



import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE     				=						"\\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"

SCREAMING_SNAKE_CASE     				=						"\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"

SCREAMING_SNAKE_CASE     				=						"\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 	lowerCamelCase      (						datasets.Metric  ):







					'''simple docstring'''







					def    A__		(			self     ):
							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    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							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=lowerCAmelCase    ,	predictions=lowerCAmelCase     )
							return score






from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							List[List[ImageInput]]:
		if isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ) and isinstance(videos[0]			,     (list, tuple) ) and is_valid_image(videos[0][0] ):
				return videos

		elif isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ) and is_valid_image(videos[0] ):
				return [videos]

		elif is_valid_image(__SCREAMING_SNAKE_CASE ):
				return [[videos]]

		raise ValueError(f'''Could not make batched video from {videos}''' )



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[Any]									=  ['pixel_values']



					def __init__(			self    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	lowerCAmelCase = 1 / 255    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else {"height": 224, "width": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							UpperCAmelCase_						=				do_center_crop
							UpperCAmelCase_						=				crop_size
							UpperCAmelCase_						=				resample
							UpperCAmelCase_						=				do_rescale
							UpperCAmelCase_						=				rescale_factor
							UpperCAmelCase_						=				do_normalize
							UpperCAmelCase_						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCAmelCase_						=				image_std if image_std is not None else IMAGENET_STANDARD_STD



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							if "shortest_edge" in size:
									UpperCAmelCase_						=				get_resize_output_image_size(lowerCAmelCase    ,	size["shortest_edge"]    ,	default_to_square=lowerCAmelCase     )
							elif "height" in size and "width" in size:
									UpperCAmelCase_						=				(size["height"], size["width"])
							else:
									raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
							return resize(lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase     )
							if "height" not in size or "width" not in size:
									raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''     )
							return center_crop(lowerCAmelCase    ,	size=(size["height"], size["width"])    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return rescale(lowerCAmelCase    ,	scale=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return normalize(lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	):
							if do_resize and size is None or resample is None:
									raise ValueError("Size and resample 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.
							UpperCAmelCase_						=				to_numpy_array(lowerCAmelCase     )

							if do_resize:
									UpperCAmelCase_						=				self.resize(image=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase     )

							if do_center_crop:
									UpperCAmelCase_						=				self.center_crop(lowerCAmelCase    ,	size=lowerCAmelCase     )

							if do_rescale:
									UpperCAmelCase_						=				self.rescale(image=lowerCAmelCase    ,	scale=lowerCAmelCase     )

							if do_normalize:
									UpperCAmelCase_						=				self.normalize(image=lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase     )

							UpperCAmelCase_						=				to_channel_dimension_format(lowerCAmelCase    ,	lowerCAmelCase     )
							return image



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_						=				resample if resample is not None else self.resample
							UpperCAmelCase_						=				do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase_						=				do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_						=				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_						=				do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_						=				image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_						=				image_std if image_std is not None else self.image_std

							UpperCAmelCase_						=				size if size is not None else self.size
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

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

							UpperCAmelCase_						=				make_batched(lowerCAmelCase     )

							UpperCAmelCase_						=				[
							    [
							        self._preprocess_image(
							            image=lowerCAmelCase    ,	do_resize=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	do_center_crop=lowerCAmelCase    ,	crop_size=lowerCAmelCase    ,	do_rescale=lowerCAmelCase    ,	rescale_factor=lowerCAmelCase    ,	do_normalize=lowerCAmelCase    ,	image_mean=lowerCAmelCase    ,	image_std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	)
							        for img in video
							    ]
							    for video in videos
							]

							UpperCAmelCase_						=				{"pixel_values": videos}
							return BatchFeature(data=lowerCAmelCase    ,	tensor_type=lowerCAmelCase     )

