
import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE							:List[str]							 =				'\\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'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'\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'

SCREAMING_SNAKE_CASE							:Optional[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 							UpperCAmelCase      (  datasets.Metric  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Tuple ):
					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					: Dict							,A					: Optional[int]							,A					: Dict ):
					__A   			= {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
					__A   			= [
					    {
					        "paragraphs": [
					            {
					                "qas": [
					                    {
					                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
					                        "id": ref["id"],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					__A   			= evaluate(dataset=A							,predictions=A )
					return score





from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


