






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE__		:							Dict 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							int 				=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__		:							List[str] 				=				{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__		:							Dict 				=				{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}







class 				UpperCAmelCase_  (		__lowercase		):
     __lowerCamelCase      				=  VOCAB_FILES_NAMES
     __lowerCamelCase      				=  PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase      				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase      				=  ['''input_ids''', '''attention_mask''']
     __lowerCamelCase      				=  RobertaTokenizer




     def __init__(				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase="replace"	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="<unk>"	,						_lowerCAmelCase="<pad>"	,						_lowerCAmelCase="<mask>"	,						_lowerCAmelCase=False	,						_lowerCAmelCase=True	,						**_lowerCAmelCase	,						):
          super().__init__(
              _lowerCAmelCase	,						_lowerCAmelCase	,						tokenizer_file=_lowerCAmelCase	,						errors=_lowerCAmelCase	,						bos_token=_lowerCAmelCase	,						eos_token=_lowerCAmelCase	,						sep_token=_lowerCAmelCase	,						cls_token=_lowerCAmelCase	,						unk_token=_lowerCAmelCase	,						pad_token=_lowerCAmelCase	,						mask_token=_lowerCAmelCase	,						add_prefix_space=_lowerCAmelCase	,						trim_offsets=_lowerCAmelCase	,						**_lowerCAmelCase	,						)

          UpperCAmelCase__      :  List[str]				      =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
          if pre_tok_state.get("""add_prefix_space"""	,						_lowerCAmelCase		) != add_prefix_space:
               UpperCAmelCase__      :  Union[str, Any]				      =			getattr(_lowerCAmelCase	,						pre_tok_state.pop("""type"""		)		)
               UpperCAmelCase__      :  Optional[Any]				      =			add_prefix_space
               UpperCAmelCase__      :  List[str]				      =			pre_tok_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			add_prefix_space

          UpperCAmelCase__      :  Dict				      =			"""post_processor"""
          UpperCAmelCase__      :  List[Any]				      =			getattr(self.backend_tokenizer	,						_lowerCAmelCase	,						_lowerCAmelCase		)
          if tokenizer_component_instance:
               UpperCAmelCase__      :  str				      =			json.loads(tokenizer_component_instance.__getstate__()		)

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    UpperCAmelCase__      :  Optional[Any]				      =			tuple(state["""sep"""]		)
               if "cls" in state:
                    UpperCAmelCase__      :  str				      =			tuple(state["""cls"""]		)

               UpperCAmelCase__      :  Optional[int]				      =			False

               if state.get("""add_prefix_space"""	,						_lowerCAmelCase		) != add_prefix_space:
                    UpperCAmelCase__      :  Union[str, Any]				      =			add_prefix_space
                    UpperCAmelCase__      :  List[str]				      =			True

               if state.get("""trim_offsets"""	,						_lowerCAmelCase		) != trim_offsets:
                    UpperCAmelCase__      :  List[str]				      =			trim_offsets
                    UpperCAmelCase__      :  Any				      =			True

               if changes_to_apply:
                    UpperCAmelCase__      :  Tuple				      =			getattr(_lowerCAmelCase	,						state.pop("""type"""		)		)
                    UpperCAmelCase__      :  str				      =			component_class(**_lowerCAmelCase		)
                    setattr(self.backend_tokenizer	,						_lowerCAmelCase	,						_lowerCAmelCase		)




     @property
     def   __UpperCAmelCase  (				self		):

          if self._mask_token is None:
               if self.verbose:
                    logger.error("""Using mask_token, but it is not set yet."""		)
               return None
          return str(self._mask_token		)




     @mask_token.setter
     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  List[Any]				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else value
          UpperCAmelCase__      :  List[Any]				      =			value




     def   __UpperCAmelCase  (				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			kwargs.get("""is_split_into_words"""	,						_lowerCAmelCase		)
          assert self.add_prefix_space or not is_split_into_words, (
              f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  List[str]				      =			kwargs.get("""is_split_into_words"""	,						_lowerCAmelCase		)

          assert self.add_prefix_space or not is_split_into_words, (
              f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  int				      =			self._tokenizer.model.save(_lowerCAmelCase	,						name=_lowerCAmelCase		)
          return tuple(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None		):
          UpperCAmelCase__      :  Optional[Any]				      =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  List[str]				      =			[self.sep_token_id]
          UpperCAmelCase__      :  Optional[Any]				      =			[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]













'''simple docstring'''






from __future__ import annotations

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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				tuple[list[list[int]], list[list[int]]]:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the reference grid
			__snake_case					:      Tuple							      =     1
			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the action grid

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

			__snake_case					:      Any							      =     False  # flag that is set when search is complete
			__snake_case					:      int							      =     False  # flag set if we can't find expand

			while not found and not resign:
						if len(_lowerCamelCase ) == 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()
									__snake_case					:      Tuple							      =     cell.pop()
									__snake_case					:      Optional[int]							      =     next_cell[2]
									__snake_case					:      List[Any]							      =     next_cell[3]
									__snake_case					:      int							      =     next_cell[1]

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

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


if __name__ == "__main__":
	__UpperCamelCase					    =	[
	    [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],
	]

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

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

	__UpperCamelCase    ,		__UpperCamelCase					    =	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])


