



from __future__ import annotations

__lowerCamelCase         =			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )     ->     tuple[list[list[int]], list[list[int]]]:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__snake_case    )    )
      ]  # the reference grid
      UpperCAmelCase_  :	Dict				      =		1
      UpperCAmelCase_  :	List[str]				      =		[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__snake_case    )    )
      ]  # the action grid

      UpperCAmelCase_  :	str				      =		init[0]
      UpperCAmelCase_  :	Dict				      =		init[1]
      UpperCAmelCase_  :	Union[str, Any]				      =		0
      UpperCAmelCase_  :	List[str]				      =		g + heuristic[x][y]  # cost from starting cell to destination cell
      UpperCAmelCase_  :	Optional[int]				      =		[[f, g, x, y]]

      UpperCAmelCase_  :	Union[str, Any]				      =		False  # flag that is set when search is complete
      UpperCAmelCase_  :	Union[str, Any]				      =		False  # flag set if we can't find expand

      while not found and not resign:
            if len(__snake_case    ) == 0:
                  raise ValueError("Algorithm is unable to find solution"    )
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  UpperCAmelCase_  :	Union[str, Any]				      =		cell.pop()
                  UpperCAmelCase_  :	Optional[Any]				      =		next_cell[2]
                  UpperCAmelCase_  :	int				      =		next_cell[3]
                  UpperCAmelCase_  :	List[str]				      =		next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        UpperCAmelCase_  :	List[str]				      =		True
                  else:
                        for i in range(len(__snake_case    )    ):  # to try out different valid actions
                              UpperCAmelCase_  :	Tuple				      =		x + DIRECTIONS[i][0]
                              UpperCAmelCase_  :	Union[str, Any]				      =		y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(__snake_case    ) and ya >= 0 and ya < len(grid[0]    ):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          UpperCAmelCase_  :	Optional[int]				      =		g + cost
                                          UpperCAmelCase_  :	Dict				      =		ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]    )
                                          UpperCAmelCase_  :	int				      =		1
                                          UpperCAmelCase_  :	Any				      =		i
      UpperCAmelCase_  :	List[Any]				      =		[]
      UpperCAmelCase_  :	Tuple				      =		goal[0]
      UpperCAmelCase_  :	Any				      =		goal[1]
      invpath.append([x, y]    )  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            UpperCAmelCase_  :	List[str]				      =		x - DIRECTIONS[action[x][y]][0]
            UpperCAmelCase_  :	Optional[int]				      =		y - DIRECTIONS[action[x][y]][1]
            UpperCAmelCase_  :	str				      =		xa
            UpperCAmelCase_  :	Any				      =		ya
            invpath.append([x, y]    )

      UpperCAmelCase_  :	int				      =		[]
      for i in range(len(__snake_case    )    ):
            path.append(invpath[len(__snake_case    ) - 1 - i]    )
      return path, action


if __name__ == "__main__":
   __lowerCamelCase         =			[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   __lowerCamelCase         =			[0, 0]
   # all coordinates are given in format [y,x]
   __lowerCamelCase         =			[len(grid) - 1, len(grid[0]) - 1]
   __lowerCamelCase         =			1

   # the cost map which pushes the path closer to the goal
   __lowerCamelCase         =			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         __lowerCamelCase         =			abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            __lowerCamelCase         =			99

   __lowerCamelCase       ,     __lowerCamelCase         =			search(grid, init, goal, cost, heuristic)

   print('''ACTION MAP''')
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
   from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase         =			get_tests_dir('''fixtures/test_sentencepiece.model''')

__lowerCamelCase         =			{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__lowerCamelCase         =			'''>>zh<<'''
__lowerCamelCase         =			'''Helsinki-NLP/'''

if is_torch_available():
   __lowerCamelCase         =			'''pt'''
elif is_tf_available():
   __lowerCamelCase         =			'''tf'''
else:
   __lowerCamelCase         =			'''jax'''


@require_sentencepiece
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				MarianTokenizer
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()
             UpperCAmelCase_  :	Union[str, Any]				      =		["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
             UpperCAmelCase_  :	Union[str, Any]				      =		dict(zip(lowercase     ,range(len(lowercase))))
             UpperCAmelCase_  :	int				      =		Path(self.tmpdirname)
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["vocab"])
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
             if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["source_spm"])
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["target_spm"])

             UpperCAmelCase_  :	str				      =		MarianTokenizer.from_pretrained(self.tmpdirname)
             tokenizer.save_pretrained(self.tmpdirname)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return MarianTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return (
                 "This is a test",
                 "This is a test",
             )



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		"</s>"
             UpperCAmelCase_  :	List[str]				      =		0

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)     ,lowercase)
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		list(self.get_tokenizer().get_vocab().keys())

             self.assertEqual(vocab_keys[0]     ,"</s>")
             self.assertEqual(vocab_keys[1]     ,"<unk>")
             self.assertEqual(vocab_keys[-1]     ,"<pad>")
             self.assertEqual(len(lowercase)     ,9)



       def  A_   (     self):

             """simple docstring"""





             self.assertEqual(self.get_tokenizer().vocab_size     ,9)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
             UpperCAmelCase_  :	Optional[int]				      =		en_de_tokenizer(["I am a small frog"]     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		[38, 121, 14, 697, 38848, 0]
             self.assertListEqual(lowercase     ,batch.input_ids[0])

             UpperCAmelCase_  :	Optional[int]				      =		tempfile.mkdtemp()
             en_de_tokenizer.save_pretrained(lowercase)
             UpperCAmelCase_  :	Any				      =		[x.name for x in Path(lowercase).glob("*")]
             self.assertIn("source.spm"     ,lowercase)
             MarianTokenizer.from_pretrained(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.get_tokenizer()

             UpperCAmelCase_  :	List[Any]				      =		tok(
                 ["I am a small frog" * 1000, "I am a small frog"]     ,padding=lowercase     ,truncation=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch.input_ids.shape     ,(2, 512))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.get_tokenizer()
             UpperCAmelCase_  :	Optional[int]				      =		tok(["I am a tiny frog", "I am a small frog"]     ,padding=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch_smaller.input_ids.shape     ,(2, 10))



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase     ,model_name="Helsinki-NLP/opus-mt-en-de"     ,revision="1a8c2263da11e68e50938f97e10cd57820bd504c"     ,decode_kwargs={"use_source_tokenizer": True}     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

             UpperCAmelCase_  :	Any				      =		"Tämä on testi"
             UpperCAmelCase_  :	List[str]				      =		"This is a test"

             UpperCAmelCase_  :	int				      =		[76, 7, 2047, 2]
             UpperCAmelCase_  :	Any				      =		[69, 12, 11, 940, 2]

             UpperCAmelCase_  :	Any				      =		tokenizer(lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Any				      =		tokenizer(text_target=lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	List[Any]				      =		tokenizer.decode(lowercase     ,skip_special_tokens=lowercase)
             self.assertEqual(lowercase     ,lowercase)
