


"""simple docstring"""



import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}







class _lowerCAmelCase       :







			def __init__(			self		,			UpperCamelCase__=None		,			**UpperCamelCase__		)			->	List[str]:

							'''simple docstring'''




							logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."		)
							snake_case					:   Optional[int]         =					model
							snake_case					:   str         =					kwargs.get("model_save_dir"		,			UpperCamelCase__		)
							snake_case					:   List[str]         =					kwargs.get("latest_model_name"		,			UpperCamelCase__		)







			def __call__(			self		,			**UpperCamelCase__		)			->	Optional[int]:

							'''simple docstring'''




							snake_case					:   Tuple         =					{k: np.array(UpperCamelCase__		) for k, v in kwargs.items()}
							return self.model.run(UpperCamelCase__		,			UpperCamelCase__		)







			@staticmethod
			def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__=None		,			UpperCamelCase__=None		)			->	Optional[Any]:

							'''simple docstring'''




							if provider is None:
											logger.info("No onnxruntime provider specified, using CPUExecutionProvider"		)
											snake_case					:   Optional[Any]         =					"CPUExecutionProvider"

							return ort.InferenceSession(UpperCamelCase__		,			providers=[provider]		,			sess_options=UpperCamelCase__		)







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			**UpperCamelCase__		)			->	int:

							'''simple docstring'''




							snake_case					:   Union[str, Any]         =					file_name if file_name is not None else ONNX_WEIGHTS_NAME

							snake_case					:   Optional[int]         =					self.model_save_dir.joinpath(self.latest_model_name		)
							snake_case					:   Optional[Any]         =					Path(UpperCamelCase__		).joinpath(UpperCamelCase__		)
							try:
											shutil.copyfile(UpperCamelCase__		,			UpperCamelCase__		)
							except shutil.SameFileError:
											pass

							# copy external weights (for models >2GB)
							snake_case					:   str         =					self.model_save_dir.joinpath(UpperCamelCase__		)
							if src_path.exists():
											snake_case					:   List[str]         =					Path(UpperCamelCase__		).joinpath(UpperCamelCase__		)
											try:
															shutil.copyfile(UpperCamelCase__		,			UpperCamelCase__		)
											except shutil.SameFileError:
															pass







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			**UpperCamelCase__		,			)			->	Optional[Any]:

							'''simple docstring'''




							if os.path.isfile(UpperCamelCase__		):
											logger.error(F'Provided path ({save_directory}) should be a directory, not a file'		)
											return

							os.makedirs(UpperCamelCase__		,			exist_ok=UpperCamelCase__		)

							# saving model weights/files
							self._save_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)







			@classmethod
			def 				lowerCamelCase	(			cls		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	Union[str, Any]:

							'''simple docstring'''




							snake_case					:   Optional[int]         =					file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(UpperCamelCase__		):
											snake_case					:   Optional[int]         =					OnnxRuntimeModel.load_model(
											    os.path.join(UpperCamelCase__		,			UpperCamelCase__		)		,			provider=UpperCamelCase__		,			sess_options=UpperCamelCase__		)
											snake_case					:   Optional[int]         =					Path(UpperCamelCase__		)
							# load model from hub
							else:
											# download model
											snake_case					:   Any         =					hf_hub_download(
											    repo_id=UpperCamelCase__		,			filename=UpperCamelCase__		,			use_auth_token=UpperCamelCase__		,			revision=UpperCamelCase__		,			cache_dir=UpperCamelCase__		,			force_download=UpperCamelCase__		,			)
											snake_case					:   Any         =					Path(UpperCamelCase__		).parent
											snake_case					:   Tuple         =					Path(UpperCamelCase__		).name
											snake_case					:   List[Any]         =					OnnxRuntimeModel.load_model(UpperCamelCase__		,			provider=UpperCamelCase__		,			sess_options=UpperCamelCase__		)
							return cls(model=UpperCamelCase__		,			**UpperCamelCase__		)







			@classmethod
			def 				lowerCamelCase	(			cls		,			UpperCamelCase__		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	List[str]:

							'''simple docstring'''




							snake_case					:   Dict         =					None
							if len(str(UpperCamelCase__		).split("@"		)		) == 2:
											snake_case					:   Optional[int]         =					model_id.split("@"		)

							return cls._from_pretrained(
							    model_id=UpperCamelCase__		,			revision=UpperCamelCase__		,			cache_dir=UpperCamelCase__		,			force_download=UpperCamelCase__		,			use_auth_token=UpperCamelCase__		,			**UpperCamelCase__		,			)









"""simple docstring"""



import numpy as np







class _lowerCAmelCase       :







   def __init__(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					(0, 0)
       snake_case					:   str         =					None
       snake_case					:   int         =					0
       snake_case					:   Optional[Any]         =					0
       snake_case					:   Tuple         =					0







   def __eq__(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return self.position == cell.position







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       print(self.position		)







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__=(5, 5)		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					np.zeros(UpperCamelCase__		)
       snake_case					:   str         =					world_size[0]
       snake_case					:   str         =					world_size[1]







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       print(self.w		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case					:   Tuple         =					cell.position[0]
       snake_case					:   str         =					cell.position[1]
       snake_case					:   List[Any]         =					[]
       for n in neughbour_cord:
           snake_case					:   Any         =					current_x + n[0]
           snake_case					:   List[str]         =					current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
               snake_case					:   Tuple         =					Cell()
               snake_case					:   int         =					(x, y)
               snake_case					:   List[Any]         =					cell
               neighbours.append(UpperCamelCase__		)
       return neighbours







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					[]
    snake_case					:   List[Any]         =					[]
    _open.append(lowercase				)

    while _open:
        snake_case					:   Union[str, Any]         =					np.argmin([n.f for n in _open]				)
        snake_case					:   Tuple         =					_open[min_f]
        _closed.append(_open.pop(lowercase				)				)
        if current == goal:
            break
        for n in world.get_neigbours(lowercase				):
            for c in _closed:
                if c == n:
                    continue
            snake_case					:   int         =					current.g + 1
            snake_case      ,snake_case					:   Any         =					n.position
            snake_case      ,snake_case					:   Any         =					goal.position
            snake_case					:   int         =					(ya - ya) ** 2 + (xa - xa) ** 2
            snake_case					:   List[Any]         =					n.h + n.g

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


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





