





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 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			int      )      ->	List[Any]:
    if isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				str          =      np.full((len(__UpperCAmelCase      ), sequence_length, 2)		,   __UpperCAmelCase      )
    else:
        UpperCamelCase__							:				Optional[Any]          =      np.full((len(__UpperCAmelCase      ), sequence_length)		,   __UpperCAmelCase      )

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

    return out_tensor.tolist()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]      )      ->	List[str]:
    UpperCamelCase__							:				List[Any]          =      ord(__UpperCAmelCase      )
    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__							:				Any          =      unicodedata.category(__UpperCAmelCase      )
    if cat.startswith('''P'''      ):
        return True
    return False






@dataclass
class     lowercase__       (	__lowercase				):

   '''simple docstring'''


   a						:							PreTrainedTokenizerBase
   a						:							Union[bool, str, PaddingStrategy]   		=		True
   a						:							Optional[int]   		=		None
   a						:							Optional[int]   		=		None
   a						:							int   		=		-100
   a						:							str   		=		"pt"




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       import torch

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

       UpperCamelCase__							:				List[str]          =      [feature['''ner_tags'''] for feature in features]
       UpperCamelCase__							:				str          =      padding_tensor(UpperCAmelCase__,		-1,		UpperCAmelCase__,		UpperCAmelCase__							)
       UpperCamelCase__							:				Dict          =      [feature['''original_entity_spans'''] for feature in features]
       UpperCamelCase__							:				List[str]          =      padding_tensor(UpperCAmelCase__,		(-1, -1),		UpperCAmelCase__,		UpperCAmelCase__							)
       UpperCamelCase__							:				List[Any]          =      {k: torch.tensor(UpperCAmelCase__,		dtype=torch.intaa							) for k, v in batch.items()}

       return batch





'''simple docstring'''




class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      list[int]    )				->  None:
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			[0] * len_array

        if len_array > 0:
           lowerCAmelCase     =			array[0]

        for i in range(1    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			self.prefix_sum[i - 1] + array[i]





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        if start == 0:
           return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      int    )				->  bool:
        lowerCAmelCase     =			{0}
        for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
              return True

           sums.add(UpperCAmelCase__    )

        return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()





