



'''simple docstring'''



import datasets

from .evaluate import evaluate


lowerCamelCase									=							'''\
@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}
}
'''

lowerCamelCase									=							'''
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.
'''

lowerCamelCase									=							'''
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 _UpperCamelCase		(  datasets.Metric		):





				'''simple docstring'''





				def 					__lowerCamelCase  (				self	:	Optional[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 					__lowerCamelCase  (				self	:	List[str]	,							_lowerCAmelCase	:	Optional[int]	,							_lowerCAmelCase	:	Tuple):







											'''simple docstring'''

											__lowercase													={prediction["id"]: prediction["prediction_text"] for prediction in predictions}
											__lowercase													=[
											    {
											        "paragraphs": [
											            {
											                "qas": [
											                    {
											                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
											                        "id": ref["id"],
											                    }
											                    for ref in references
											                ]
											            }
											        ]
											    }
											]
											__lowercase													=evaluate(dataset=lowerCAmelCase__	,							predictions=lowerCAmelCase__)
											return score










"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TFMobileBertForMaskedLM,
		    TFMobileBertForMultipleChoice,
		    TFMobileBertForNextSentencePrediction,
		    TFMobileBertForPreTraining,
		    TFMobileBertForQuestionAnswering,
		    TFMobileBertForSequenceClassification,
		    TFMobileBertForTokenClassification,
		    TFMobileBertModel,
		)


@require_tf
class 			__UpperCamelCase							(		a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Optional[int]    =(
							    (
							        TFMobileBertModel,
							        TFMobileBertForMaskedLM,
							        TFMobileBertForNextSentencePrediction,
							        TFMobileBertForPreTraining,
							        TFMobileBertForQuestionAnswering,
							        TFMobileBertForSequenceClassification,
							        TFMobileBertForTokenClassification,
							        TFMobileBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCamelCase  :   Tuple    =(
							    {
							        """feature-extraction""": TFMobileBertModel,
							        """fill-mask""": TFMobileBertForMaskedLM,
							        """question-answering""": TFMobileBertForQuestionAnswering,
							        """text-classification""": TFMobileBertForSequenceClassification,
							        """token-classification""": TFMobileBertForTokenClassification,
							        """zero-shot""": TFMobileBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase  :   Any    =False
							lowerCamelCase  :   str    =False






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					Any:
								a :						Dict      =							super()._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ,   return_labels=lowerCAmelCase__ )

								if return_labels:
									if model_class in get_values(lowerCAmelCase__ ):
										a :						str      =							tf.zeros(self.model_tester.batch_size ,   dtype=tf.intaa )

								return inputs_dict


							class 			__UpperCamelCase							(		a__		):






														def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=13 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					List[str]:
															a :						Union[str, Any]      =							parent
															a :						str      =							batch_size
															a :						Optional[int]      =							seq_length
															a :						Tuple      =							is_training
															a :						int      =							use_input_mask
															a :						Union[str, Any]      =							use_token_type_ids
															a :						List[str]      =							use_labels
															a :						str      =							vocab_size
															a :						List[Any]      =							hidden_size
															a :						Any      =							num_hidden_layers
															a :						List[Any]      =							num_attention_heads
															a :						Tuple      =							intermediate_size
															a :						str      =							hidden_act
															a :						List[Any]      =							hidden_dropout_prob
															a :						Optional[Any]      =							attention_probs_dropout_prob
															a :						Union[str, Any]      =							max_position_embeddings
															a :						Tuple      =							type_vocab_size
															a :						Union[str, Any]      =							type_sequence_label_size
															a :						Optional[int]      =							initializer_range
															a :						Any      =							num_labels
															a :						str      =							num_choices
															a :						List[Any]      =							scope
															a :						str      =							embedding_size






														def        __a  (  self )      ->					Dict:
															a :						Optional[int]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

															a :						Optional[int]      =							None
															if self.use_input_mask:
																a :						int      =							random_attention_mask([self.batch_size, self.seq_length] )

															a :						int      =							None
															if self.use_token_type_ids:
																a :						Tuple      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

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

															a :						Tuple      =							MobileBertConfig(
															    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 ,   embedding_size=self.embedding_size ,   )

															return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					int:
															a :						List[Any]      =							TFMobileBertModel(config=lowerCAmelCase__ )
															a :						int      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[Any]      =							model(lowerCAmelCase__ )

															a :						List[str]      =							[input_ids, input_mask]
															a :						Dict      =							model(lowerCAmelCase__ )

															a :						List[str]      =							model(lowerCAmelCase__ )

															self.parent.assertEqual(
															    result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size) )
															self.parent.assertEqual(result.pooler_output.shape ,   (self.batch_size, self.hidden_size) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[Any]:
															a :						List[Any]      =							TFMobileBertForMaskedLM(config=lowerCAmelCase__ )
															a :						Union[str, Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Optional[int]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					int:
															a :						int      =							TFMobileBertForNextSentencePrediction(config=lowerCAmelCase__ )
															a :						Any      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[str]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, 2) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[str]:
															a :						Dict      =							TFMobileBertForPreTraining(config=lowerCAmelCase__ )
															a :						Union[str, Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[Any]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(
															    result.prediction_logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )
															self.parent.assertEqual(result.seq_relationship_logits.shape ,   (self.batch_size, 2) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Tuple:
															a :						List[str]      =							self.num_labels
															a :						List[str]      =							TFMobileBertForSequenceClassification(config=lowerCAmelCase__ )
															a :						Optional[Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Dict      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Union[str, Any]:
															a :						Optional[int]      =							self.num_choices
															a :						Tuple      =							TFMobileBertForMultipleChoice(config=lowerCAmelCase__ )
															a :						Dict      =							tf.tile(tf.expand_dims(lowerCAmelCase__ ,   1 ) ,   (1, self.num_choices, 1) )
															a :						Optional[Any]      =							tf.tile(tf.expand_dims(lowerCAmelCase__ ,   1 ) ,   (1, self.num_choices, 1) )
															a :						Optional[int]      =							tf.tile(tf.expand_dims(lowerCAmelCase__ ,   1 ) ,   (1, self.num_choices, 1) )
															a :						Dict      =							{
															    "input_ids": multiple_choice_inputs_ids,
															    "attention_mask": multiple_choice_input_mask,
															    "token_type_ids": multiple_choice_token_type_ids,
															}
															a :						List[Any]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
															a :						Any      =							self.num_labels
															a :						Union[str, Any]      =							TFMobileBertForTokenClassification(config=lowerCAmelCase__ )
															a :						Any      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Any      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[str]:
															a :						Dict      =							TFMobileBertForQuestionAnswering(config=lowerCAmelCase__ )
															a :						int      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Optional[Any]      =							model(lowerCAmelCase__ )
															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        __a  (  self )      ->					List[str]:
															a :						Dict      =							self.prepare_config_and_inputs()
															(
															    (
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			
															) :						Dict      =							config_and_inputs
															a :						Optional[int]      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
															return config, inputs_dict






							def        __a  (  self )      ->					List[str]:
								a :						List[str]      =							TFMobileBertModelTest.TFMobileBertModelTester(self )
								a :						str      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   hidden_size=37 )






							def        __a  (  self )      ->					Any:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					List[str]:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						Any      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Any:
								a :						Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase__ )






							def        __a  (  self )      ->					List[str]:
								a :						Tuple      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase__ )






							@slow
							def        __a  (  self )      ->					Any:
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
									a :						Union[str, Any]      =							TFMobileBertModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )


@require_tf
class 			__UpperCamelCase							(		unittest.TestCase		):






							@slow
							def        __a  (  self )      ->					Optional[int]:
								a :						Optional[int]      =							TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
								a :						Any      =							tf.constant([[0, 1, 2, 3, 4, 5]] )
								a :						int      =							model(lowerCAmelCase__ )[0]

								a :						Optional[int]      =							[1, 6, 3_0522]
								self.assertEqual(output.shape ,   lowerCAmelCase__ )

								a :						Optional[Any]      =							tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ] )
								tf.debugging.assert_near(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 )


