






"""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,
)


_UpperCamelCase		:  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'

_UpperCamelCase		:  Tuple						=		'\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'

_UpperCamelCase		:  int						=		'\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       UpperCamelCase_				(      self	):
      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       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=1.0	):
      lowercase							   =	{p['id']: p['no_answer_probability'] for p in predictions}
      lowercase							   =	[{'paragraphs': [{'qas': references}]}]
      lowercase							   =	{p['id']: p['prediction_text'] for p in predictions}

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

      lowercase   ,					lowercase							   =	get_raw_scores(_lowerCamelCase							,		_lowerCamelCase	)
      lowercase							   =	apply_no_ans_threshold(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)
      lowercase							   =	apply_no_ans_threshold(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)
      lowercase							   =	make_eval_dict(_lowerCamelCase							,		_lowerCamelCase	)

      if has_ans_qids:
        lowercase							   =	make_eval_dict(_lowerCamelCase							,		_lowerCamelCase							,		qid_list=_lowerCamelCase	)
        merge_eval(_lowerCamelCase							,		_lowerCamelCase							,		'HasAns'	)
      if no_ans_qids:
        lowercase							   =	make_eval_dict(_lowerCamelCase							,		_lowerCamelCase							,		qid_list=_lowerCamelCase	)
        merge_eval(_lowerCamelCase							,		_lowerCamelCase							,		'NoAns'	)
      find_all_best_thresh(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)
      return dict(_lowerCamelCase	)










"""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 ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	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       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image	)

      # Test not batched input
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	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
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	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
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


