

'''simple docstring'''



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       snake_case   (	snake_case      : int					,		snake_case      : Dict					,		snake_case      : str					,		snake_case      : Any			) ->  int:


    """simple docstring"""


    if isinstance(snake_case					,		snake_case			):
        lowerCAmelCase 						=							np.full((len(snake_case			), sequence_length, 2)					,		snake_case			)
    else:
        lowerCAmelCase 						=							np.full((len(snake_case			), sequence_length)					,		snake_case			)

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

    return out_tensor.tolist()


def       snake_case   (	snake_case      : Dict			) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							ord(snake_case			)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    lowerCAmelCase 						=							unicodedata.category(snake_case			)
    if cat.startswith('P'			):
        return True
    return False



@dataclass
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       PreTrainedTokenizerBase
  SCREAMING_SNAKE_CASE			:       Union[bool, str, PaddingStrategy]				=     True
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     None
  SCREAMING_SNAKE_CASE			:       int				=     -100
  SCREAMING_SNAKE_CASE			:       str				=     "pt"

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''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(
          _SCREAMING_SNAKE_CASE      , 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(_SCREAMING_SNAKE_CASE	) + [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE	)) for label in labels
          ]
      else:
          lowerCAmelCase 						=							[
              [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE	)) + list(_SCREAMING_SNAKE_CASE	) for label in labels
          ]

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

      return batch








'''simple docstring'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)			)
    return edges


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





