
'''simple docstring'''







from copy import deepcopy


class 						__UpperCAmelCase      :


							def __init__(       self    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								if arr is None and size is not None:
									_snake_case   						=		size
									_snake_case   						=		[0] * size
								elif arr is not None:
									self.init(lowerCAmelCase_  )
								else:
									raise ValueError('Either arr or size must be specified'  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		len(lowerCAmelCase_  )
								_snake_case   						=		deepcopy(lowerCAmelCase_  )
								for i in range(1    ,   self.size  ):
									_snake_case   						=		self.next_(lowerCAmelCase_  )
									if j < self.size:
										self.tree[j] += self.tree[i]


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.tree[:]
								for i in range(self.size - 1    ,   0    ,   -1  ):
									_snake_case   						=		self.next_(lowerCAmelCase_  )
									if j < self.size:
										arr[j] -= arr[i]
								return arr


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_  ):





								"""simple docstring"""






								return index + (index & (-index))


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_  ):





								"""simple docstring"""






								return index - (index & (-index))


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if index == 0:
									self.tree[0] += value
									return
								while index < self.size:
									self.tree[index] += value
									_snake_case   						=		self.next_(lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								self.add(lowerCAmelCase_    ,   value - self.get(lowerCAmelCase_  )  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if right == 0:
									return 0
								_snake_case   						=		self.tree[0]
								right -= 1  # make right inclusive
								while right > 0:
									result += self.tree[right]
									_snake_case   						=		self.prev(lowerCAmelCase_  )
								return result


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return self.prefix(lowerCAmelCase_  ) - self.prefix(lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return self.query(lowerCAmelCase_    ,   index + 1  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								value -= self.tree[0]
								if value < 0:
									return -1

								_snake_case   						=		1  # Largest power of 2 <= size
								while j * 2 < self.size:
									j *= 2

								_snake_case   						=		0

								while j > 0:
									if i + j < self.size and self.tree[i + j] <= value:
										value -= self.tree[i + j]
										i += j
									j //= 2
								return i


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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							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					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

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

       return batch


