



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 							a_  (		unittest.TestCase				):

						def __init__(    self	:Optional[int]   ,						_lowercase	:str   ,						_lowercase	:Tuple=7   ,						_lowercase	:str=3   ,						_lowercase	:Dict=18   ,						_lowercase	:int=30   ,						_lowercase	:int=400   ,						_lowercase	:Dict=True   ,						_lowercase	:int=None   ,						_lowercase	:List[Any]=True   ,						_lowercase	:int=False   ,						_lowercase	:Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,						_lowercase	:Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,						_lowercase	:Optional[Any]=True   ,						)		->					int:
												UpperCAmelCase_     							=     size if size is not None else {'''height''': 20, '''width''': 20}
												UpperCAmelCase_     							=     parent
												UpperCAmelCase_     							=     batch_size
												UpperCAmelCase_     							=     num_channels
												UpperCAmelCase_     							=     image_size
												UpperCAmelCase_     							=     min_resolution
												UpperCAmelCase_     							=     max_resolution
												UpperCAmelCase_     							=     do_resize
												UpperCAmelCase_     							=     size
												UpperCAmelCase_     							=     do_normalize
												UpperCAmelCase_     							=     image_mean
												UpperCAmelCase_     							=     image_std
												UpperCAmelCase_     							=     do_pad
												UpperCAmelCase_     							=     do_convert_rgb

						def 	__a       (    self	:int)		->					Optional[int]:
												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 	__a       (    self	:Tuple   ,						_lowercase	:Dict=False   ,						_lowercase	:Union[str, Any]=False   ,						_lowercase	:Dict=False)		->					int:
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		UpperCAmelCase_     							=     []
																		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:
																		UpperCAmelCase_     							=     []
																		for i in range(self.batch_size):
																								UpperCAmelCase_							,      UpperCAmelCase_     							=     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
																		UpperCAmelCase_     							=     [Image.fromarray(np.moveaxis(_lowercase   ,						0   ,						-1)) for x in image_inputs]

												if torchify:
																		UpperCAmelCase_     							=     [torch.from_numpy(_lowercase) for x in image_inputs]

												return image_inputs







@require_torch
@require_vision
class 							a_  (		_snake_case					, unittest.TestCase				):
						UpperCamelCase__      :						Tuple				       =BlipImageProcessor if is_vision_available() else None

						def 	__a       (    self	:Optional[int])		->					str:
												UpperCAmelCase_     							=     BlipImageProcessingTester(self)

						@property
						def 	__a       (    self	:Optional[Any])		->					Dict:
												return self.image_processor_tester.prepare_image_processor_dict()

						def 	__a       (    self	:Union[str, Any])		->					str:
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(_lowercase   ,						'''do_resize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''size'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_normalize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''image_mean'''))
												self.assertTrue(hasattr(_lowercase   ,						'''image_std'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_convert_rgb'''))

						def 	__a       (    self	:Tuple)		->					int:
												pass

						def 	__a       (    self	:Optional[Any])		->					Union[str, Any]:
												# Initialize image_processor
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												UpperCAmelCase_     							=     self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						Image.Image)

												# Test not batched input
												UpperCAmelCase_     							=     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
												UpperCAmelCase_     							=     image_processor(_lowercase   ,						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 	__a       (    self	:str)		->					int:
												# Initialize image_processor
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												UpperCAmelCase_     							=     self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase   ,						numpify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						np.ndarray)

												# Test not batched input
												UpperCAmelCase_     							=     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
												UpperCAmelCase_     							=     image_processor(_lowercase   ,						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 	__a       (    self	:Optional[Any])		->					str:
												# Initialize image_processor
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												UpperCAmelCase_     							=     self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase   ,						torchify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						torch.Tensor)

												# Test not batched input
												UpperCAmelCase_     							=     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
												UpperCAmelCase_     							=     image_processor(_lowercase   ,						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 							a_  (		_snake_case					, unittest.TestCase				):
						UpperCamelCase__      :						Dict				       =BlipImageProcessor if is_vision_available() else None

						def 	__a       (    self	:Tuple)		->					Dict:
												UpperCAmelCase_     							=     BlipImageProcessingTester(self   ,						num_channels=4)
												UpperCAmelCase_     							=     3

						@property
						def 	__a       (    self	:str)		->					Dict:
												return self.image_processor_tester.prepare_image_processor_dict()

						def 	__a       (    self	:Any)		->					int:
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(_lowercase   ,						'''do_resize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''size'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_normalize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''image_mean'''))
												self.assertTrue(hasattr(_lowercase   ,						'''image_std'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_convert_rgb'''))

						def 	__a       (    self	:Optional[Any])		->					Optional[Any]:
												pass




						def 	__a       (    self	:Dict)		->					str:
												# Initialize image_processor
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												UpperCAmelCase_     							=     self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						Image.Image)

												# Test not batched input
												UpperCAmelCase_     							=     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
												UpperCAmelCase_     							=     image_processor(_lowercase   ,						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'''],
												    )   ,						)










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_	       =   "\\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_	       =   "\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_	       =   "\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 	__a       (    self	:List[Any])		->					List[str]:
												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	:int   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=1.0)		->					str:
												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(_lowercase)  # 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(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase)

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





