



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 		snake_case						(	datasets.Metric  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[Any]:

    """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							:			Any				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[int])				->				str:

    """simple docstring"""







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





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Optional[Any]          =		CTRLTokenizer
   snake_case_					: Optional[Any]          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			int)				->				Dict:

    """simple docstring"""







    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _snake_case    :  Dict					=					["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
    _snake_case    :  Optional[int]					=					dict(zip(lowerCAmelCase				,		range(len(lowerCAmelCase))))
    _snake_case    :  int					=					["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
    _snake_case    :  List[Any]					=					{"""unk_token""": """<unk>"""}

    _snake_case    :  Dict					=					os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["""vocab_file"""])
    _snake_case    :  Any					=					os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["""merges_file"""])
    with open(self.vocab_file				,		"""w"""				,		encoding="""utf-8""") as fp:
     fp.write(json.dumps(lowerCAmelCase) + """\n""")
    with open(self.merges_file				,		"""w"""				,		encoding="""utf-8""") as fp:
     fp.write("""\n""".join(lowerCAmelCase))

   def      UpperCamelCase_  (					self							:			Dict				,		**lowerCAmelCase							:			int)				->				Optional[Any]:

    """simple docstring"""







    kwargs.update(self.special_tokens_map)
    return CTRLTokenizer.from_pretrained(self.tmpdirname				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    return input_text, output_text


   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				str:

    """simple docstring"""







    _snake_case    :  str					=					CTRLTokenizer(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map)
    _snake_case    :  Dict					=					"""adapt react readapt apt"""
    _snake_case    :  int					=					"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
    _snake_case    :  Tuple					=					tokenizer.tokenize(lowerCAmelCase)
    self.assertListEqual(lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  str					=					tokens + [tokenizer.unk_token]

    _snake_case    :  Union[str, Any]					=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)				,		lowerCAmelCase)
