

'''simple docstring'''



import datasets

from .evaluate import evaluate


__snake_case			:     List[str]										=      '\\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'

__snake_case			:     Tuple										=      '\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'

__snake_case			:     Tuple										=      '\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 lowercase__						(				self	:						Optional[Any]			)      ->		List[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 lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						List[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Union[str, Any]     ={prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
									A__					:       Optional[int]     =[
									    {
									        """paragraphs""": [
									            {
									                """qas""": [
									                    {
									                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
									                        """id""": ref["""id"""],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									A__					:       Tuple     =evaluate(dataset=lowerCAmelCase_		,      predictions=lowerCAmelCase_			)
									return score




'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[int]			)      ->		int:





									'''simple docstring'''
									A__					:       int     =tempfile.mkdtemp()

									# fmt: off
									A__					:       Optional[int]     =["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
									# fmt: on
									A__					:       List[Any]     =dict(zip(lowerCAmelCase_		,      range(len(lowerCAmelCase_			)			)			)			)
									A__					:       Tuple     =["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
									A__					:       int     ={"""unk_token""": """<unk>"""}

									A__					:       Optional[Any]     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]			)
									A__					:       int     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]			)
									with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write(json.dumps(lowerCAmelCase_			) + """\n"""			)
									with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write("""\n""".join(lowerCAmelCase_			)			)

									A__					:       Dict     ={
									    """do_resize""": True,
									    """size""": 20,
									    """do_center_crop""": True,
									    """crop_size""": 18,
									    """do_normalize""": True,
									    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
									    """image_std""": [0.26862954, 0.26130258, 0.27577711],
									}
									A__					:       Dict     =os.path.join(self.tmpdirname		,      lowerCAmelCase_			)
									with open(self.image_processor_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															json.dump(lowerCAmelCase_		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		int:





									'''simple docstring'''
									return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]		,      **lowerCAmelCase_	:						str			)      ->		Union[str, Any]:





									'''simple docstring'''
									return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname			)


			def lowercase__						(				self	:						List[str]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Dict     =[np.random.randint(2_55		,      size=(3, 30, 4_00)		,      dtype=np.uinta			)]

									A__					:       int     =[Image.fromarray(np.moveaxis(lowerCAmelCase_		,      0		,      -1			)			) for x in image_inputs]

									return image_inputs


			def lowercase__						(				self	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =self.get_tokenizer()
									A__					:       Optional[int]     =self.get_rust_tokenizer()
									A__					:       Any     =self.get_image_processor()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_slow.save_pretrained(self.tmpdirname			)
									A__					:       Dict     =CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase_			)

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_fast.save_pretrained(self.tmpdirname			)
									A__					:       Optional[int]     =CLIPSegProcessor.from_pretrained(self.tmpdirname			)

									self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()			)
									self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase_			)

									self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						str			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[str]     =CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()			)
									processor.save_pretrained(self.tmpdirname			)

									A__					:       List[Any]     =self.get_tokenizer(bos_token="""(BOS)"""		,      eos_token="""(EOS)"""			)
									A__					:       Union[str, Any]     =self.get_image_processor(do_normalize=lowerCAmelCase_		,      padding_value=1.0			)

									A__					:       Optional[int]     =CLIPSegProcessor.from_pretrained(
									    self.tmpdirname		,      bos_token="""(BOS)"""		,      eos_token="""(EOS)"""		,      do_normalize=lowerCAmelCase_		,      padding_value=1.0			)

									self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()			)
									self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase_			)

									self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()			)
									self.assertIsInstance(processor.image_processor		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						str			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       int     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Dict     =self.prepare_image_inputs()

									A__					:       List[Any]     =image_processor(lowerCAmelCase_		,      return_tensors="""np"""			)
									A__					:       List[Any]     =processor(images=lowerCAmelCase_		,      return_tensors="""np"""			)

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1e-2			)


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Any     ="""lower newer"""

									A__					:       Optional[int]     =processor(text=lowerCAmelCase_			)

									A__					:       Optional[int]     =tokenizer(lowerCAmelCase_			)

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]			)


			def lowercase__						(				self	:						List[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       Union[str, Any]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Optional[Any]     ="""lower newer"""
									A__					:       List[str]     =self.prepare_image_inputs()

									A__					:       Optional[Any]     =processor(text=lowerCAmelCase_		,      images=lowerCAmelCase_			)

									self.assertListEqual(list(inputs.keys()			)		,      ["""input_ids""", """attention_mask""", """pixel_values"""]			)

									# test if it raises when no input is passed
									with pytest.raises(lowerCAmelCase_			):
															processor()


			def lowercase__						(				self	:						Any			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       List[str]     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Tuple     =self.prepare_image_inputs()
									A__					:       str     =self.prepare_image_inputs()

									A__					:       int     =processor(images=lowerCAmelCase_		,      visual_prompt=lowerCAmelCase_			)

									self.assertListEqual(list(inputs.keys()			)		,      ["""pixel_values""", """conditional_pixel_values"""]			)

									# test if it raises when no input is passed
									with pytest.raises(lowerCAmelCase_			):
															processor()



			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									A__					:       List[str]     =self.get_image_processor()
									A__					:       Optional[int]     =self.get_tokenizer()

									A__					:       Any     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       List[Any]     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A__					:       Tuple     =processor.batch_decode(lowerCAmelCase_			)
									A__					:       List[Any]     =tokenizer.batch_decode(lowerCAmelCase_			)

									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

