
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   :					Tuple    =			'\\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   :					List[str]    =			'\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   :					List[str]    =			'\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 							lowercase   (						datasets.Metric  ):



		def __UpperCamelCase					(					self			)    ->   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.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							, A_							, A_							, A_=1.0			)    ->   Dict:
				"""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(__snake_case			)  # 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            =	get_raw_scores(__snake_case							, __snake_case			)
				UpperCamelCase            =	apply_no_ans_threshold(__snake_case							, __snake_case							, __snake_case							, __snake_case			)
				UpperCamelCase            =	apply_no_ans_threshold(__snake_case							, __snake_case							, __snake_case							, __snake_case			)
				UpperCamelCase            =	make_eval_dict(__snake_case							, __snake_case			)

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







'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





