



"""simple docstring"""






import datasets

from .evaluate import evaluate


_lowercase			:			List[Any]										=						"""\
@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			:			List[Any]										=						"""
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			:			Any										=						"""
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 	__SCREAMING_SNAKE_CASE     (				datasets.Metric       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  str					)-> 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 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional[Any]					)-> int:
								lowerCamelCase__		:		Any		     ={prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
								lowerCamelCase__		:		str		     =[
								    {
								        '''paragraphs''': [
								            {
								                '''qas''': [
								                    {
								                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
								                        '''id''': ref['''id'''],
								                    }
								                    for ref in references
								                ]
								            }
								        ]
								    }
								]
								lowerCamelCase__		:		str		     =evaluate(dataset=lowerCamelCase,  predictions=lowerCamelCase					)
								return score









"""simple docstring"""






import os
from collections import deque

import torch
from torch.utils.data import Dataset

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  Dict="",  lowerCamelCase							:  Tuple="train"					)-> Dict:
								assert os.path.isdir(lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =[]
								lowerCamelCase__		:		Dict		     =os.listdir(lowerCamelCase					)
								for story_filename in story_filenames_list:
											if "summary" in story_filename:
														continue
											lowerCamelCase__		:		Optional[int]		     =os.path.join(lowerCamelCase,  lowerCamelCase					)
											if not os.path.isfile(lowerCamelCase					):
														continue
											self.documents.append(lowerCamelCase					)



					def __len__(	self							:  Optional[Any]					)-> List[str]:
								return len(self.documents					)




					def __getitem__(	self							:  List[str],  lowerCamelCase							:  Dict					)-> str:
								lowerCamelCase__		:		int		     =self.documents[idx]
								lowerCamelCase__		:		List[Any]		     =document_path.split('''/'''					)[-1]
								with open(lowerCamelCase,  encoding='''utf-8'''					) as source:
											lowerCamelCase__		:		Optional[int]		     =source.read()
											lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =process_story(lowerCamelCase					)
								return document_name, story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =list(filter(lambda __lowerCamelCase     : len(__lowerCamelCase     ) != 0							,		[line.strip() for line in raw_story.split('''\n'''     )]     )     )

			# for some unknown reason some lines miss a period, add it
			lowerCamelCase__		:		Dict		     =[_add_missing_period(__lowerCamelCase     ) for line in nonempty_lines]

			# gather article lines
			lowerCamelCase__		:		Union[str, Any]		     =[]
			lowerCamelCase__		:		Optional[Any]		     =deque(__lowerCamelCase     )
			while True:
						try:
									lowerCamelCase__		:		Tuple		     =lines.popleft()
									if element.startswith('''@highlight'''     ):
												break
									story_lines.append(__lowerCamelCase     )
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			lowerCamelCase__		:		Dict		     =list(filter(lambda __lowerCamelCase     : not t.startswith('''@highlight'''     )							,		__lowerCamelCase     )     )

			return story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
			if line.startswith('''@highlight'''     ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			if len(__lowerCamelCase     ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase     ))     )
						return sequence






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =torch.ones_like(__lowerCamelCase     )
			lowerCamelCase__		:		Any		     =sequence == pad_token_id
			lowerCamelCase__		:		List[str]		     =0
			return mask






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[tokenizer.encode(__lowerCamelCase     ) for line in story_lines]
			lowerCamelCase__		:		List[Any]		     =[token for sentence in story_lines_token_ids for token in sentence]
			lowerCamelCase__		:		List[Any]		     =[tokenizer.encode(__lowerCamelCase     ) for line in summary_lines]
			lowerCamelCase__		:		Optional[int]		     =[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =[]
			for sequence in batch:
						lowerCamelCase__		:		Optional[int]		     =-1
						lowerCamelCase__		:		List[str]		     =[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2     )
						batch_embeddings.append(__lowerCamelCase     )
			return torch.tensor(__lowerCamelCase     )




