




'''simple docstring'''



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

__UpperCamelCase      :       Optional[int]      			=      logging.get_logger(__name__)
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vision-encoder-decoder"
      __a  					=True




      def __init__(  self ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                F"""A configuraton of type {self.model_type} cannot be instantiated because """
                F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""      )

         __a				     =    kwargs.pop('encoder'      )
         __a				     =    encoder_config.pop('model_type'      )
         __a				     =    kwargs.pop('decoder'      )
         __a				     =    decoder_config.pop('model_type'      )

         __a				     =    AutoConfig.for_model(lowerCamelCase ,   **lowerCamelCase      )
         __a				     =    AutoConfig.for_model(lowerCamelCase ,   **lowerCamelCase      )
         __a				     =    True




      @classmethod
      def  __UpperCamelCase							(  cls ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      )							->PretrainedConfig:



         '''simple docstring'''
         logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'      )
         __a				     =    True
         __a				     =    True

         return cls(encoder=encoder_config.to_dict() ,   decoder=decoder_config.to_dict() ,   **lowerCamelCase      )






      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    copy.deepcopy(self.__dict__      )
         __a				     =    self.encoder.to_dict()
         __a				     =    self.decoder.to_dict()
         __a				     =    self.__class__.model_type
         return output







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=version.parse("1.11"     )




      @property
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]      )




      @property
      def  __UpperCamelCase							(  self      )							->float:



         '''simple docstring'''
         return 1e-4






      @property
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}      )







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @property
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a				     =    OrderedDict()
         __a				     =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         __a				     =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         __a				     =    {0: 'batch', 1: 'encoder_sequence'}

         return common_inputs






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = -1 ,   lowerCamelCase = -1 ,   lowerCamelCase = False ,   lowerCamelCase = None ,   )							->Mapping[str, Any]:



         '''simple docstring'''
         import torch

         __a				     =    OrderedDict()

         __a				     =    super().generate_dummy_inputs(
             lowerCamelCase ,   batch_size=lowerCamelCase ,   seq_length=lowerCamelCase ,   is_pair=lowerCamelCase ,   framework=lowerCamelCase      )

         __a     ,			__a				     =    dummy_input['input_ids'].shape
         __a				     =    (batch, encoder_sequence, self._config.encoder_hidden_size)
         __a				     =    dummy_input.pop('input_ids'      )
         __a				     =    dummy_input.pop('attention_mask'      )
         __a				     =    torch.zeros(lowerCamelCase      )

         return common_inputs







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @property
      def  __UpperCamelCase							(  self      )							->None:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->OnnxConfig:



         '''simple docstring'''
         return VisionEncoderDecoderEncoderOnnxConfig(lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = "default"      )							->OnnxConfig:



         '''simple docstring'''
         __a				     =    encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(lowerCamelCase ,   lowerCamelCase      )







'''simple docstring'''



import datasets

from .evaluate import evaluate


__UpperCamelCase      :       int      			=      """\
@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}
}
"""

__UpperCamelCase      :       int      			=      """
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.
"""

__UpperCamelCase      :       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 							__SCREAMING_SNAKE_CASE       (							datasets.Metric     ):




      def  __UpperCamelCase							(  self      )							->Optional[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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __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=lowerCamelCase ,   predictions=lowerCamelCase      )
         return score