



"""simple docstring"""


import datasets

from .evaluate import evaluate


__A   :							Dict										=   "\\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[Any]										=   "\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   :							Union[str, 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  lowercase__						(		self     :    Optional[Any]   )->List[str]:
							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  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    str   )->Optional[Any]:
							_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=__UpperCamelCase				,   predictions=__UpperCamelCase   )
							return score








"""simple docstring"""


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

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


if is_sentencepiece_available():
					from .tokenization_fnet import FNetTokenizer
else:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

							super().__init__(
							    __UpperCamelCase				,   tokenizer_file=__UpperCamelCase				,   do_lower_case=__UpperCamelCase				,   remove_space=__UpperCamelCase				,   keep_accents=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   **__UpperCamelCase				,   )

							_UpperCAmelCase					=    do_lower_case
							_UpperCAmelCase					=    remove_space
							_UpperCAmelCase					=    keep_accents
							_UpperCAmelCase					=    vocab_file
							_UpperCAmelCase					=    False if not self.vocab_file else True



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

							return (out_vocab_file,)



