



from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

a_			=					TypeVar("""T""")



class  __lowerCAmelCase     (	Generic[T]   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    None

    __lowerCamelCase													=    len(__UpperCAmelCase				)
    __lowerCamelCase													=    [any_type for _ in range(self.N				)] + arr
    __lowerCamelCase													=    fnc
    self.build()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for p in range(self.N - 1	,				0	,				-1				):
       __lowerCamelCase													=    self.fn(self.st[p * 2]	,				self.st[p * 2 + 1]				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    p += self.N
    __lowerCamelCase													=    v
    while p > 1:
       __lowerCamelCase													=    p // 2
       __lowerCamelCase													=    self.fn(self.st[p * 2]	,				self.st[p * 2 + 1]				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):  # noqa: E741



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    l + self.N, r + self.N

    __lowerCamelCase													=    None
    while l <= r:
       if l % 2 == 1:
          __lowerCamelCase													=    self.st[l] if res is None else self.fn(__UpperCAmelCase	,				self.st[l]				)
       if r % 2 == 0:
          __lowerCamelCase													=    self.st[r] if res is None else self.fn(__UpperCAmelCase	,				self.st[r]				)
       __lowerCamelCase							,__lowerCamelCase													=    (l + 1) // 2, (r - 1) // 2
    return res


if __name__ == "__main__":
  from functools import reduce

  a_			=					[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  a_			=					{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  a_			=					SegmentTree(test_array, min)
  a_			=					SegmentTree(test_array, max)
  a_			=					SegmentTree(test_array, lambda a, b: a + b)





  def 		a__			(		):
     for i in range(len(_UpperCamelCase							)							):
        for j in range(_UpperCamelCase ,len(_UpperCamelCase							)							):
           __lowerCamelCase													=    reduce(_UpperCamelCase ,test_array[i : j + 1]							)
           __lowerCamelCase													=    reduce(_UpperCamelCase ,test_array[i : j + 1]							)
           __lowerCamelCase													=    reduce(lambda _UpperCamelCase ,_UpperCamelCase							: a + b ,test_array[i : j + 1]							)
           assert min_range == min_segment_tree.query(_UpperCamelCase ,_UpperCamelCase							)
           assert max_range == max_segment_tree.query(_UpperCamelCase ,_UpperCamelCase							)
           assert sum_range == sum_segment_tree.query(_UpperCamelCase ,_UpperCamelCase							)

  test_all_segments()

  for index, value in test_updates.items():
    a_			=					value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()











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






