






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_		:		int			= '\\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_		:		Any			= '\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_		:		List[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					):





       """simple docstring"""







       def 			_a    (					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 			_a    (					self  ,			_lowerCamelCase  ,			_lowerCamelCase  ,			_lowerCamelCase=1.0							):
         UpperCamelCase_:					str          =				{p['''id''']: p['''no_answer_probability'''] for p in predictions}
         UpperCamelCase_:					Union[str, Any]          =				[{'''paragraphs''': [{'''qas''': references}]}]
         UpperCamelCase_:					int          =				{p['''id''']: p['''prediction_text'''] for p in predictions}

         UpperCamelCase_:					Optional[int]          =				make_qid_to_has_ans(UpperCamelCase__							)  # maps qid to True/False
         UpperCamelCase_:					Optional[int]          =				[k for k, v in qid_to_has_ans.items() if v]
         UpperCamelCase_:					Dict          =				[k for k, v in qid_to_has_ans.items() if not v]

         UpperCamelCase_:					List[Any]          =				get_raw_scores(UpperCamelCase__  ,			UpperCamelCase__							)
         UpperCamelCase_:					List[Any]          =				apply_no_ans_threshold(UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__							)
         UpperCamelCase_:					List[Any]          =				apply_no_ans_threshold(UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__							)
         UpperCamelCase_:					int          =				make_eval_dict(UpperCamelCase__  ,			UpperCamelCase__							)

         if has_ans_qids:
           UpperCamelCase_:					Any          =				make_eval_dict(UpperCamelCase__  ,			UpperCamelCase__  ,			qid_list=UpperCamelCase__							)
           merge_eval(UpperCamelCase__  ,			UpperCamelCase__  ,			'HasAns'							)
         if no_ans_qids:
           UpperCamelCase_:					Any          =				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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]=7   ,     UpperCamelCase__							:       Union[str, Any]=3   ,     UpperCamelCase__							:       Tuple=18   ,     UpperCamelCase__							:       Union[str, Any]=30   ,     UpperCamelCase__							:       Dict=400   ,     UpperCamelCase__							:       Tuple=True   ,     UpperCamelCase__							:       Optional[Any]=None   ,     UpperCamelCase__							:       Any=True   ,     UpperCamelCase__							:       Dict=False   ,     UpperCamelCase__							:       Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     UpperCamelCase__							:       int=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     UpperCamelCase__							:       str=True   ,     )		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		size if size is not None else {'''height''': 20, '''width''': 20}
				snake_case   :    Union[str, Any]			       =		parent
				snake_case   :    Optional[int]			       =		batch_size
				snake_case   :    Dict			       =		num_channels
				snake_case   :    List[Any]			       =		image_size
				snake_case   :    List[Any]			       =		min_resolution
				snake_case   :    Dict			       =		max_resolution
				snake_case   :    Dict			       =		do_resize
				snake_case   :    Optional[int]			       =		size
				snake_case   :    Dict			       =		do_normalize
				snake_case   :    Tuple			       =		image_mean
				snake_case   :    Optional[int]			       =		image_std
				snake_case   :    int			       =		do_pad
				snake_case   :    List[Any]			       =		do_convert_rgb



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]=False   ,     UpperCamelCase__							:       Tuple=False   ,     UpperCamelCase__							:       Union[str, Any]=False	)		->       List[str]:






				"""simple docstring"""







				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				if equal_resolution:
						snake_case   :    Any			       =		[]
						for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta	)	)
				else:
						snake_case   :    Union[str, Any]			       =		[]
						for i in range(self.batch_size	):
								snake_case       ,snake_case   :    List[Any]			       =		np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution	)   ,     2	)
								image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta	)	)

				if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						snake_case   :    Optional[int]			       =		[Image.fromarray(np.moveaxis(UpperCamelCase__   ,     0   ,     -1	)	) for x in image_inputs]

				if torchify:
						snake_case   :    Optional[Any]			       =		[torch.from_numpy(UpperCamelCase__	) for x in image_inputs]

				return image_inputs







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Dict:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Union[str, Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Optional[int]			       =		image_processor(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
				snake_case   :    int			       =		image_processor(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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Any			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    List[str]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     numpify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     np.ndarray	)

				# Test not batched input
				snake_case   :    Dict			       =		image_processor(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
				snake_case   :    Dict			       =		image_processor(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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    List[Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     torchify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     torch.Tensor	)

				# Test not batched input
				snake_case   :    List[str]			       =		image_processor(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
				snake_case   :    str			       =		image_processor(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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self   ,     num_channels=4	)
				snake_case   :    Optional[int]			       =		3



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Tuple:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



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






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       List[str]:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Tuple			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    int			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Union[str, Any]			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )


