


"""simple docstring"""







import datasets

from .evaluate import evaluate


a__				:							Optional[Any]						=     '''\
@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__				:							Tuple						=     '''
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__				:							List[Any]						=     '''
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   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	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   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				Dict   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						__SCREAMING_SNAKE_CASE						   =				[
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						__SCREAMING_SNAKE_CASE						   =				evaluate(dataset=UpperCAmelCase__			,			predictions=UpperCAmelCase__   )
						return score


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)