






"""simple docstring"""





import datasets

from .evaluate import evaluate


__lowerCamelCase							=  '\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

__lowerCamelCase							=  '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

__lowerCamelCase							=  '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the CUAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> 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\'}]\n    >>> 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\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION			)
class       __A		(   datasets.Metric			):

					def   lowerCamelCase__   (			self  :      Tuple					)			->							int:
											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   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Dict					)			->							List[str]:
											__magic_name__:	Union[str, Any]							=       {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
											__magic_name__:	List[str]							=       [
											    {
											        """paragraphs""": [
											            {
											                """qas""": [
											                    {
											                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
											                        """id""": ref["""id"""],
											                    }
											                    for ref in references
											                ]
											            }
											        ]
											    }
											]
											__magic_name__:	Tuple							=       evaluate(dataset=__snake_case     ,		predictions=__snake_case					)
											return score





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Dict      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,	**snake_case				: Optional[int]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	List[str]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Optional[int]           =   do_resize
							_lowercase       :	List[str]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Union[str, Any]           =   do_center_crop
							_lowercase       :	List[str]           =   crop_size
							_lowercase       :	Tuple           =   do_rescale
							_lowercase       :	Union[str, Any]           =   rescale_factor
							_lowercase       :	str           =   do_normalize
							_lowercase       :	List[Any]           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowercase       :	List[Any]           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_lowercase       :	List[str]           =   int((256 / 224) * size["shortest_edge"]    )
													_lowercase       :	Dict           =   get_resize_output_image_size(snake_case      ,	size=snake_case      ,	default_to_square=snake_case    )
													_lowercase       :	int           =   {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
							return resize(
							    snake_case      ,	size=(size_dict["height"], size_dict["width"])      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[TensorType] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			BatchFeature:
							_lowercase       :	Optional[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Any           =   resample if resample is not None else self.resample
							_lowercase       :	Optional[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	Union[str, Any]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Optional[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Dict           =   image_std if image_std is not None else self.image_std

							_lowercase       :	Optional[int]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Dict           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Dict           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."    )

							if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."    )

							if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."    )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."    )

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Dict           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[Any]           =   [self.resize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	Union[str, Any]           =   [self.center_crop(snake_case      ,	snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	List[str]           =   [self.rescale(snake_case      ,	snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	str           =   [self.normalize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	str           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )




