





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    '''files'''    ,       [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]    ,       )
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						tmp_path_factory.mktemp('''dset_infos_dir'''					)
       if "full:README.md" in files:
              with open(dataset_infos_dir / '''README.md'''    ,       '''w'''					) as f:
                     f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''					)
       if "empty:README.md" in files:
              with open(dataset_infos_dir / '''README.md'''    ,       '''w'''					) as f:
                     f.write(''''''					)
    # we want to support dataset_infos.json for backward compatibility
       if "dataset_infos.json" in files:
              with open(dataset_infos_dir / '''dataset_infos.json'''    ,       '''w'''					) as f:
                     f.write('''{"default": {"dataset_size": 42}}'''					)
       _UpperCamelCase              =						DatasetInfosDict.from_directory(lowerCAmelCase					)
       assert dataset_infos
       assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    '''dataset_info'''    ,       [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''    ,       features=Features({'''a''': Value('''int32'''					)}					)    ,       builder_name='''builder'''    ,       config_name='''config'''    ,       version='''1.0.0'''    ,       splits=[{'''name''': '''train'''}]    ,       download_size=42    ,       ),
    ]    ,       )
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						str(lowerCAmelCase					)
       dataset_info.write_to_directory(lowerCAmelCase					)
       _UpperCamelCase              =						DatasetInfo.from_directory(lowerCAmelCase					)
       assert dataset_info == reloaded
       assert os.path.exists(os.path.join(lowerCAmelCase    ,       '''dataset_info.json'''					)					)


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						DatasetInfo(
           description='''foo'''    ,       citation='''bar'''    ,       homepage='''https://foo.bar'''    ,       license='''CC0'''    ,       features=Features({'''a''': Value('''int32'''					)}					)    ,       post_processed={}    ,       supervised_keys=()    ,       task_templates=[]    ,       builder_name='''builder'''    ,       config_name='''config'''    ,       version='''1.0.0'''    ,       splits=[{'''name''': '''train''', '''num_examples''': 42}]    ,       download_checksums={}    ,       download_size=1_337    ,       post_processing_size=442    ,       dataset_size=1_234    ,       size_in_bytes=1_337 + 442 + 1_234    ,       )
       _UpperCamelCase              =						dataset_info._to_yaml_dict()
       assert sorted(lowerCAmelCase					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
       for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
              assert key in dataset_info_yaml_dict
              assert isinstance(dataset_info_yaml_dict[key]    ,       (list, dict, int, str)					)
       _UpperCamelCase              =						yaml.safe_dump(lowerCAmelCase					)
       _UpperCamelCase              =						yaml.safe_load(lowerCAmelCase					)
       assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						DatasetInfo()
       _UpperCamelCase              =						dataset_info._to_yaml_dict()
       assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    '''dataset_infos_dict'''    ,       [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}					),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}					),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''    ,       features=Features({'''a''': Value('''int32'''					)}					)    ,       builder_name='''builder'''    ,       config_name='''config'''    ,       version='''1.0.0'''    ,       splits=[{'''name''': '''train'''}]    ,       download_size=42    ,       )
            }					),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42					),
                '''v2''': DatasetInfo(dataset_size=1_337					),
            }					),
    ]    ,       )
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						str(lowerCAmelCase					)
       dataset_infos_dict.write_to_directory(lowerCAmelCase					)
       _UpperCamelCase              =						DatasetInfosDict.from_directory(lowerCAmelCase					)

       # the config_name of the dataset_infos_dict take over the attribute
       for config_name, dataset_info in dataset_infos_dict.items():
              _UpperCamelCase              =						config_name
              # the yaml representation doesn't include fields like description or citation
              # so we just test that we can recover what we can from the yaml
              _UpperCamelCase              =						DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
       assert dataset_infos_dict == reloaded

       if dataset_infos_dict:
              assert os.path.exists(os.path.join(lowerCAmelCase    ,       '''README.md'''					)					)













import datasets

from .evaluate import evaluate


lowercase    : str    	=  """\
@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}
}
"""

lowercase    : Tuple    	=  """
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.
"""

lowercase    : List[str]    	=  """
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  __A(						datasets.Metric ):

     def 	_UpperCamelCase     (      self      ):





            """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,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
            _UpperCamelCase              =						[
                {
                    '''paragraphs''': [
                        {
                            '''qas''': [
                                {
                                    '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                    '''id''': ref['''id'''],
                                }
                                for ref in references
                            ]
                        }
                    ]
                }
            ]
            _UpperCamelCase              =						evaluate(dataset=A,       predictions=A      )
            return score






