


"""simple docstring"""







import datasets

from .evaluate import evaluate


lowerCamelCase_       :       List[str]      						=  """\
@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_       :       List[str]      						=  """
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_       :       str      						=  """
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      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Tuple:
     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=__A			,   predictions=__A						)
     return score



"""simple docstring"""







from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE      )
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   **__A						) ->					Dict:
     super().__init__(**__A						)

     if self.framework == "tf":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''						)

     requires_backends(self			,   '''vision'''						)
     self.check_model_type(__A						)


 def __call__(		self			,   __A			,   __A = None			,   **__A			,   ) ->					List[str]:
     if "text_queries" in kwargs:
         a					=kwargs.pop('''text_queries'''						)

     if isinstance(__A			,   (str, Image.Image)						):
         a					={'''image''': image, '''candidate_labels''': candidate_labels}
     else:
         a					=image
     a					=super().__call__(__A			,   **__A						)
     return results


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     if "top_k" in kwargs:
         a					=kwargs['''top_k''']
     return {}, {}, postprocess_params


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(inputs['''image''']						)
     a					=inputs['''candidate_labels''']
     if isinstance(__A			,   __A						):
         a					=candidate_labels.split(''','''						)

     a					=torch.tensor([[image.height, image.width]]			,   dtype=torch.intaa						)
     for i, candidate_label in enumerate(__A						):
         a					=self.tokenizer(__A			,   return_tensors=self.framework						)
         a					=self.image_processor(__A			,   return_tensors=self.framework						)
         yield {
             "is_last": i == len(__A						) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=model_inputs.pop('''target_size'''						)
     a					=model_inputs.pop('''candidate_label'''						)
     a					=model_inputs.pop('''is_last'''						)

     a					=self.model(**__A						)

     a					={'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
     return model_outputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.1			,   __A=None						) ->					List[str]:
     a					=[]
     for model_output in model_outputs:
         a					=model_output['''candidate_label''']
         a					=BaseModelOutput(__A						)
         a					=self.image_processor.post_process_object_detection(
             outputs=__A			,   threshold=__A			,   target_sizes=model_output['''target_size''']						)[0]

         for index in outputs["scores"].nonzero():
             a					=outputs['''scores'''][index].item()
             a					=self._get_bounding_box(outputs['''boxes'''][index][0]						)

             a					={'''score''': score, '''label''': label, '''box''': box}
             results.append(__A						)

     a					=sorted(__A			,   key=lambda __A						: x["score"]			,   reverse=__A						)
     if top_k:
         a					=results[:top_k]

     return results





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict[str, int]:
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''						)
     a		,				a		,				a		,				a					=box.int().tolist()
     a					={
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox