





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase    : Any    	=  """src/diffusers"""

# Matches is_xxx_available()
lowercase    : str    	=  re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
lowercase    : Dict    	=  re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


lowercase    : Union[str, Any]    	=  """
{0} = None
"""

lowercase    : Union[str, Any]    	=  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


lowercase    : Tuple    	=  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						_re_backend.findall(lowerCAmelCase					)
       if len(lowerCAmelCase					) == 0:
              return None

       return "_and_".join(lowerCAmelCase					)


def     SCREAMING_SNAKE_CASE			(      ):
       with open(os.path.join(lowerCAmelCase    ,       '''__init__.py'''					)    ,       '''r'''    ,       encoding='''utf-8'''    ,       newline='''\n'''					) as f:
              _UpperCamelCase              =						f.readlines()

       # Get to the point we do the actual imports for type checking
       _UpperCamelCase              =						0
       _UpperCamelCase              =						{}
       # Go through the end of the file
       while line_index < len(lowerCAmelCase					):
              # If the line contains is_backend_available, we grab all objects associated with the `else` block
              _UpperCamelCase              =						find_backend(lines[line_index]					)
              if backend is not None:
                     while not lines[line_index].startswith('''else:'''					):
                            line_index += 1
                     line_index += 1
                     _UpperCamelCase              =						[]
                     # Until we unindent, add backend objects to the list
                     while line_index < len(lowerCAmelCase					) and len(lines[line_index]					) > 1:
                            _UpperCamelCase              =						lines[line_index]
                            _UpperCamelCase              =						_re_single_line_import.search(lowerCAmelCase					)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(''', '''					)					)
                            elif line.startswith(''' ''' * 8					):
                                   objects.append(line[8:-2]					)
                            line_index += 1

                     if len(lowerCAmelCase					) > 0:
                            _UpperCamelCase              =						objects
              else:
                     line_index += 1

       return backend_specific_objects


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       if name.isupper():
              return DUMMY_CONSTANT.format(lowerCAmelCase					)
       elif name.islower():
              return DUMMY_FUNCTION.format(lowerCAmelCase    ,       lowerCAmelCase					)
       else:
              return DUMMY_CLASS.format(lowerCAmelCase    ,       lowerCAmelCase					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase=None					):
       if backend_specific_objects is None:
              _UpperCamelCase              =						read_init()
       # For special correspondence backend to module name as used in the function requires_modulename
       _UpperCamelCase              =						{}

       for backend, objects in backend_specific_objects.items():
              _UpperCamelCase              =						'''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_'''					)					) + ''']'''
              _UpperCamelCase              =						'''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
              dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
              dummy_file += "\n".join([create_dummy_object(lowerCAmelCase    ,       lowerCAmelCase					) for o in objects]					)
              _UpperCamelCase              =						dummy_file

       return dummy_files


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase=False					):
       _UpperCamelCase              =						create_dummy_files()
       # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
       _UpperCamelCase              =						{'''torch''': '''pt'''}

       # Locate actual dummy modules and read their content.
       _UpperCamelCase              =						os.path.join(lowerCAmelCase    ,       '''utils'''					)
       _UpperCamelCase              =						{
           backend: os.path.join(lowerCAmelCase    ,       f'''dummy_{short_names.get(lowerCAmelCase    ,       lowerCAmelCase					)}_objects.py'''					)
           for backend in dummy_files.keys()
       }

       _UpperCamelCase              =						{}
       for backend, file_path in dummy_file_paths.items():
              if os.path.isfile(lowerCAmelCase					):
                     with open(lowerCAmelCase    ,       '''r'''    ,       encoding='''utf-8'''    ,       newline='''\n'''					) as f:
                            _UpperCamelCase              =						f.read()
              else:
                     _UpperCamelCase              =						''''''

       for backend in dummy_files.keys():
              if dummy_files[backend] != actual_dummies[backend]:
                     if overwrite:
                            print(
                                f'''Updating diffusers.utils.dummy_{short_names.get(lowerCAmelCase    ,       lowerCAmelCase					)}_objects.py as the main '''
                                '''__init__ has new objects.'''					)
                            with open(dummy_file_paths[backend]    ,       '''w'''    ,       encoding='''utf-8'''    ,       newline='''\n'''					) as f:
                                   f.write(dummy_files[backend]					)
                     else:
                            raise ValueError(
                                '''The main __init__ has objects that are not present in '''
                                f'''diffusers.utils.dummy_{short_names.get(lowerCAmelCase    ,       lowerCAmelCase					)}_objects.py. Run `make fix-copies` '''
                                '''to fix this.'''					)


if __name__ == "__main__":
       lowercase    : Union[str, Any]    	=  argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowercase    : Optional[int]    	=  parser.parse_args()

       check_dummies(args.fix_and_overwrite)













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






