


import numpy as np



class 	__lowerCamelCase			:







       def __init__(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			(0, 0)
            __UpperCamelCase =			None
            __UpperCamelCase =			0
            __UpperCamelCase =			0
            __UpperCamelCase =			0







       def __eq__(			self: Any,A_: Tuple					):






            '''simple docstring'''




            return self.position == cell.position







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            print(self.position					)



class 	__lowerCamelCase			:







       def __init__(			self: Union[str, Any],A_: List[Any]=(5, 5)					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            print(self.w					)







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			[]
     _open.append(UpperCamelCase__ )

     while _open:
          __UpperCamelCase =			np.argmin([n.f for n in _open] )
          __UpperCamelCase =			_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 =			current.g + 1
               __UpperCamelCase =			n.position
               __UpperCamelCase =			goal.position
               __UpperCamelCase =			(ya - ya) ** 2 + (xa - xa) ** 2
               __UpperCamelCase =			n.h + n.g

               for c in _open:
                    if c == n and c.f < n.f:
                         continue
               _open.append(UpperCamelCase__ )
     __UpperCamelCase =			[]
     while current.parent is not None:
          path.append(current.position )
          __UpperCamelCase =			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)




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : int											=					tempfile.mkdtemp()

           UpperCamelCase__     : str											=					BlipImageProcessor()
           UpperCamelCase__     : List[Any]											=					GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''	)

           UpperCamelCase__     : List[Any]											=					BlipaProcessor(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           processor.save_pretrained(self.tmpdirname	)






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




           """simple docstring"""







           return AutoProcessor.from_pretrained(self.tmpdirname    ,    **__SCREAMING_SNAKE_CASE	).tokenizer






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




           """simple docstring"""







           return AutoProcessor.from_pretrained(self.tmpdirname    ,    **__SCREAMING_SNAKE_CASE	).image_processor






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






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




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					[np.random.randint(2_5_5    ,    size=(3, 3_0, 4_0_0)    ,    dtype=np.uinta	)]

           UpperCamelCase__     : Dict											=					[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE    ,    0    ,    -1	)	) for x in image_inputs]

           return image_inputs






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




           """simple docstring"""







           UpperCamelCase__     : str											=					BlipaProcessor(tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           UpperCamelCase__     : Any											=					self.get_tokenizer(bos_token='''(BOS)'''    ,    eos_token='''(EOS)'''	)
           UpperCamelCase__     : str											=					self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE    ,    padding_value=1.0	)

           UpperCamelCase__     : Optional[Any]											=					BlipaProcessor.from_pretrained(
               self.tmpdirname    ,    bos_token='''(BOS)'''    ,    eos_token='''(EOS)'''    ,    do_normalize=__SCREAMING_SNAKE_CASE    ,    padding_value=1.0	)

           self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()	)
           self.assertIsInstance(processor.tokenizer    ,    __SCREAMING_SNAKE_CASE	)

           self.assertEqual(processor.image_processor.to_json_string()    ,    image_processor_add_kwargs.to_json_string()	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_tokenizer()

           UpperCamelCase__     : int											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					self.prepare_image_inputs()

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

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.get_image_processor()
           UpperCamelCase__     : Any											=					self.get_tokenizer()

           UpperCamelCase__     : Tuple											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					'''lower newer'''

           UpperCamelCase__     : Tuple											=					processor(text=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					tokenizer(__SCREAMING_SNAKE_CASE    ,    return_token_type_ids=__SCREAMING_SNAKE_CASE	)

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.get_image_processor()
           UpperCamelCase__     : Optional[int]											=					self.get_tokenizer()

           UpperCamelCase__     : Dict											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Tuple											=					'''lower newer'''
           UpperCamelCase__     : Any											=					self.prepare_image_inputs()

           UpperCamelCase__     : Any											=					processor(text=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''pixel_values''', '''input_ids''', '''attention_mask''']	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[int]											=					self.get_tokenizer()

           UpperCamelCase__     : Tuple											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           UpperCamelCase__     : Tuple											=					processor.batch_decode(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Dict											=					self.get_tokenizer()

           UpperCamelCase__     : int											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					'''lower newer'''
           UpperCamelCase__     : List[str]											=					self.prepare_image_inputs()

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

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()	)    ,    ['''pixel_values''', '''input_ids''', '''attention_mask''']	)


