






"""simple docstring"""





import datasets

from .evaluate import evaluate


__lowerCamelCase							=  '\\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'

__lowerCamelCase							=  '\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'

__lowerCamelCase							=  '\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			):

					def   lowerCamelCase__   (			self  :      Tuple					)			->							int:
											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  :      List[str]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Dict					)			->							List[str]:
											__magic_name__:	Union[str, Any]							=       {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
											__magic_name__:	List[str]							=       [
											    {
											        """paragraphs""": [
											            {
											                """qas""": [
											                    {
											                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
											                        """id""": ref["""id"""],
											                    }
											                    for ref in references
											                ]
											            }
											        ]
											    }
											]
											__magic_name__:	Tuple							=       evaluate(dataset=__snake_case     ,		predictions=__snake_case					)
											return score







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""	)

					if not os.path.isdir(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

					if os.path.abspath(self.vocab_file	) != os.path.abspath(A	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

