



'''simple docstring'''





import datasets

from .evaluate import evaluate


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

__a          =		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

__a          =		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> 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'}]\n    >>> 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'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION	)
class     UpperCAmelCase_       (       datasets.Metric	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Union[str, Any]						):
          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 lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Any						):
          snake_case__  :    List[Any]    						=      {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
          snake_case__  :    Optional[Any]    						=      [
              {
                  """paragraphs""": [
                      {
                          """qas""": [
                              {
                                  """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                  """id""": ref["""id"""],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          snake_case__  :    Any    						=      evaluate(dataset=snake_case_ ,     predictions=snake_case_						)
          return score





'''simple docstring'''





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      test_file.split(os.path.sep		)
      if components[0:2] != ["tests", "models"]:
            raise ValueError(
                """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
                f"{test_file} instead."		)
      snake_case__  :    Dict    						=      components[-1]
      if not test_fn.endswith("""py"""		):
            raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead."		)
      if not test_fn.startswith("""test_modeling_"""		):
            raise ValueError(
                f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."		)

      snake_case__  :    int    						=      components[:-1] + [test_fn.replace(""".py"""     ,   """"""		)]
      snake_case__  :    int    						=      """.""".join(_lowerCAmelCase		)

      return test_module_path





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__  :    str    						=      get_module_path(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      importlib.import_module(_lowerCAmelCase		)

      return test_module





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    List[Any]    						=      []
      snake_case__  :    Optional[int]    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            if attr.endswith("""ModelTester"""		):
                  tester_classes.append(getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    List[str]    						=      []
      snake_case__  :    Any    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)
            # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
            # `all_model_classes` is not empty (which also excludes other special classes).
            snake_case__  :    List[str]    						=      getattr(_lowerCAmelCase     ,   """all_model_classes"""     ,   []		)
            if len(_lowerCAmelCase		) > 0:
                  test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    Any    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes		)

      # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Optional[int]    						=      test_class()
      if hasattr(_lowerCAmelCase     ,   """setUp"""		):
            test.setUp()

      snake_case__  :    Any    						=      None
      if hasattr(_lowerCAmelCase     ,   """model_tester"""		):
            # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
            if test.model_tester is not None:
                  snake_case__  :    Tuple    						=      test.model_tester.__class__

      return model_tester





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    Union[str, Any]    						=      get_test_classes(_lowerCAmelCase		)

      snake_case__  :    str    						=      []
      for test_class in test_classes:
            if model_class in test_class.all_model_classes:
                  target_test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Optional[Any]    						=      get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Union[str, Any]    						=      []
      for test_class in test_classes:
            snake_case__  :    Tuple    						=      get_model_tester_from_test_class(_lowerCAmelCase		)
            if tester_class is not None:
                  tester_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      snake_case__  :    Optional[Any]    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      {test_class: get_model_tester_from_test_class(_lowerCAmelCase		) for test_class in test_classes}
      return test_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Any    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    Any    						=      {
          model_class: get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_test_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Union[str, Any]    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    str    						=      {
          model_class: get_tester_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_to_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o.__name__
      elif isinstance(_lowerCAmelCase     ,   (list, tuple)		):
            return [to_json(_lowerCAmelCase		) for x in o]
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return {to_json(_lowerCAmelCase		): to_json(_lowerCAmelCase		) for k, v in o.items()}
      else:
            return o
