

import datasets

from .evaluate import evaluate


A_       : int         =			'\\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_       : List[str]         =			'\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_       : Any         =			'\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     _a						(datasets.Metric				):


					'''simple docstring'''






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






import argparse
from collections import defaultdict

import yaml


A_       : List[str]         =			'docs/source/en/_toctree.yml'

def 						UpperCamelCase					(lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Dict        =		defaultdict(lowercase_					)
	A__							:    Optional[int]        =		[]
	A__							:    Union[str, Any]        =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}					)
		else:
			new_doc_list.append(lowercase_					)

	A__							:    Optional[int]        =		new_doc_list
	A__							:    Optional[int]        =		[key for key, value in counts.items() if value > 1]

	A__							:    Optional[Any]        =		[]
	for duplicate_key in duplicates:
		A__							:    List[Any]        =		list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}					)
		if len(lowercase_					) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
			    """others."""					)
		# Only add this once
		new_doc.append({"""local""": duplicate_key, """title""": titles[0]}					)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]					)
	A__							:    Dict        =		sorted(lowercase_    ,      key=lambda lowercase_					: s["title"].lower()					)

	# "overview" gets special treatment and is always first
	if len(lowercase_					) > 1:
		raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""					)

	overview_doc.extend(lowercase_					)

	# Sort
	return overview_doc

def 						UpperCamelCase					(lowercase_: Tuple=False					)     ->      List[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    Dict        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    List[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    Union[str, Any]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    Dict        =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	A__							:    List[Any]        =		api_doc[scheduler_idx]["""sections"""]
	A__							:    Union[str, Any]        =		clean_doc_toc(lowercase_					)

	A__							:    Optional[int]        =		False
	if new_scheduler_doc != scheduler_doc:
		A__							:    List[Any]        =		True
		if overwrite:
			A__							:    Optional[int]        =		new_scheduler_doc

	if diff:
		if overwrite:
			A__							:    Tuple        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)

def 						UpperCamelCase					(lowercase_: Dict=False					)     ->      Optional[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    int        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    Optional[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    List[str]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    List[Any]        =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	A__							:    Dict        =		False
	A__							:    Tuple        =		api_doc[pipeline_idx]["""sections"""]
	A__							:    Tuple        =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			A__							:    List[Any]        =		pipeline_doc["""section"""]
			A__							:    Dict        =		clean_doc_toc(lowercase_					)
			if overwrite:
				A__							:    Optional[Any]        =		new_sub_pipeline_doc
		new_pipeline_docs.append(lowercase_					)

	# sort overall pipeline doc
	A__							:    Optional[int]        =		clean_doc_toc(lowercase_					)

	if new_pipeline_docs != pipeline_docs:
		A__							:    int        =		True
		if overwrite:
			A__							:    List[Any]        =		new_pipeline_docs

	if diff:
		if overwrite:
			A__							:    Union[str, Any]        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)


if __name__ == "__main__":
	A_       : str         =			argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A_       : str         =			parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)



