"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  if isinstance(lowercase   ,lowercase		):
    _UpperCAmelCase						 =      np.full((len(lowercase		), sequence_length, 2)   ,lowercase		)
  else:
    _UpperCAmelCase						 =      np.full((len(lowercase		), sequence_length)   ,lowercase		)

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

  return out_tensor.tolist()






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      ord(lowercase		)
  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
  _UpperCAmelCase						 =      unicodedata.category(lowercase		)
  if cat.startswith("""P"""		):
    return True
  return False


@dataclass
class        a				(		lowerCAmelCase_   ):
    _snake_case     :  PreTrainedTokenizerBase
    _snake_case     :  Union[bool, str, PaddingStrategy]     							=							True
    _snake_case     :  Optional[int]     							=							None
    _snake_case     :  Optional[int]     							=							None
    _snake_case     :  int     							=							-1_00
    _snake_case     :  str     							=							"pt"


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  int					):
      import torch

      _UpperCAmelCase						 =      """label""" if """label""" in features[0].keys() else """labels"""
      _UpperCAmelCase						 =      [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _UpperCAmelCase						 =      self.tokenizer.pad(
          __lowerCAmelCase   ,				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

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

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

      return batch

"""simple docstring"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
