



'''simple docstring'''





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    [[] for _ in range(_UpperCamelCase				)]
    __lowerCamelCase													=    size




 def __getitem__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return iter(self._graph[vertex]				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._size




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''


    if weight not in (0, 1):
       raise ValueError('''Edge weight must be either 0 or 1.'''				)

    if to_vertex < 0 or to_vertex >= self.size:
       raise ValueError('''Vertex indexes must be in [0; size).'''				)

    self._graph[from_vertex].append(Edge(_UpperCamelCase	,				_UpperCamelCase				)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    deque([start_vertex]				)
    __lowerCamelCase													=    [None] * self.size
    __lowerCamelCase													=    0

    while queue:
       __lowerCamelCase													=    queue.popleft()
       __lowerCamelCase													=    distances[current_vertex]
       if current_distance is None:
          continue

       for edge in self[current_vertex]:
          __lowerCamelCase													=    current_distance + edge.weight
          __lowerCamelCase													=    distances[edge.destination_vertex]
          if (
              isinstance(_UpperCamelCase	,				_UpperCamelCase				)
              and new_distance >= dest_vertex_distance
          ):
             continue
          __lowerCamelCase													=    new_distance
          if edge.weight == 0:
             queue.appendleft(edge.destination_vertex				)
          else:
             queue.append(edge.destination_vertex				)

    if distances[finish_vertex] is None:
       raise ValueError('''No path from start_vertex to finish_vertex.'''				)

    return distances[finish_vertex]


if __name__ == "__main__":
  import doctest

  doctest.testmod()











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






