






import numpy as np







class      UpperCAmelCase_		:



      """simple docstring"""
      def __init__(							self					)							-> Tuple:
          UpperCamelCase			:Optional[Any]      				=  (0, 0)
          UpperCamelCase			:str      				=  None
          UpperCamelCase			:List[str]      				=  0
          UpperCamelCase			:Dict      				=  0
          UpperCamelCase			:Tuple      				=  0
      def __eq__(							self       ,							SCREAMING_SNAKE_CASE_					)							-> Optional[int]:
          return self.position == cell.position




      def        UpperCAmelCase		(							self					)							-> Tuple:
          print(self.position					)







class      UpperCAmelCase_		:



      """simple docstring"""
      def __init__(							self       ,							SCREAMING_SNAKE_CASE_=(5, 5)					)							-> List[Any]:
          UpperCamelCase			:Tuple      				=  np.zeros(__UpperCAmelCase					)
          UpperCamelCase			:Optional[Any]      				=  world_size[0]
          UpperCamelCase			:List[str]      				=  world_size[1]
      def        UpperCAmelCase		(							self					)							-> List[str]:
          print(self.w					)




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







def     _A  (							SCREAMING_SNAKE_CASE__    :      Dict	,    SCREAMING_SNAKE_CASE__    :      Dict	,    SCREAMING_SNAKE_CASE__    :      str			):
    UpperCamelCase			:Any      				=  []
    UpperCamelCase			:Dict      				=  []
    _open.append(SCREAMING_SNAKE_CASE__			)

    while _open:
        UpperCamelCase			:List[str]      				=  np.argmin([n.f for n in _open]			)
        UpperCamelCase			:List[Any]      				=  _open[min_f]
        _closed.append(_open.pop(SCREAMING_SNAKE_CASE__			)			)
        if current == goal:
            break
        for n in world.get_neigbours(SCREAMING_SNAKE_CASE__			):
            for c in _closed:
                if c == n:
                    continue
            UpperCamelCase			:str      				=  current.g + 1
            UpperCamelCase				,  UpperCamelCase			:Union[str, Any]      				=  n.position
            UpperCamelCase				,  UpperCamelCase			:List[Any]      				=  goal.position
            UpperCamelCase			:Optional[int]      				=  (ya - ya) ** 2 + (xa - xa) ** 2
            UpperCamelCase			:List[str]      				=  n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(SCREAMING_SNAKE_CASE__			)
    UpperCamelCase			:Optional[int]      				=  []
    while current.parent is not None:
        path.append(current.position			)
        UpperCamelCase			:List[str]      				=  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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__							  =						{
    "vocab_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
        ),
        "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
        "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-openqa": (
            "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-reader": (
            "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-openqa": (
            "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-reader": (
            "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__							  =						{
    "google/realm-cc-news-pretrained-embedder": 512,
    "google/realm-cc-news-pretrained-encoder": 512,
    "google/realm-cc-news-pretrained-scorer": 512,
    "google/realm-cc-news-pretrained-openqa": 512,
    "google/realm-orqa-nq-openqa": 512,
    "google/realm-orqa-nq-reader": 512,
    "google/realm-orqa-wq-openqa": 512,
    "google/realm-orqa-wq-reader": 512,
}

UpperCAmelCase__							  =						{
    "google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-reader": {"do_lower_case": True},
    "google/realm-orqa-wq-openqa": {"do_lower_case": True},
    "google/realm-orqa-wq-reader": {"do_lower_case": True},
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    VOCAB_FILES_NAMES
      __snake_case	    =    PRETRAINED_VOCAB_FILES_MAP
      __snake_case	    =    PRETRAINED_INIT_CONFIGURATION
      __snake_case	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case	    =    RealmTokenizer







      def __init__( self :		List[Any]					,   __UpperCAmelCase :		Union[str, Any]=None					,   __UpperCAmelCase :		Tuple=None					,   __UpperCAmelCase :		List[Any]=True					,   __UpperCAmelCase :		Any="[UNK]"					,   __UpperCAmelCase :		Dict="[SEP]"					,   __UpperCAmelCase :		List[str]="[PAD]"					,   __UpperCAmelCase :		str="[CLS]"					,   __UpperCAmelCase :		Optional[int]="[MASK]"					,   __UpperCAmelCase :		str=True					,   __UpperCAmelCase :		List[str]=None					,   **__UpperCAmelCase :		str					,   )       ->Optional[int]:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase					,   tokenizer_file=__UpperCAmelCase					,   do_lower_case=__UpperCAmelCase					,   unk_token=__UpperCAmelCase					,   sep_token=__UpperCAmelCase					,   pad_token=__UpperCAmelCase					,   cls_token=__UpperCAmelCase					,   mask_token=__UpperCAmelCase					,   tokenize_chinese_chars=__UpperCAmelCase					,   strip_accents=__UpperCAmelCase					,   **__UpperCAmelCase					,   )

       a						    = json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           normalizer_state.get('''lowercase'''					,   __UpperCAmelCase    ) != do_lower_case
           or normalizer_state.get('''strip_accents'''					,   __UpperCAmelCase    ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''					,   __UpperCAmelCase    ) != tokenize_chinese_chars
       ):
        a						    = getattr(__UpperCAmelCase					,   normalizer_state.pop('''type'''    )    )
        a						    = do_lower_case
        a						    = strip_accents
        a						    = tokenize_chinese_chars
        a						    = normalizer_class(**__UpperCAmelCase    )

       a						    = do_lower_case







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		List[Any]					,   **__UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       a						    = PaddingStrategy.MAX_LENGTH

       a						    = text
       a						    = kwargs.pop('''text_pair'''					,   __UpperCAmelCase    )
       a						    = kwargs.pop('''return_tensors'''					,   __UpperCAmelCase    )

       a						    = {
           '''input_ids''': [],
           '''attention_mask''': [],
           '''token_type_ids''': [],
       }

       for idx, candidate_text in enumerate(__UpperCAmelCase    ):
        if batch_text_pair is not None:
         a						    = batch_text_pair[idx]
        else:
         a						    = None

        a						    = super().__call__(__UpperCAmelCase					,   __UpperCAmelCase					,   return_tensors=__UpperCAmelCase					,   **__UpperCAmelCase    )

        a						    = encoded_candidates.get('''input_ids'''    )
        a						    = encoded_candidates.get('''attention_mask'''    )
        a						    = encoded_candidates.get('''token_type_ids'''    )

        if encoded_input_ids is not None:
         output_data["input_ids"].append(__UpperCAmelCase    )
        if encoded_attention_mask is not None:
         output_data["attention_mask"].append(__UpperCAmelCase    )
        if encoded_token_type_ids is not None:
         output_data["token_type_ids"].append(__UpperCAmelCase    )

       a						    = {key: item for key, item in output_data.items() if len(__UpperCAmelCase    ) != 0}

       return BatchEncoding(__UpperCAmelCase					,   tensor_type=__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Optional[int]					,   __UpperCAmelCase :		Dict=None    )       ->str:




       """simple docstring"""


       a						    = [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """simple docstring"""


       a						    = [self.sep_token_id]
       a						    = [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    ) * [0] + len(token_ids_a + sep    ) * [1]







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str] = None    )       ->Tuple[str]:




       """simple docstring"""


       a						    = self._tokenizer.model.save(__UpperCAmelCase					,   name=__UpperCAmelCase    )
       return tuple(__UpperCAmelCase    )


