





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, Pipeline


if is_vision_available():
       from ..image_utils import load_image


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase    : str    	=  logging.get_logger(__name__)


lowercase    : Any    	=  Dict[str, Any]
lowercase    : Any    	=  List[Prediction]

@add_end_docstrings(__UpperCAmelCase )
class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

            super().__init__(*A,       **A      )

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

            requires_backends(self,       '''vision'''      )
            self.check_model_type(
                dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()      )      )

     def 	_UpperCamelCase   (      self,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						{}
            if "threshold" in kwargs:
                   _UpperCamelCase              =						kwargs['''threshold''']
            return {}, {}, postprocess_kwargs

     def __call__(      self,       *A,       **A      ):





            """simple docstring"""

            return super().__call__(*A,       **A      )

     def 	_UpperCamelCase   (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						load_image(A      )
            _UpperCamelCase              =						torch.IntTensor([[image.height, image.width]]      )
            _UpperCamelCase              =						self.image_processor(images=[image],       return_tensors='''pt'''      )
            if self.tokenizer is not None:
                   _UpperCamelCase              =						self.tokenizer(text=inputs['''words'''],       boxes=inputs['''boxes'''],       return_tensors='''pt'''      )
            _UpperCamelCase              =						target_size
            return inputs

     def 	_UpperCamelCase   (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						model_inputs.pop('''target_size'''      )
            _UpperCamelCase              =						self.model(**A      )
            _UpperCamelCase              =						outputs.__class__({'''target_size''': target_size, **outputs}      )
            if self.tokenizer is not None:
                   _UpperCamelCase              =						model_inputs['''bbox''']
            return model_outputs

     def 	_UpperCamelCase   (      self,       A,       A=0.9      ):





            """simple docstring"""

            _UpperCamelCase              =						model_outputs['''target_size''']
            if self.tokenizer is not None:
                   # This is a LayoutLMForTokenClassification variant.
                   # The OCR got the boxes and the model classified the words.
                   _UpperCamelCase       ,		_UpperCamelCase              =						target_size[0].tolist()

                   def unnormalize(A      ):
                          return self._get_bounding_box(
                              torch.Tensor(
                                  [
                                      (width * bbox[0] / 1000),
                                      (height * bbox[1] / 1000),
                                      (width * bbox[2] / 1000),
                                      (height * bbox[3] / 1000),
                                  ]      )      )

                   _UpperCamelCase       ,		_UpperCamelCase              =						model_outputs['''logits'''].squeeze(0      ).softmax(dim=-1      ).max(dim=-1      )
                   _UpperCamelCase              =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                   _UpperCamelCase              =						[unnormalize(A      ) for bbox in model_outputs['''bbox'''].squeeze(0      )]
                   _UpperCamelCase              =						['''score''', '''label''', '''box''']
                   _UpperCamelCase              =						[dict(zip(A,       A      )      ) for vals in zip(scores.tolist(),       A,       A      ) if vals[0] > threshold]
            else:
                   # This is a regular ForObjectDetectionModel
                   _UpperCamelCase              =						self.image_processor.post_process_object_detection(A,       A,       A      )
                   _UpperCamelCase              =						raw_annotations[0]
                   _UpperCamelCase              =						raw_annotation['''scores''']
                   _UpperCamelCase              =						raw_annotation['''labels''']
                   _UpperCamelCase              =						raw_annotation['''boxes''']

                   _UpperCamelCase              =						scores.tolist()
                   _UpperCamelCase              =						[self.model.config.idalabel[label.item()] for label in labels]
                   _UpperCamelCase              =						[self._get_bounding_box(A      ) for box in boxes]

                   # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                   _UpperCamelCase              =						['''score''', '''label''', '''box''']
                   _UpperCamelCase              =						[
                       dict(zip(A,       A      )      )
                       for vals in zip(raw_annotation['''scores'''],       raw_annotation['''labels'''],       raw_annotation['''boxes''']      )
                   ]

            return annotation



     def 	_UpperCamelCase   (      self,       A      ):





            """simple docstring"""

            if self.framework != "pt":
                   raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''      )
            _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						box.int().tolist()
            _UpperCamelCase              =						{
                '''xmin''': xmin,
                '''ymin''': ymin,
                '''xmax''': xmax,
                '''ymax''': ymax,
            }
            return bbox













import datasets

from .evaluate import evaluate


lowercase    : 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}
}
"""

lowercase    : 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.
"""

lowercase    : List[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 ):

     def 	_UpperCamelCase     (      self      ):





            """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 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

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






