"""simple docstring"""



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)

@dataclass
class SCREAMING_SNAKE_CASE  (     _snake_case						):







 """simple docstring"""


 a_							:						Union[List[PIL.Image.Image], np.ndarray]
 a_							:						Optional[List[bool]]


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipeline_cycle_diffusion import CycleDiffusionPipeline
     from .pipeline_stable_diffusion import StableDiffusionPipeline
     from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
     from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
     from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
     from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
     from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
     from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
     from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
     from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
     from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
     from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
     from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
     from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
     from .pipeline_stable_unclip import StableUnCLIPPipeline
     from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
     from .safety_checker import StableDiffusionSafetyChecker
     from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
     from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionPixaPixZeroPipeline,
     )
else:
     from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
     from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
     from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
     if not (
         is_torch_available()
         and is_transformers_available()
         and is_k_diffusion_available()
         and is_k_diffusion_version(">=", "0.0.12")
     ):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
     if not (is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
     from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
     from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
     from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
     from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
     import flax

     @flax.struct.dataclass
     class SCREAMING_SNAKE_CASE  (     _snake_case						):







      """simple docstring"""


      a_							:						np.ndarray
      a_							:						List[bool]
     from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
     from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
     from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
     from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
     from .safety_checker_flax import FlaxStableDiffusionSafetyChecker









'''simple docstring'''

import numpy as np







class __a			:





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							(0, 0)
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0





   def __eq__(       self					:      int					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return self.position == cell.position





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

      '''simple docstring'''

      print(self.position     )







class __a			:





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=(5, 5)     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.zeros(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							world_size[0]
      __SCREAMING_SNAKE_CASE   				=							world_size[1]





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      print(self.w     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE   				=							cell.position[0]
      __SCREAMING_SNAKE_CASE   				=							cell.position[1]
      __SCREAMING_SNAKE_CASE   				=							[]
      for n in neughbour_cord:
         __SCREAMING_SNAKE_CASE   				=							current_x + n[0]
         __SCREAMING_SNAKE_CASE   				=							current_y + n[1]
         if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            __SCREAMING_SNAKE_CASE   				=							Cell()
            __SCREAMING_SNAKE_CASE   				=							(x, y)
            __SCREAMING_SNAKE_CASE   				=							cell
            neighbours.append(lowerCamelCase     )
      return neighbours





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							[]
   _open.append(__UpperCAmelCase   )

   while _open:
      __SCREAMING_SNAKE_CASE   				=							np.argmin([n.f for n in _open]   )
      __SCREAMING_SNAKE_CASE   				=							_open[min_f]
      _closed.append(_open.pop(__UpperCAmelCase   )   )
      if current == goal:
         break
      for n in world.get_neigbours(__UpperCAmelCase   ):
         for c in _closed:
            if c == n:
               continue
         __SCREAMING_SNAKE_CASE   				=							current.g + 1
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							n.position
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							goal.position
         __SCREAMING_SNAKE_CASE   				=							(ya - ya) ** 2 + (xa - xa) ** 2
         __SCREAMING_SNAKE_CASE   				=							n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							[]
   while current.parent is not None:
      path.append(current.position   )
      __SCREAMING_SNAKE_CASE   				=							current.parent
   path.append(current.position   )
   return path[::-1]


if __name__ == "__main__":
    a   	=    Gridworld()
    # Start position and goal
    a   	=    Cell()
    a   	=    (0, 0)
    a   	=    Cell()
    a   	=    (4, 4)
    print(F'''path from {start.position} to {goal.position}''')
    a   	=    astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        a   	=    1
    print(world.w)





