
import datasets

from .evaluate import evaluate


__A				='''\
@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}
}
'''

__A				='''
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.
'''

__A				='''
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						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
					lowerCamelCase_						=			[
					    {
					        "paragraphs": [
					            {
					                "qas": [
					                    {
					                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
					                        "id": ref["id"],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					lowerCamelCase_						=			evaluate(dataset=lowercase						,  predictions=lowercase )
					return score








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

UpperCamelCase__    			=			"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    int					=    CamembertTokenizer
						_a							:    Dict					=    CamembertTokenizerFast
						_a							:    Tuple					=    True
						_a							:    List[Any]					=    True






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "<pad>"
											__lowerCAmelCase				   =       1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A			)		,			_A			)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A			)		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       list(self.get_tokenizer().get_vocab().keys()			)

											self.assertEqual(vocab_keys[0]		,			"<s>NOTUSED"			)
											self.assertEqual(vocab_keys[1]		,			"<pad>"			)
											self.assertEqual(vocab_keys[-1]		,			"<mask>"			)
											self.assertEqual(len(_A			)		,			1_0_0_4			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.assertEqual(self.get_tokenizer().vocab_size		,			1_0_0_5			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)
											__lowerCAmelCase				   =       CamembertTokenizerFast.from_pretrained(self.tmpdirname			)

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											# <unk> tokens are not the same for `rust` than for `slow`.
											# Because spm gives back raw token instead of `unk` in EncodeAsPieces
											# tokens = tokenizer.tokenize(sequence)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.test_rust_tokenizer:
																return

											__lowerCAmelCase				   =       self.get_tokenizer()
											__lowerCAmelCase				   =       self.get_rust_tokenizer()

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.tokenize(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       self.get_rust_tokenizer()
											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# camembert is a french model. So we also use french texts.
											__lowerCAmelCase				   =       [
											    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
											    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
											    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
											    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
											    "telles que la traduction et la synthèse de texte.",
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=_A		,			model_name="camembert-base"		,			revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"		,			sequences=_A		,			)




