAMING_SNAKE_CASE								=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_6, 1_6, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3




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

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[int]=torch.floataa							,    snake_case__ :      int=0    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.manual_seed(_lowercase    )
     SCREAMING_SNAKE_CASE								=			np.random.RandomState(_lowercase    ).standard_normal((1, 4, 3_2, 3_2)    )
     SCREAMING_SNAKE_CASE								=			torch.from_numpy(_lowercase    ).to(device=_lowercase							,    dtype=_lowercase    )
     SCREAMING_SNAKE_CASE								=			{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(_lowercase    )
     pipe.set_progress_bar_config(disable=_lowercase    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(_lowercase    )
     SCREAMING_SNAKE_CASE								=			pipe(**_lowercase    ).images
     SCREAMING_SNAKE_CASE								=			image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 2_5_6, 2_5_6, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_slice - image_slice    ).max()
     assert max_diff < 1E-3




@nightly
@require_torch_gpu
class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Dict=torch.floataa							,    snake_case__ :      Union[str, Any]=0    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.manual_seed(_lowercase    )
     SCREAMING_SNAKE_CASE								=			np.random.RandomState(_lowercase    ).standard_normal((1, 4, 3_2, 3_2)    )
     SCREAMING_SNAKE_CASE								=			torch.from_numpy(_lowercase    ).to(device=_lowercase							,    dtype=_lowercase    )
     SCREAMING_SNAKE_CASE								=			{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 5_0,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(_lowercase    )
     pipe.set_progress_bar_config(disable=_lowercase    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(_lowercase    )
     SCREAMING_SNAKE_CASE								=			pipe(**_lowercase    ).images[0]

     SCREAMING_SNAKE_CASE								=			load_numpy(
         'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_image - image    ).max()
     assert max_diff < 1E-3




import heapq
import sys

import numpy as np

a_    :						Optional[int]    		=      tuple[int, int]




class      UpperCamelCase :

    def __init__(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			set()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     return len(self.elements    ) == 0

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return self.elements[0][1]




    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
     self.set.remove(snake_case__    )
     return (priority, item)


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)
 return np.linalg.norm(a - b	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Dict:
 '''simple docstring'''


 return consistent_heuristic(_UpperCamelCase		,							_UpperCamelCase	) // t


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Optional[int]:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  TPos		,							_UpperCamelCase		:  dict[TPos, float]	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			g_function[start] + Wa * heuristics[i](_UpperCamelCase		,							_UpperCamelCase	)
 return ans


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  Tuple	)		->							int:
 '''simple docstring'''


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

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

 SCREAMING_SNAKE_CASE								=			'-'
 SCREAMING_SNAKE_CASE								=			back_pointer[goal]
 while x != start:
  ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			x
  # print(x)
  SCREAMING_SNAKE_CASE								=			'-'
  SCREAMING_SNAKE_CASE								=			back_pointer[x]
 SCREAMING_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:-'	)
 SCREAMING_SNAKE_CASE								=			back_pointer[goal]
 while x != start:
  print(_UpperCamelCase		,							end=' '	)
  SCREAMING_SNAKE_CASE								=			back_pointer[x]
 print(_UpperCamelCase	)
 sys.exit()


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos	)		->							Any:
 '''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        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[Any]		,							)		->							List[Any]:
 '''simple docstring'''


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

   if valid(_UpperCamelCase	) and g_function[neighbours] > g_function[s] + 1:
    SCREAMING_SNAKE_CASE								=			g_function[s] + 1
    SCREAMING_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        __lowerCAmelCase    (       )		->							Tuple:
 '''simple docstring'''


 SCREAMING_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_    :						str    		=      {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a_    :						List[str]    		=      [
    (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_    :						Union[str, Any]    		=      make_common_ground()


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

# start and end destination
a_    :						int    		=      (0, 0)
a_    :						Optional[int]    		=      (n - 1, n - 1)

a_    :						Union[str, Any]    		=      1


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos		,							_UpperCamelCase		:  int	)		->							List[Any]:
 '''simple docstring'''


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

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

 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_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:
     SCREAMING_SNAKE_CASE						,	SCREAMING_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:
     SCREAMING_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)

