




"""simple docstring"""





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      a__				(	datasets.Metric   ):


			def       a_     (      self      :    Union[str, 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       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						__UpperCAmelCase	: Union[str, Any]		=					[
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						__UpperCAmelCase	: List[Any]		=					evaluate(dataset=UpperCamelCase_      ,  predictions=UpperCamelCase_)
						return score








"""simple docstring"""





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


A   =       logging.get_logger(__name__)

A   =       {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"gpt_bigcode"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}


			def __init__(      self      :    Any      ,  UpperCamelCase_      :    Tuple=50257      ,  UpperCamelCase_      :    Dict=1024      ,  UpperCamelCase_      :    Optional[Any]=768      ,  UpperCamelCase_      :    Optional[Any]=12      ,  UpperCamelCase_      :    Any=12      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    List[Any]="gelu_pytorch_tanh"      ,  UpperCamelCase_      :    Optional[Any]=0.1      ,  UpperCamelCase_      :    Union[str, Any]=0.1      ,  UpperCamelCase_      :    Tuple=0.1      ,  UpperCamelCase_      :    Tuple=1e-5      ,  UpperCamelCase_      :    List[str]=0.02      ,  UpperCamelCase_      :    Dict=True      ,  UpperCamelCase_      :    Optional[int]=True      ,  UpperCamelCase_      :    str=50256      ,  UpperCamelCase_      :    Union[str, Any]=50256      ,  UpperCamelCase_      :    Dict=True      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					vocab_size
						__UpperCAmelCase	: Optional[int]		=					n_positions
						__UpperCAmelCase	: Tuple		=					n_embd
						__UpperCAmelCase	: str		=					n_layer
						__UpperCAmelCase	: Dict		=					n_head
						__UpperCAmelCase	: Optional[Any]		=					n_inner
						__UpperCAmelCase	: Optional[Any]		=					activation_function
						__UpperCAmelCase	: List[str]		=					resid_pdrop
						__UpperCAmelCase	: List[Any]		=					embd_pdrop
						__UpperCAmelCase	: Optional[Any]		=					attn_pdrop
						__UpperCAmelCase	: Dict		=					layer_norm_epsilon
						__UpperCAmelCase	: List[str]		=					initializer_range
						__UpperCAmelCase	: int		=					scale_attn_weights
						__UpperCAmelCase	: Tuple		=					use_cache
						__UpperCAmelCase	: List[Any]		=					attention_softmax_in_fpaa
						__UpperCAmelCase	: Any		=					scale_attention_softmax_in_fpaa
						__UpperCAmelCase	: str		=					multi_query

						__UpperCAmelCase	: int		=					bos_token_id
						__UpperCAmelCase	: str		=					eos_token_id

						super().__init__(bos_token_id=UpperCamelCase_      ,  eos_token_id=UpperCamelCase_      ,  **UpperCamelCase_)


