
"""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 DeiTImageProcessor

class __lowerCAmelCase						( unittest.TestCase     ):





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=7      ,	__UpperCAmelCase=3      ,	__UpperCAmelCase=18      ,	__UpperCAmelCase=30      ,	__UpperCAmelCase=400      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=[0.5, 0.5, 0.5]      ,	__UpperCAmelCase=[0.5, 0.5, 0.5]      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			size if size is not None else {'height': 20, 'width': 20}
      __UpperCamelCase							    =			crop_size if crop_size is not None else {'height': 18, 'width': 18}

      __UpperCamelCase							    =			parent
      __UpperCamelCase							    =			batch_size
      __UpperCamelCase							    =			num_channels
      __UpperCamelCase							    =			image_size
      __UpperCamelCase							    =			min_resolution
      __UpperCamelCase							    =			max_resolution
      __UpperCamelCase							    =			do_resize
      __UpperCamelCase							    =			size
      __UpperCamelCase							    =			do_center_crop
      __UpperCamelCase							    =			crop_size
      __UpperCamelCase							    =			do_normalize
      __UpperCamelCase							    =			image_mean
      __UpperCamelCase							    =			image_std







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      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,
      }


@require_torch
@require_vision
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		DeiTImageProcessor if is_vision_available() else None
     lowercase		  =		True





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			DeiTImageProcessingTester(self )





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'do_resize' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'size' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'do_center_crop' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'center_crop' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'do_normalize' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'image_mean' ) )
      self.assertTrue(hasattr(__UpperCAmelCase      ,	'image_std' ) )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size      ,	{'height': 20, 'width': 20} )
      self.assertEqual(image_processor.crop_size      ,	{'height': 18, 'width': 18} )

      __UpperCamelCase							    =			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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase							    =			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
      __UpperCamelCase							    =			image_processing(__UpperCAmelCase      ,	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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      __UpperCamelCase							    =			prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=__UpperCAmelCase      ,	numpify=__UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase      ,	np.ndarray )

      # Test not batched input
      __UpperCamelCase							    =			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
      __UpperCamelCase							    =			image_processing(__UpperCAmelCase      ,	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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase							    =			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
      __UpperCamelCase							    =			image_processing(__UpperCAmelCase      ,	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'],
          )      ,	)





"""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      :							List[str]          =					"\\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      :							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"

UpperCamelCase      :							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 __lowerCAmelCase						( datasets.Metric     ):





     def 		UpperCAmelCase   (      self ):

      '''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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=1.0 ):

      '''simple docstring'''




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

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

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

      if has_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'HasAns' )
      if no_ans_qids:
       __UpperCamelCase							    =			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 )



