


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


__lowerCAmelCase     							=			logging.get_logger(__name__)

__lowerCAmelCase     							=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase     							=			{
    '''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'''
        ),
    },
}

__lowerCAmelCase     							=			{
    '''roberta-base''': 5_1_2,
    '''roberta-large''': 5_1_2,
    '''roberta-large-mnli''': 5_1_2,
    '''distilroberta-base''': 5_1_2,
    '''roberta-base-openai-detector''': 5_1_2,
    '''roberta-large-openai-detector''': 5_1_2,
}


class  __SCREAMING_SNAKE_CASE       (						lowercase):
     __SCREAMING_SNAKE_CASE  : Optional[Any]     =			VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  : Dict     =			PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  : Optional[Any]     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  : Optional[Any]     =			["""input_ids""", """attention_mask"""]
     __SCREAMING_SNAKE_CASE  : Union[str, Any]     =			RobertaTokenizer


     def __init__(			self      :		str       ,  __UpperCamelCase      :		Dict=None       ,  __UpperCamelCase      :		Optional[int]=None       ,  __UpperCamelCase      :		Union[str, Any]=None       ,  __UpperCamelCase      :		List[str]="replace"       ,  __UpperCamelCase      :		Tuple="<s>"       ,  __UpperCamelCase      :		Tuple="</s>"       ,  __UpperCamelCase      :		List[Any]="</s>"       ,  __UpperCamelCase      :		int="<s>"       ,  __UpperCamelCase      :		Optional[Any]="<unk>"       ,  __UpperCamelCase      :		List[Any]="<pad>"       ,  __UpperCamelCase      :		str="<mask>"       ,  __UpperCamelCase      :		str=False       ,  __UpperCamelCase      :		List[str]=True       ,  **__UpperCamelCase      :		Any       ,  ):
       super().__init__(
           UpperCamelCase_       ,  UpperCamelCase_       ,  tokenizer_file=UpperCamelCase_       ,  errors=UpperCamelCase_       ,  bos_token=UpperCamelCase_       ,  eos_token=UpperCamelCase_       ,  sep_token=UpperCamelCase_       ,  cls_token=UpperCamelCase_       ,  unk_token=UpperCamelCase_       ,  pad_token=UpperCamelCase_       ,  mask_token=UpperCamelCase_       ,  add_prefix_space=UpperCamelCase_       ,  trim_offsets=UpperCamelCase_       ,  **UpperCamelCase_       ,  )

       _UpperCAmelCase												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get("add_prefix_space"       ,  UpperCamelCase_ ) != add_prefix_space:
         _UpperCAmelCase												=     getattr(UpperCamelCase_       ,  pre_tok_state.pop("type" ) )
         _UpperCAmelCase												=     add_prefix_space
         _UpperCAmelCase												=     pre_tok_class(**UpperCamelCase_ )

       _UpperCAmelCase												=     add_prefix_space

       _UpperCAmelCase												=     "post_processor"
       _UpperCAmelCase												=     getattr(self.backend_tokenizer       ,  UpperCamelCase_       ,  UpperCamelCase_ )
       if tokenizer_component_instance:
         _UpperCAmelCase												=     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												=     tuple(state["sep"] )
         if "cls" in state:
           _UpperCAmelCase												=     tuple(state["cls"] )

         _UpperCAmelCase												=     False

         if state.get("add_prefix_space"       ,  UpperCamelCase_ ) != add_prefix_space:
           _UpperCAmelCase												=     add_prefix_space
           _UpperCAmelCase												=     True

         if state.get("trim_offsets"       ,  UpperCamelCase_ ) != trim_offsets:
           _UpperCAmelCase												=     trim_offsets
           _UpperCAmelCase												=     True

         if changes_to_apply:
           _UpperCAmelCase												=     getattr(UpperCamelCase_       ,  state.pop("type" ) )
           _UpperCAmelCase												=     component_class(**UpperCamelCase_ )
           setattr(self.backend_tokenizer       ,  UpperCamelCase_       ,  UpperCamelCase_ )


     @property
     def 							UpperCAmelCase__  (			self      :		Optional[Any] ):
       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      :		Union[str, Any]       ,  __UpperCamelCase      :		Dict ):
       _UpperCAmelCase												=     AddedToken(UpperCamelCase_       ,  lstrip=UpperCamelCase_       ,  rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_       ,  UpperCamelCase_ ) else value
       _UpperCAmelCase												=     value


     def 							UpperCAmelCase__  (			self      :		str       ,  *__UpperCamelCase      :		Any       ,  **__UpperCamelCase      :		Union[str, Any] ):
       _UpperCAmelCase												=     kwargs.get("is_split_into_words"       ,  UpperCamelCase_ )
       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(*UpperCamelCase_       ,  **UpperCamelCase_ )


     def 							UpperCAmelCase__  (			self      :		int       ,  *__UpperCamelCase      :		int       ,  **__UpperCamelCase      :		Optional[int] ):
       _UpperCAmelCase												=     kwargs.get("is_split_into_words"       ,  UpperCamelCase_ )

       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(*UpperCamelCase_       ,  **UpperCamelCase_ )


     def 							UpperCAmelCase__  (			self      :		Optional[Any]       ,  __UpperCamelCase      :		Any       ,  __UpperCamelCase      :		int = None ):
       _UpperCAmelCase												=     self._tokenizer.model.save(UpperCamelCase_       ,  name=UpperCamelCase_ )
       return tuple(UpperCamelCase_ )


     def 							UpperCAmelCase__  (			self      :		Dict       ,  __UpperCamelCase      :		Optional[Any]       ,  __UpperCamelCase      :		Tuple=None ):
       _UpperCAmelCase												=     [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      :		str       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		Any = None ):
       _UpperCAmelCase												=     [self.sep_token_id]
       _UpperCAmelCase												=     [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

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

			if current_node.pos == self.target.pos:
				snake_case__	       =   True
				return self.retrace_path(UpperCamelCase_		)

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			for child_node in successors:
				if child_node in self.closed_nodes:
					continue

				if child_node not in self.open_nodes:
					self.open_nodes.append(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

					if child_node.g_cost < better_node.g_cost:
						self.open_nodes.append(UpperCamelCase_		)
					else:
						self.open_nodes.append(UpperCamelCase_		)

		if not self.reached:
			return [self.start.pos]
		return None





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

			if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCamelCase_		) - 1):
				continue

			if grid[pos_y][pos_x] != 0:
				continue

			successors.append(
			    Node(
			        UpperCamelCase_      ,       UpperCamelCase_      ,       self.target.pos_y      ,       self.target.pos_x      ,       parent.g_cost + 1      ,       UpperCamelCase_      ,       )		)
		return successors





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


if __name__ == "__main__":
			a__					:			List[str]          =     (0, 0)
			a__					:			Dict          =     (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



