


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			List[Any]=7					,      __lowerCamelCase							:			Dict=3					,      __lowerCamelCase							:			Any=30					,      __lowerCamelCase							:			str=400					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			Optional[Any]=None					,      __lowerCamelCase							:			Optional[int]=True					,      __lowerCamelCase							:			List[str]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			Tuple=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			Dict=1 / 255					,      __lowerCamelCase							:			Optional[int]=True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
             lowerCamelCase__		:				Optional[int]    =  parent
             lowerCamelCase__		:				Dict    =  batch_size
             lowerCamelCase__		:				Optional[Any]    =  num_channels
             lowerCamelCase__		:				Optional[int]    =  min_resolution
             lowerCamelCase__		:				List[Any]    =  max_resolution
             lowerCamelCase__		:				Optional[Any]    =  do_resize
             lowerCamelCase__		:				Union[str, Any]    =  size
             lowerCamelCase__		:				List[Any]    =  do_normalize
             lowerCamelCase__		:				List[Any]    =  image_mean
             lowerCamelCase__		:				Optional[Any]    =  image_std
             lowerCamelCase__		:				Union[str, Any]    =  do_rescale
             lowerCamelCase__		:				Dict    =  rescale_factor
             lowerCamelCase__		:				List[Any]    =  do_pad





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			str=False       ):


             '''simple docstring'''



             if not batched:
                   lowerCamelCase__		:				Dict    =  image_inputs[0]
                   if isinstance(__lowerCamelCase					,      Image.Image       ):
                         lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  image.size
                   else:
                         lowerCamelCase__    ,   lowerCamelCase__		:				str    =  image.shape[1], image.shape[2]
                   if w < h:
                         lowerCamelCase__		:				str    =  int(self.size["shortest_edge"] * h / w       )
                         lowerCamelCase__		:				Optional[Any]    =  self.size["shortest_edge"]
                   elif w > h:
                         lowerCamelCase__		:				Optional[Any]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				List[str]    =  int(self.size["shortest_edge"] * w / h       )
                   else:
                         lowerCamelCase__		:				Tuple    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				str    =  self.size["shortest_edge"]

             else:
                   lowerCamelCase__		:				Union[str, Any]    =  []
                   for image in image_inputs:
                         lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  self.get_expected_values([image]       )
                         expected_values.append((expected_height, expected_width)       )
                   lowerCamelCase__		:				List[Any]    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[0]       )[0]
                   lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[1]       )[1]

             return expected_height, expected_width







@require_torch
@require_vision
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			ConditionalDetrImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  ConditionalDetrImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_mean"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_std"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_resize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "size"       )       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size					,      {"shortest_edge": 18, "longest_edge": 1333}       )
             self.assertEqual(image_processor.do_pad					,      __lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  self.image_processing_class.from_dict(
                 self.image_processor_dict					,      size=42					,      max_size=84					,      pad_and_return_pixel_mask=__lowerCamelCase       )
             self.assertEqual(image_processor.size					,      {"shortest_edge": 42, "longest_edge": 84}       )
             self.assertEqual(image_processor.do_pad					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				Dict    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      Image.Image       )

             # Test not batched input
             lowerCamelCase__		:				Optional[Any]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             lowerCamelCase__		:				List[str]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      np.ndarray       )

             # Test not batched input
             lowerCamelCase__		:				List[str]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__		:				int    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				str    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      torch.Tensor       )

             # Test not batched input
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__		:				List[str]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       @slow
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Tuple    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"image_id": 39769, "annotations": target}

             # encode them
             lowerCamelCase__		:				int    =  ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"       )
             lowerCamelCase__		:				List[str]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				Union[str, Any]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				int    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				int    =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				str    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				int    =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				str    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				Any    =  torch.tensor([75, 75, 63, 65, 17, 17]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify orig_size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )





       @slow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Dict    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             lowerCamelCase__		:				Dict    =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

             # encode them
             lowerCamelCase__		:				Union[str, Any]    =  ConditionalDetrImageProcessor(format="coco_panoptic"       )
             lowerCamelCase__		:				List[Any]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      masks_path=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				str    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				str    =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Any    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Tuple    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Any    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				List[Any]    =  torch.tensor([17, 17, 63, 75, 75, 93]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify masks
             lowerCamelCase__		:				Optional[int]    =  822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,      __lowerCamelCase       )
             # verify orig_size
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )






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


