






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 CLIPImageProcessor
class 							A_      (  unittest.TestCase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A=7							,					_A=3							,					_A=18							,					_A=30							,					_A=400							,					_A=True							,					_A=None							,					_A=True							,					_A=None							,					_A=True							,					_A=[0.48145466, 0.4578275, 0.40821073]							,					_A=[0.26862954, 0.26130258, 0.27577711]							,					_A=True							,					)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			size if size is not None else {'''shortest_edge''': 20}
     _UpperCAmelCase			: Optional[int]     					=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     _UpperCAmelCase			: Union[str, Any]     					=			parent
     _UpperCAmelCase			: str     					=			batch_size
     _UpperCAmelCase			: Optional[int]     					=			num_channels
     _UpperCAmelCase			: Any     					=			image_size
     _UpperCAmelCase			: Tuple     					=			min_resolution
     _UpperCAmelCase			: Optional[Any]     					=			max_resolution
     _UpperCAmelCase			: List[Any]     					=			do_resize
     _UpperCAmelCase			: Any     					=			size
     _UpperCAmelCase			: Optional[int]     					=			do_center_crop
     _UpperCAmelCase			: Tuple     					=			crop_size
     _UpperCAmelCase			: int     					=			do_normalize
     _UpperCAmelCase			: Any     					=			image_mean
     _UpperCAmelCase			: Optional[Any]     					=			image_std
     _UpperCAmelCase			: int     					=			do_convert_rgb


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

     """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,
         "do_convert_rgb": self.do_convert_rgb,
     }


 def 	snake_case__			(		self							,					_A=False							,					_A=False							,					_A=False)	->						Dict:

     """simple docstring"""



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

     if equal_resolution:
         _UpperCAmelCase			: List[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:
         _UpperCAmelCase			: Optional[int]     					=			[]
         for i in range(self.batch_size):
             _UpperCAmelCase			: 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
         _UpperCAmelCase			: Optional[int]     					=			[Image.fromarray(np.moveaxis(_A							,					0							,					-1)) for x in image_inputs]

     if torchify:
         _UpperCAmelCase			: Union[str, Any]     					=			[torch.from_numpy(_A) for x in image_inputs]

     return image_inputs



@require_torch
@require_vision
class 							A_      (  __lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   CLIPImageProcessor if is_vision_available() else None


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

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			CLIPImageProcessingTester(self)


 @property
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(_A							,					'''do_resize'''))
     self.assertTrue(hasattr(_A							,					'''size'''))
     self.assertTrue(hasattr(_A							,					'''do_center_crop'''))
     self.assertTrue(hasattr(_A							,					'''center_crop'''))
     self.assertTrue(hasattr(_A							,					'''do_normalize'''))
     self.assertTrue(hasattr(_A							,					'''image_mean'''))
     self.assertTrue(hasattr(_A							,					'''image_std'''))
     self.assertTrue(hasattr(_A							,					'''do_convert_rgb'''))


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

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size							,					{'''shortest_edge''': 20})
     self.assertEqual(image_processor.crop_size							,					{'''height''': 18, '''width''': 18})

     _UpperCAmelCase			: List[str]     					=			self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84)
     self.assertEqual(image_processor.size							,					{'''shortest_edge''': 42})
     self.assertEqual(image_processor.crop_size							,					{'''height''': 84, '''width''': 84})


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase			: int     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					Image.Image)

     # Test not batched input
     _UpperCAmelCase			: int     					=			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			: str     					=			image_processing(_A							,					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 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



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

     # Test not batched input
     _UpperCAmelCase			: Union[str, Any]     					=			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			: Tuple     					=			image_processing(_A							,					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 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



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

     # Test not batched input
     _UpperCAmelCase			: Dict     					=			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			: Union[str, Any]     					=			image_processing(_A							,					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'''],
         )							,					)



@require_torch
@require_vision
class 							A_      (  __lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     int	=   CLIPImageProcessor if is_vision_available() else None


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			CLIPImageProcessingTester(self							,					num_channels=4)
     _UpperCAmelCase			: int     					=			3


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

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(_A							,					'''do_resize'''))
     self.assertTrue(hasattr(_A							,					'''size'''))
     self.assertTrue(hasattr(_A							,					'''do_center_crop'''))
     self.assertTrue(hasattr(_A							,					'''center_crop'''))
     self.assertTrue(hasattr(_A							,					'''do_normalize'''))
     self.assertTrue(hasattr(_A							,					'''image_mean'''))
     self.assertTrue(hasattr(_A							,					'''image_std'''))
     self.assertTrue(hasattr(_A							,					'''do_convert_rgb'''))


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

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase			: List[Any]     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					Image.Image)

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

     # Test batched
     _UpperCAmelCase			: Optional[int]     					=			image_processing(_A							,					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.crop_size['''height'''],
             self.image_processor_tester.crop_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,
)


SCREAMING_SNAKE_CASE								=  '\\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'

SCREAMING_SNAKE_CASE								=  '\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'

SCREAMING_SNAKE_CASE								=  '\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     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						List[str]:

     """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 	snake_case__			(		self							,					_A							,					_A							,					_A=1.0)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			{p['''id''']: p['''no_answer_probability'''] for p in predictions}
     _UpperCAmelCase			: List[str]     					=			[{'''paragraphs''': [{'''qas''': references}]}]
     _UpperCAmelCase			: List[Any]     					=			{p['''id''']: p['''prediction_text'''] for p in predictions}

     _UpperCAmelCase			: List[str]     					=			make_qid_to_has_ans(_A)  # maps qid to True/False
     _UpperCAmelCase			: str     					=			[k for k, v in qid_to_has_ans.items() if v]
     _UpperCAmelCase			: List[Any]     					=			[k for k, v in qid_to_has_ans.items() if not v]

     _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			get_raw_scores(_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: List[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			make_eval_dict(_A							,					_A)

     if has_ans_qids:
         _UpperCAmelCase			: Tuple     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''HasAns''')
     if no_ans_qids:
         _UpperCAmelCase			: Any     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''NoAns''')
     find_all_best_thresh(_A							,					_A							,					_A							,					_A							,					_A							,					_A)
     return dict(_A)




