





'''simple docstring'''

def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
    A__       =    1
    A__       =    0
    A__       =    1
    A__       =    1
    A__       =    3
    A__       =    3
    A__       =    limit
    A__       =    0

    A__       =    ""
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(_lowerCamelCase )
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            A__       =    10 * (r - n * t)
            A__       =    ((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            A__       =    nr
        else:
            A__       =    (2 * q + r) * l
            A__       =    (q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            A__       =    nn
            A__       =    nr
    return result





def 		UpperCamelCase       (							):
    print(F"{calculate_pi(50 ) = }" )
    import doctest

    doctest.testmod()


if __name__ == "__main__":
  main()










'''simple docstring'''

import torch

from transformers import AutoTokenizer






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
     A__       =    AutoTokenizer.from_pretrained(lowercase_							)
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:

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

         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)

     elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
         A__       =    self.tokenizer(
             lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, 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   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
     A__       =    []
     for idx in range(len(W_query["input_ids"]							)							):
         A__       =    A__       =    range(p_start.shape[1]							)

         A__       =    []
         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 lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
         A__       =    []
         for k in range(len(lowercase_							)							):
             if output[k][2] * output[k][3] >= thresh:
                 A__,			A__       =    output[k][0], output[k][1]
                 A__       =    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(lowercase_							)

     return final_outputs



