'''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,
)


_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				"\\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"

_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				"\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"

_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				"\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 				_snake_case	(			datasets.Metric							):




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										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      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__=1.0							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   {p['''id''']: p['''no_answer_probability'''] for p in predictions}
										snake_case_			      =   [{'''paragraphs''': [{'''qas''': references}]}]
										snake_case_			      =   {p['''id''']: p['''prediction_text'''] for p in predictions}

										snake_case_			      =   make_qid_to_has_ans(__UpperCamelCase							)  # maps qid to True/False
										snake_case_			      =   [k for k, v in qid_to_has_ans.items() if v]
										snake_case_			      =   [k for k, v in qid_to_has_ans.items() if not v]

										snake_case_			      =   get_raw_scores(__UpperCamelCase       ,							__UpperCamelCase							)
										snake_case_			      =   apply_no_ans_threshold(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase							)
										snake_case_			      =   apply_no_ans_threshold(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase							)
										snake_case_			      =   make_eval_dict(__UpperCamelCase       ,							__UpperCamelCase							)

										if has_ans_qids:
																	snake_case_			      =   make_eval_dict(__UpperCamelCase       ,							__UpperCamelCase       ,							qid_list=__UpperCamelCase							)
																	merge_eval(__UpperCamelCase       ,							__UpperCamelCase       ,							"HasAns"							)
										if no_ans_qids:
																	snake_case_			      =   make_eval_dict(__UpperCamelCase       ,							__UpperCamelCase       ,							qid_list=__UpperCamelCase							)
																	merge_eval(__UpperCamelCase       ,							__UpperCamelCase       ,							"NoAns"							)
										find_all_best_thresh(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase							)
										return dict(__UpperCamelCase							)










'''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 ViltImageProcessor




class 				_snake_case	(			unittest.TestCase							):




			def __init__(      self       ,							a__       ,							a__=7       ,							a__=3       ,							a__=18       ,							a__=30       ,							a__=400       ,							a__=True       ,							a__=None       ,							a__=2       ,							a__=True       ,							a__=[0.5, 0.5, 0.5]       ,							a__=[0.5, 0.5, 0.5]       ,							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   size if size is not None else {"shortest_edge": 30}
										snake_case_			      =   parent
										snake_case_			      =   batch_size
										snake_case_			      =   num_channels
										snake_case_			      =   image_size
										snake_case_			      =   min_resolution
										snake_case_			      =   max_resolution
										snake_case_			      =   do_resize
										snake_case_			      =   size
										snake_case_			      =   size_divisor
										snake_case_			      =   do_normalize
										snake_case_			      =   image_mean
										snake_case_			      =   image_std




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "size_divisor": self.size_divisor,
										}




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=False							)       ->       str:

										'''simple docstring'''




										if not batched:
																	snake_case_			      =   self.size["shortest_edge"]
																	snake_case_			      =   image_inputs[0]
																	if isinstance(a__       ,							Image.Image							):
																								snake_case_      ,   snake_case_			      =   image.size
																	else:
																								snake_case_      ,   snake_case_			      =   image.shape[1], image.shape[2]
																	snake_case_			      =   size / min(a__       ,							a__							)
																	if h < w:
																								snake_case_      ,   snake_case_			      =   size, scale * w
																	else:
																								snake_case_      ,   snake_case_			      =   scale * h, size

																	snake_case_			      =   int((1_333 / 800) * size							)
																	if max(a__       ,							a__							) > max_size:
																								snake_case_			      =   max_size / max(a__       ,							a__							)
																								snake_case_			      =   newh * scale
																								snake_case_			      =   neww * scale

																	snake_case_      ,   snake_case_			      =   int(newh + 0.5							), int(neww + 0.5							)
																	snake_case_      ,   snake_case_			      =   (
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

										else:
																	snake_case_			      =   []
																	for image in image_inputs:
																								snake_case_      ,   snake_case_			      =   self.get_expected_values([image]							)
																								expected_values.append((expected_height, expected_width)							)
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[0]							)[0]
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[1]							)[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 				_snake_case	(			lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					ViltImageProcessor if is_vision_available() else None




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   ViltImageProcessingTester(self							)




			@property
			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(a__       ,							"image_mean"							)							)
										self.assertTrue(hasattr(a__       ,							"image_std"							)							)
										self.assertTrue(hasattr(a__       ,							"do_normalize"							)							)
										self.assertTrue(hasattr(a__       ,							"do_resize"							)							)
										self.assertTrue(hasattr(a__       ,							"size"							)							)
										self.assertTrue(hasattr(a__       ,							"size_divisor"							)							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 30}							)

										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42}							)




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

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							Image.Image							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_			      =   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
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										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'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_			      =   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
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




