"""simple docstring"""


import heapq
import sys

import numpy as np

UpperCAmelCase__     						=  tuple[int, int]


class 				a :


    def __init__(    self		:     Any    ):
      _UpperCAmelCase         =					[]
      _UpperCAmelCase         =					set()


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


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return len(self.elements    ) == 0


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


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     List[Any]    ):
      if item in self.set:
        self.set.remove(__lowerCAmelCase    )
        _UpperCAmelCase         =					[]
        ((_UpperCAmelCase)       , (_UpperCAmelCase))         =					heapq.heappop(self.elements    )
        while x != item:
          temp.append((pro, x)    )
          ((_UpperCAmelCase)       , (_UpperCAmelCase))         =					heapq.heappop(self.elements    )
        for prito, yyy in temp:
          heapq.heappush(self.elements      , (prito, yyy)    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return self.elements[0][1]


    def    lowerCAmelCase_     (    self		:     Dict    ):
      ((_UpperCAmelCase)       , (_UpperCAmelCase))         =					heapq.heappop(self.elements    )
      self.set.remove(__lowerCAmelCase    )
      return (priority, item)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # euclidean distance
  _UpperCAmelCase         =					np.array(lowercase						)
  _UpperCAmelCase         =					np.array(lowercase						)
  return np.linalg.norm(a - b						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # integer division by time variable
  return consistent_heuristic(lowercase      ,lowercase						) // t






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # manhattan distance
  return abs(p[0] - goal[0]						) + abs(p[1] - goal[1]						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					g_function[start] + Wa * heuristics[i](lowercase      ,lowercase						)
  return ans






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					np.chararray((n, n)						)
  for i in range(lowercase						):
    for j in range(lowercase						):
      _UpperCAmelCase         =					"""*"""

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

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

  for i in range(lowercase						):
    for j in range(lowercase						):
      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(lowercase      ,end=""" """						)
    _UpperCAmelCase         =					back_pointer[x]
  print(lowercase						)
  sys.exit()






def 			__UpperCAmelCase    (	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 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,):







  """simple docstring"""
  for itera in range(lowercase						):
    open_list[itera].remove_element(lowercase						)
  # print("s", s)
  # print("j", j)
  ((_UpperCAmelCase)       , (_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(lowercase						) and neighbours not in visited:
        # print("neighbour", neighbours)
        visited.add(lowercase						)
        _UpperCAmelCase         =					-1
        _UpperCAmelCase         =					float("""inf"""						)

      if valid(lowercase						) 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(lowercase      ,key(lowercase      ,0      ,lowercase      ,lowercase						)						)
          if neighbours not in close_list_inad:
            for var in range(1      ,lowercase						):
              if key(lowercase      ,lowercase      ,lowercase      ,lowercase						) <= Wa * key(
                  lowercase      ,0      ,lowercase      ,lowercase						):
                open_list[j].put(
                    lowercase      ,key(lowercase      ,lowercase      ,lowercase      ,lowercase						)						)






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  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),
    (1_0, 1),
    (1_1, 1),
    (1_2, 1),
    (1_3, 1),
    (1_4, 1),
    (1_5, 1),
    (1_6, 1),
    (1_7, 1),
    (1_8, 1),
    (1_9, 1),
]
UpperCAmelCase__     						=  make_common_ground()


UpperCAmelCase__     						=  blocks_blk
# hyper parameters
UpperCAmelCase__     						=  1
UpperCAmelCase__     						=  1
UpperCAmelCase__     						=  2_0
UpperCAmelCase__     						=  3  # one consistent and two other inconsistent

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

UpperCAmelCase__     						=  1






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







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

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

  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[]
  while open_list[0].minkey() < float("""inf"""						):
    for i in range(1      ,lowercase						):
      # 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(lowercase      ,lowercase      ,lowercase						)
        else:
          _UpperCAmelCase       , _UpperCAmelCase         =					open_list[i].top_show()
          visited.add(lowercase						)
          expand_state(
              lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,)
          close_list_inad.append(lowercase						)
      else:
        if g_function[goal] <= open_list[0].minkey():
          if g_function[goal] < float("""inf"""						):
            do_something(lowercase      ,lowercase      ,lowercase						)
        else:
          _UpperCAmelCase         =					open_list[0].top_show()
          visited.add(lowercase						)
          expand_state(
              lowercase      ,0      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,)
          close_list_anchor.append(lowercase						)
  print("""No path found to goal"""						)
  print()
  for i in range(n - 1      ,-1      ,-1						):
    for j in range(lowercase						):
      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 torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     int   							=							StableUnCLIPPipeline
    _snake_case   :     str   							=							TEXT_TO_IMAGE_PARAMS
    _snake_case   :     Any   							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case   :     Optional[Any]   							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case   :     str   							=							TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _snake_case   :     str   							=							False


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					32
      _UpperCAmelCase         =					embedder_hidden_size

      # prior components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=__lowerCAmelCase      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					PriorTransformer(
          num_attention_heads=2      , attention_head_dim=12      , embedding_dim=__lowerCAmelCase      , num_layers=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDPMScheduler(
          variance_type="""fixed_small_log"""      , prediction_type="""sample"""      , num_train_timesteps=1000      , clip_sample=__lowerCAmelCase      , clip_sample_range=5.0      , beta_schedule="""squaredcos_cap_v2"""      , )

      # regular denoising components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase    )
      _UpperCAmelCase         =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=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      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					UNetaDConditionModel(
          sample_size=32      , in_channels=4      , out_channels=4      , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      , block_out_channels=(32, 64)      , attention_head_dim=(2, 4)      , class_embed_type="""projection"""      , projection_class_embeddings_input_dim=embedder_projection_dim * 2      , cross_attention_dim=__lowerCAmelCase      , layers_per_block=1      , upcast_attention=__lowerCAmelCase      , use_linear_projection=__lowerCAmelCase      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDIMScheduler(
          beta_schedule="""scaled_linear"""      , beta_start=0.00_085      , beta_end=0.012      , prediction_type="""v_prediction"""      , set_alpha_to_one=__lowerCAmelCase      , steps_offset=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					AutoencoderKL()

      _UpperCAmelCase         =					{
          # prior components
          """prior_tokenizer""": prior_tokenizer,
          """prior_text_encoder""": prior_text_encoder,
          """prior""": prior,
          """prior_scheduler""": prior_scheduler,
          # image noising components
          """image_normalizer""": image_normalizer,
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder,
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
      }

      return components


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     str=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
      _UpperCAmelCase         =					{
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """prior_num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase    )



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


    def    lowerCAmelCase_     (    self		:     str    ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""    )

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					torch.Generator(device="""cpu"""    ).manual_seed(0    )
      _UpperCAmelCase         =					pipe("""anime turle"""      , generator=__lowerCAmelCase      , output_type="""np"""    )

      _UpperCAmelCase         =					output.images[0]

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

      assert_mean_pixel_difference(__lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any    ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      _UpperCAmelCase         =					pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					pipe(
          """anime turtle"""      , prior_num_inference_steps=2      , num_inference_steps=2      , output_type="""np"""      , )

      _UpperCAmelCase         =					torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
