



"""simple docstring"""







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


A    =	'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

A    =	'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

A    =	'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\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 list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'

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



			def 							__lowercase				( self       :							Any		)       ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
									        {
									            "predictions": {
									                "id": datasets.Value("string"		),
									                "prediction_text": datasets.Value("string"		),
									                "no_answer_probability": datasets.Value("float32"		),
									            },
									            "references": {
									                "id": datasets.Value("string"		),
									                "answers": datasets.features.Sequence(
									                    {"text": datasets.Value("string"		), "answer_start": datasets.Value("int32"		)}		),
									            },
									        }		)      ,  codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]      ,  reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]      ,  )



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							str      ,  _lowercase       :							Tuple=1.0		)       ->				Optional[Any]:
									snake_case      : Any							=   {p["id"]: p["no_answer_probability"] for p in predictions}
									snake_case      : Any							=   [{"paragraphs": [{"qas": references}]}]
									snake_case      : Tuple							=   {p["id"]: p["prediction_text"] for p in predictions}

									snake_case      : List[str]							=   make_qid_to_has_ans(_lowercase		)  # maps qid to True/False
									snake_case      : List[str]							=   [k for k, v in qid_to_has_ans.items() if v]
									snake_case      : Tuple							=   [k for k, v in qid_to_has_ans.items() if not v]

									snake_case						, snake_case      : Optional[Any]							=   get_raw_scores(_lowercase      ,  _lowercase		)
									snake_case      : Any							=   apply_no_ans_threshold(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									snake_case      : List[Any]							=   apply_no_ans_threshold(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									snake_case      : Any							=   make_eval_dict(_lowercase      ,  _lowercase		)

									if has_ans_qids:
															snake_case      : Union[str, Any]							=   make_eval_dict(_lowercase      ,  _lowercase      ,  qid_list=_lowercase		)
															merge_eval(_lowercase      ,  _lowercase      ,  "HasAns"		)
									if no_ans_qids:
															snake_case      : List[str]							=   make_eval_dict(_lowercase      ,  _lowercase      ,  qid_list=_lowercase		)
															merge_eval(_lowercase      ,  _lowercase      ,  "NoAns"		)
									find_all_best_thresh(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									return dict(_lowercase		)








"""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 OwlViTImageProcessor

class 			_a     (   unittest.TestCase):



			def __init__( self       :							Optional[int]      ,  _lowercase       :							int      ,  _lowercase       :							Union[str, Any]=7      ,  _lowercase       :							Union[str, Any]=3      ,  _lowercase       :							int=18      ,  _lowercase       :							Any=30      ,  _lowercase       :							str=400      ,  _lowercase       :							str=True      ,  _lowercase       :							Dict=None      ,  _lowercase       :							int=True      ,  _lowercase       :							Union[str, Any]=None      ,  _lowercase       :							str=True      ,  _lowercase       :							Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]      ,  _lowercase       :							str=[0.26862954, 0.26130258, 0.27577711]      ,  _lowercase       :							Optional[int]=True      ,  )       ->				Optional[int]:
									snake_case      : str							=   parent
									snake_case      : Any							=   batch_size
									snake_case      : Optional[int]							=   num_channels
									snake_case      : Any							=   image_size
									snake_case      : Optional[Any]							=   min_resolution
									snake_case      : int							=   max_resolution
									snake_case      : List[Any]							=   do_resize
									snake_case      : Dict							=   size if size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_center_crop
									snake_case      : List[str]							=   crop_size if crop_size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_normalize
									snake_case      : str							=   image_mean
									snake_case      : Optional[Any]							=   image_std
									snake_case      : str							=   do_convert_rgb



			def 							__lowercase				( self       :							Tuple		)       ->				Union[str, Any]:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									}



@require_torch
@require_vision
class 			_a     (   SCREAMING_SNAKE_CASE__    ,  unittest.TestCase):
			__magic_name__    				=    OwlViTImageProcessor if is_vision_available() else None



			def 							__lowercase				( self       :							Any		)       ->				List[Any]:
									snake_case      : Dict							=   OwlViTImageProcessingTester(self		)



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



			def 							__lowercase				( self       :							List[Any]		)       ->				Union[str, Any]:
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_lowercase      ,  "do_resize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "size"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_normalize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_mean"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_std"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_convert_rgb"		)		)



			def 							__lowercase				( self       :							Any		)       ->				Any:
									snake_case      : List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size      ,  {"height": 18, "width": 18}		)
									self.assertEqual(image_processor.crop_size      ,  {"height": 18, "width": 18}		)

									snake_case      : int							=   self.image_processing_class.from_dict(self.image_processor_dict      ,  size=42      ,  crop_size=84		)
									self.assertEqual(image_processor.size      ,  {"height": 42, "width": 42}		)
									self.assertEqual(image_processor.crop_size      ,  {"height": 84, "width": 84}		)



			def 							__lowercase				( self       :							Any		)       ->				Optional[int]:
									# Initialize image_processing
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									snake_case      : Tuple							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase		)

									for image in image_inputs:
															self.assertIsInstance(_lowercase      ,  Image.Image		)

									# Test not batched input
									snake_case      : Dict							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : Optional[Any]							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Dict:
									# Initialize image_processing
									snake_case      : Optional[int]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									snake_case      : int							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase      ,  numpify=_lowercase		)
									for image in image_inputs:
															self.assertIsInstance(_lowercase      ,  np.ndarray		)

									# Test not batched input
									snake_case      : str							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : str							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									# Initialize image_processing
									snake_case      : Any							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									snake_case      : Dict							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase      ,  torchify=_lowercase		)
									for image in image_inputs:
															self.assertIsInstance(_lowercase      ,  torch.Tensor		)

									# Test not batched input
									snake_case      : Union[str, Any]							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : Any							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )



