
import torch

from transformers import AutoTokenizer

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		a__		):
    _lowerCamelCase      =						AutoTokenizer.from_pretrained(_UpperCAmelCase		)


  def     _UpperCAmelCase  (					self	,		a__		):

    if isinstance(_UpperCAmelCase	,		_UpperCAmelCase		) and all(isinstance(_x	,		_UpperCAmelCase		) for _x in x		):
      _lowerCamelCase      =						None
      for l in x:
        _lowerCamelCase      =						self.tokenizer(
            _UpperCAmelCase	,		padding='''max_length'''	,		max_length=3_84	,		truncation=_UpperCAmelCase	,		return_tensors='''pt'''	,		)
        _lowerCamelCase      =						torch.tensor([len(_UpperCAmelCase		)]		)
        if d is not None:
          for k in d.keys():
            _lowerCamelCase      =						torch.cat((d[k], t[k])	,		0		)
        else:
          _lowerCamelCase      =						t

      _lowerCamelCase      =						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''		)		)
      _lowerCamelCase      =						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''		)		)

    elif isinstance(_UpperCAmelCase	,		_UpperCAmelCase		) and all(isinstance(_x	,		_UpperCAmelCase		) for _x in x		):
      _lowerCamelCase      =						self.tokenizer(
          _UpperCAmelCase	,		padding='''max_length'''	,		max_length=3_84	,		truncation=_UpperCAmelCase	,		return_tensors='''pt'''	,		)

    else:
      raise Exception(
          '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
          ''' strings` for supports are supported.'''		)

    return d


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__	,		a__=0.70		):
    _lowerCamelCase      =						[]
    for idx in range(len(W_query['''input_ids''']		)		):
      _lowerCamelCase      =						_lowerCamelCase      =						range(p_start.shape[1]		)

      _lowerCamelCase      =						[]
      for start_id in start_indexes:
        for end_id in end_indexes:
          if start_id < end_id:
            output.append(
                (
                    start_id,
                    end_id,
                    p_start[idx][start_id].item(),
                    p_end[idx][end_id].item(),
                )		)

      output.sort(key=lambda a__		: (tup[2] * tup[3])	,		reverse=_UpperCAmelCase		)
      _lowerCamelCase      =						[]
      for k in range(len(_UpperCAmelCase		)		):
        if output[k][2] * output[k][3] >= thresh:
          _lowerCamelCase    ,    _lowerCamelCase      =						output[k][0], output[k][1]
          _lowerCamelCase      =						self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]		)
          temp.append((decoded, output[k][2] * output[k][3])		)

      final_outputs.append(_UpperCAmelCase		)

    return final_outputs






import math
def lowerCamelCase_       (			A			:      int						):




      """simple docstring"""

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5		,					int(math.sqrt(A						) + 1						)		,					6						):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


if __name__ == "__main__":
 print(f'''{solution() = }''')



