




"""simple docstring"""


import datasets

from .evaluate import evaluate


A_      			=       '''\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
'''

A_      			=       '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''

A_      			=       '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the CUAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'aupr\': Area Under the Precision-Recall curve
    \'prec_at_80_recall\': Precision at 80% recall
    \'prec_at_90_recall\': Precision at 90% recall
Examples:
    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
    >>> cuad_metric = datasets.load_metric("cuad")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,    _KWARGS_DESCRIPTION				)
class       lowercase(  datasets.Metric				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   return datasets.MetricInfo(
       description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
           {
               """predictions""": {
                   """id""": datasets.Value("""string"""	),
                   """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""	)	),
               },
               """references""": {
                   """id""": datasets.Value("""string"""	),
                   """answers""": datasets.features.Sequence(
                       {
                           """text""": datasets.Value("""string"""	),
                           """answer_start""": datasets.Value("""int32"""	),
                       }	),
               },
           }	),   codebase_urls=["""https://www.atticusprojectai.org/cuad"""],   reference_urls=["""https://www.atticusprojectai.org/cuad"""],   )


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	int,   a_:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
   _snake_case							:	Tuple			=						[
       {
           """paragraphs""": [
               {
                   """qas""": [
                       {
                           """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                           """id""": ref["""id"""],
                       }
                       for ref in references
                   ]
               }
           ]
       }
   ]
   _snake_case							:	Optional[int]			=						evaluate(dataset=a_,   predictions=a_	)
   return score








"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

A_      			=       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_      			=       [ord(letter) for letter in string.ascii_lowercase]
A_      			=       {ord(char) for char in VALID_CHARS}

A_      			=       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      tuple[int, ...]					):
  """simple docstring"""

  _snake_case							:	str			=						""
  _snake_case							:	int
  _snake_case							:	int
  _snake_case							:	int

  for keychar, cipherchar in zip(cycle(snake_case__					)	,     snake_case__					):
    _snake_case							:	List[str]			=						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(snake_case__					)

  return decoded


def       UpperCAmelCase__ (snake_case__							:      list[int]					):
  """simple docstring"""

  _snake_case							:	list[str]			=						[]
  for key in product(snake_case__	,     repeat=3					):
    _snake_case							:	List[Any]			=						try_key(snake_case__	,     snake_case__					)
    if encoded is not None:
      possibles.append(snake_case__					)
  return possibles


def       UpperCAmelCase__ (snake_case__							:      list[str]	,     snake_case__							:      str					):
  """simple docstring"""

  return [possible for possible in possibles if common_word in possible.lower()]


def       UpperCAmelCase__ (snake_case__							:      str = "p059_cipher.txt"					):
  """simple docstring"""

  _snake_case							:	list[int]
  _snake_case							:	list[str]
  _snake_case							:	str
  _snake_case							:	str
  _snake_case							:	str			=						Path(snake_case__					).parent.joinpath(snake_case__					).read_text(encoding="""utf-8"""					)

  _snake_case							:	List[Any]			=						[int(snake_case__					) for number in data.strip().split(""","""					)]

  _snake_case							:	Optional[Any]			=						filter_valid_chars(snake_case__					)
  for common_word in COMMON_WORDS:
    _snake_case							:	Union[str, Any]			=						filter_common_word(snake_case__	,     snake_case__					)
    if len(snake_case__					) == 1:
      break

  _snake_case							:	Optional[int]			=						possibles[0]
  return sum(ord(snake_case__					) for char in decoded_text					)


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


