

import datasets

from .evaluate import evaluate


_UpperCamelCase 			=		"\\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"

_UpperCamelCase 			=		"\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"

_UpperCamelCase 			=		"\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   __lowercase      (datasets.Metric ):
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       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      ,							A_      ,							A_					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       __lowerCAmelCase					:						List[str]					=					[
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       __lowerCAmelCase					:						Tuple					=					evaluate(dataset=A_      ,							predictions=A_					)
       return score





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							A_      ,							A_=13      ,							A_=7      ,							A_=True      ,							A_=True      ,							A_=True      ,							A_=True      ,							A_=99      ,							A_=32      ,							A_=5      ,							A_=4      ,							A_=37      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=512      ,							A_=16      ,							A_=2      ,							A_=0.02      ,							A_=False      ,							A_=True      ,							A_="None"      ,							A_=3      ,							A_=4      ,							A_=None      ,							)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					parent
       __lowerCAmelCase					:						List[str]					=					batch_size
       __lowerCAmelCase					:						Dict					=					seq_length
       __lowerCAmelCase					:						List[Any]					=					is_training
       __lowerCAmelCase					:						List[Any]					=					use_input_mask
       __lowerCAmelCase					:						Optional[int]					=					use_token_type_ids
       __lowerCAmelCase					:						Tuple					=					use_labels
       __lowerCAmelCase					:						str					=					vocab_size
       __lowerCAmelCase					:						int					=					hidden_size
       __lowerCAmelCase					:						Any					=					num_hidden_layers
       __lowerCAmelCase					:						Any					=					num_attention_heads
       __lowerCAmelCase					:						Dict					=					intermediate_size
       __lowerCAmelCase					:						int					=					hidden_act
       __lowerCAmelCase					:						int					=					hidden_dropout_prob
       __lowerCAmelCase					:						Any					=					attention_probs_dropout_prob
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						Union[str, Any]					=					type_vocab_size
       __lowerCAmelCase					:						Union[str, Any]					=					type_sequence_label_size
       __lowerCAmelCase					:						Optional[int]					=					initializer_range
       __lowerCAmelCase					:						int					=					num_labels
       __lowerCAmelCase					:						int					=					num_choices
       __lowerCAmelCase					:						List[str]					=					relative_attention
       __lowerCAmelCase					:						Union[str, Any]					=					position_biased_input
       __lowerCAmelCase					:						int					=					pos_att_type
       __lowerCAmelCase					:						List[Any]					=					scope
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size					)

       __lowerCAmelCase					:						int					=					None
       if self.use_input_mask:
             __lowerCAmelCase					:						List[Any]					=					ids_tensor([self.batch_size, self.seq_length]      ,							vocab_size=2					)

       __lowerCAmelCase					:						List[str]					=					None
       if self.use_token_type_ids:
             __lowerCAmelCase					:						Tuple					=					ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						List[str]					=					None
       if self.use_labels:
             __lowerCAmelCase					:						str					=					ids_tensor([self.batch_size]      ,							self.type_sequence_label_size					)
             __lowerCAmelCase					:						Dict					=					ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels					)
             __lowerCAmelCase					:						Union[str, Any]					=					ids_tensor([self.batch_size]      ,							self.num_choices					)

       __lowerCAmelCase					:						Tuple					=					self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       return DebertaConfig(
           vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							type_vocab_size=self.type_vocab_size      ,							initializer_range=self.initializer_range      ,							relative_attention=self.relative_attention      ,							position_biased_input=self.position_biased_input      ,							pos_att_type=self.pos_att_type      ,							)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_config()
       __lowerCAmelCase					:						Dict					=					300
       return config
 def 	UpperCamelCase__  (							self      ,							A_					)					->Union[str, Any]:



       '''simple docstring'''





       self.parent.assertListEqual(list(result.loss.size()					)      ,							[]					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					DebertaModel(config=A_					)
       model.to(A_					)
       model.eval()
       __lowerCAmelCase					:						str					=					model(A_      ,							attention_mask=A_      ,							token_type_ids=A_					)[0]
       __lowerCAmelCase					:						Any					=					model(A_      ,							token_type_ids=A_					)[0]
       __lowerCAmelCase					:						List[str]					=					model(A_					)[0]

       self.parent.assertListEqual(list(sequence_output.size()					)      ,							[self.batch_size, self.seq_length, self.hidden_size]					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					DebertaForMaskedLM(config=A_					)
       model.to(A_					)
       model.eval()
       __lowerCAmelCase					:						Union[str, Any]					=					model(A_      ,							attention_mask=A_      ,							token_type_ids=A_      ,							labels=A_					)

       self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.num_labels
       __lowerCAmelCase					:						Tuple					=					DebertaForSequenceClassification(A_					)
       model.to(A_					)
       model.eval()
       __lowerCAmelCase					:						Union[str, Any]					=					model(A_      ,							attention_mask=A_      ,							token_type_ids=A_      ,							labels=A_					)
       self.parent.assertListEqual(list(result.logits.size()					)      ,							[self.batch_size, self.num_labels]					)
       self.check_loss_output(A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.num_labels
       __lowerCAmelCase					:						Optional[int]					=					DebertaForTokenClassification(config=A_					)
       model.to(A_					)
       model.eval()
       __lowerCAmelCase					:						Tuple					=					model(A_      ,							attention_mask=A_      ,							token_type_ids=A_      ,							labels=A_					)
       self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					DebertaForQuestionAnswering(config=A_					)
       model.to(A_					)
       model.eval()
       __lowerCAmelCase					:						int					=					model(
           A_      ,							attention_mask=A_      ,							token_type_ids=A_      ,							start_positions=A_      ,							end_positions=A_      ,							)
       self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)					)
       self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)					)





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.prepare_config_and_inputs()
       (
           (
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							
       )					:						Tuple					=					config_and_inputs
       __lowerCAmelCase					:						Tuple					=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  (
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _UpperCamelCase			  =  (
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  False
 _UpperCamelCase			  =  False
 _UpperCamelCase			  =  False
 _UpperCamelCase			  =  False
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					DebertaModelTester(self					)
       __lowerCAmelCase					:						List[Any]					=					ConfigTester(self      ,							config_class=A_      ,							hidden_size=37					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       self.config_tester.run_common_tests()
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*A_					)
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*A_					)
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*A_					)
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*A_					)





 @slow
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCAmelCase					:						Optional[int]					=					DebertaModel.from_pretrained(A_					)
             self.assertIsNotNone(A_					)







@require_torch
@require_sentencepiece
@require_tokenizers
class   __lowercase      (unittest.TestCase ):
 @unittest.skip(reason='''Model not available yet'''					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       pass





 @slow
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					DebertaModel.from_pretrained('''microsoft/deberta-base'''					)

       __lowerCAmelCase					:						Tuple					=					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
       __lowerCAmelCase					:						Union[str, Any]					=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
       with torch.no_grad():
             __lowerCAmelCase					:						Optional[int]					=					model(A_      ,							attention_mask=A_					)[0]
       # compare the actual values for a slice.
       __lowerCAmelCase					:						Optional[Any]					=					torch.tensor(
           [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]					)
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,							A_      ,							atol=1e-4					)      ,							f"""{output[:, 1:4, 1:4]}"""					)


