"""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 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Dict:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							{prediction['id']: prediction['prediction_text'] for prediction in predictions}
													A__		   =							[
													    {
													        'paragraphs': [
													            {
													                'qas': [
													                    {
													                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
													                        'id': ref['id'],
													                    }
													                    for ref in references
													                ]
													            }
													        ]
													    }
													]
													A__		   =							evaluate(dataset=__UpperCAmelCase						,predictions=__UpperCAmelCase   )
													return score






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





