






"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor





class 					_UpperCAmelCase       ( unittest.TestCase							):




					def __init__(				self     :						Dict    ,						A     :						List[Any]    ,						A     :						Tuple=7    ,						A     :						Dict=3    ,						A     :						Optional[Any]=18    ,						A     :						Union[str, Any]=30    ,						A     :						List[str]=4_00    ,						A     :						List[Any]=True    ,						A     :						Union[str, Any]=32    ,						A     :						Any=True    ,						)  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   parent
							lowercase_ : Optional[int]							       =   batch_size
							lowercase_ : Any							       =   num_channels
							lowercase_ : List[str]							       =   image_size
							lowercase_ : Optional[int]							       =   min_resolution
							lowercase_ : Dict							       =   max_resolution
							lowercase_ : str							       =   do_resize
							lowercase_ : Optional[Any]							       =   size_divisor
							lowercase_ : List[Any]							       =   do_rescale






					def   A  (				self     :						Dict   )  ->  Optional[int]:
							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}





@require_torch
@require_vision
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   GLPNImageProcessor if is_vision_available() else None




					def   A  (				self     :						Union[str, Any]   )  ->  List[str]:
							lowercase_ : Any							       =   GLPNImageProcessingTester(self   )




					@property
					def   A  (				self     :						int   )  ->  Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (				self     :						List[str]   )  ->  int:
							lowercase_ : Any							       =   self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(A    ,						'''do_resize'''   )   )
							self.assertTrue(hasattr(A    ,						'''size_divisor'''   )   )
							self.assertTrue(hasattr(A    ,						'''resample'''   )   )
							self.assertTrue(hasattr(A    ,						'''do_rescale'''   )   )




					def   A  (				self     :						Any   )  ->  str:
							pass




					def   A  (				self     :						Tuple   )  ->  Tuple:
							# Initialize image_processing
							lowercase_ : List[Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowercase_ : Optional[int]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						Image.Image   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : str							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )




					def   A  (				self     :						List[Any]   )  ->  Tuple:
							# Initialize image_processing
							lowercase_ : Union[str, Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowercase_ : str							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						numpify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						np.ndarray   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : Any							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






					def   A  (				self     :						Optional[int]   )  ->  List[Any]:
							# Initialize image_processing
							lowercase_ : Optional[int]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowercase_ : Tuple							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						torch.Tensor   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : Optional[int]							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







import datasets

from .evaluate import evaluate


A      :    Dict		=					"\\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"

A      :    Any		=					"\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"

A      :    str		=					"\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 			_lowercase (							datasets.Metric):


       """simple docstring"""




       def 						lowerCAmelCase     (   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 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
             lowerCamelCase__		:				Any    =  [
                 {
                     "paragraphs": [
                         {
                             "qas": [
                                 {
                                     "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                     "id": ref["id"],
                                 }
                                 for ref in references
                             ]
                         }
                     ]
                 }
             ]
             lowerCamelCase__		:				Optional[int]    =  evaluate(dataset=__lowerCamelCase					,      predictions=__lowerCamelCase       )
             return score


