
import numpy as np




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					(0, 0)
           UpperCamelCase__     : Optional[int]											=					None
           UpperCamelCase__     : Optional[int]											=					0
           UpperCamelCase__     : Optional[int]											=					0
           UpperCamelCase__     : Union[str, Any]											=					0






     def __eq__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return self.position == cell.position






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           print(self.position	)




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=(5, 5)	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					np.zeros(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					world_size[0]
           UpperCamelCase__     : str											=					world_size[1]






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           print(self.w	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Any											=					[]
      UpperCamelCase__     : List[Any]											=					[]
      _open.append(UpperCamelCase__		)

      while _open:
            UpperCamelCase__     : str											=					np.argmin([n.f for n in _open]		)
            UpperCamelCase__     : int											=					_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
                  UpperCamelCase__     : int											=					current.g + 1
                  UpperCamelCase__					,UpperCamelCase__     : List[Any]											=					n.position
                  UpperCamelCase__					,UpperCamelCase__     : Dict											=					goal.position
                  UpperCamelCase__     : Optional[int]											=					(ya - ya) ** 2 + (xa - xa) ** 2
                  UpperCamelCase__     : Tuple											=					n.h + n.g

                  for c in _open:
                        if c == n and c.f < n.f:
                              continue
                  _open.append(UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					[]
      while current.parent is not None:
            path.append(current.position		)
            UpperCamelCase__     : Optional[int]											=					current.parent
      path.append(current.position		)
      return path[::-1]


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




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


