
"""simple docstring"""




from __future__ import annotations

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

A__      :	List[str]          =    TypeVar("""T""")







class   _lowercase						(       Generic[T]				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					None:
          UpperCAmelCase__							: Any | T 							=						None

          UpperCAmelCase__							: int 							=						len(__UpperCamelCase  )
          UpperCAmelCase__							: list[T] 							=						[any_type for _ in range(self.N  )] + arr
          UpperCAmelCase__							: Tuple 							=						fnc
          self.build()



     def  lowerCAmelCase__       (      self  )->					None:
          for p in range(self.N - 1 ,			0 ,			-1  ):
               UpperCAmelCase__							: Dict 							=						self.fn(self.st[p * 2] ,			self.st[p * 2 + 1]  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					None:
          p += self.N
          UpperCAmelCase__							: Any 							=						v
          while p > 1:
               UpperCAmelCase__							: str 							=						p // 2
               UpperCAmelCase__							: Tuple 							=						self.fn(self.st[p * 2] ,			self.st[p * 2 + 1]  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					T | None:  # noqa: E741
          UpperCAmelCase__							: Optional[int] 							=						l + self.N, r + self.N

          UpperCAmelCase__							: T | None 							=						None
          while l <= r:
               if l % 2 == 1:
                    UpperCAmelCase__							: Union[str, Any] 							=						self.st[l] if res is None else self.fn(__UpperCamelCase ,			self.st[l]  )
               if r % 2 == 0:
                    UpperCAmelCase__							: Any 							=						self.st[r] if res is None else self.fn(__UpperCamelCase ,			self.st[r]  )
               UpperCAmelCase__							: Optional[int] 							=						(l + 1) // 2, (r - 1) // 2
          return res


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

 A__      :	Optional[Any]          =    [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

 A__      :	List[str]          =    {
     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__      :	str          =    SegmentTree(test_array, min)
 A__      :	Tuple          =    SegmentTree(test_array, max)
 A__      :	List[str]          =    SegmentTree(test_array, lambda a, b: a + b)



 def 							a__ (				):






      '''simple docstring'''
      for i in range(len(lowerCAmelCase		)		):
           for j in range(lowerCAmelCase       ,    len(lowerCAmelCase		)		):
                UpperCAmelCase__							: int 							=						reduce(lowerCAmelCase       ,    test_array[i : j + 1]		)
                UpperCAmelCase__							: Tuple 							=						reduce(lowerCAmelCase       ,    test_array[i : j + 1]		)
                UpperCAmelCase__							: List[Any] 							=						reduce(lambda lowerCAmelCase       ,    lowerCAmelCase		: a + b       ,    test_array[i : j + 1]		)
                assert min_range == min_segment_tree.query(lowerCAmelCase       ,    lowerCAmelCase		)
                assert max_range == max_segment_tree.query(lowerCAmelCase       ,    lowerCAmelCase		)
                assert sum_range == sum_segment_tree.query(lowerCAmelCase       ,    lowerCAmelCase		)

 test_all_segments()

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





"""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 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     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
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						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

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



