
"""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 				__a    (   unittest.TestCase					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=7     ,						_lowerCamelCase=3     ,						_lowerCamelCase=18     ,						_lowerCamelCase=30     ,						_lowerCamelCase=400     ,						_lowerCamelCase=True     ,						_lowerCamelCase=None     ,						_lowerCamelCase=True     ,						_lowerCamelCase=None     ,						_lowerCamelCase=True     ,						_lowerCamelCase=[0.5, 0.5, 0.5]     ,						_lowerCamelCase=[0.5, 0.5, 0.5]     ,						) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase						      =					size if size is not None else {"height": 20, "width": 20}
										__lowercase						      =					crop_size if crop_size is not None else {"height": 18, "width": 18}

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





							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, 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,
										}





@require_torch
@require_vision
class 				__a    (   __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Optional[Any]					      =      DeiTImageProcessor if is_vision_available() else None
							_lowerCamelCase				: Any					      =      True
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					DeiTImageProcessingTester(self					)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"do_resize"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"size"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"do_center_crop"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"center_crop"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"do_normalize"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"image_mean"					)					)
										self.assertTrue(hasattr(_lowerCamelCase     ,						"image_std"					)					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					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}					)

										__lowercase						      =					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      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										pass
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										# Initialize image_processing
										__lowercase						      =					self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										__lowercase						      =					prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_lowerCamelCase					)
										for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase     ,						Image.Image					)

										# Test not batched input
										__lowercase						      =					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
										__lowercase						      =					image_processing(_lowerCamelCase     ,						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      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										# Initialize image_processing
										__lowercase						      =					self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										__lowercase						      =					prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_lowerCamelCase     ,						numpify=_lowerCamelCase					)
										for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase     ,						np.ndarray					)

										# Test not batched input
										__lowercase						      =					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
										__lowercase						      =					image_processing(_lowerCamelCase     ,						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      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										# Initialize image_processing
										__lowercase						      =					self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										__lowercase						      =					prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_lowerCamelCase     ,						torchify=_lowerCamelCase					)
										for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase     ,						torch.Tensor					)

										# Test not batched input
										__lowercase						      =					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
										__lowercase						      =					image_processing(_lowerCamelCase     ,						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,
)


_lowercase							 =							'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'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}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,      _KWARGS_DESCRIPTION					)
class 				__a    (   datasets.Metric					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

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

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

										if has_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"HasAns"					)
										if no_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"NoAns"					)
										find_all_best_thresh(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										return dict(_lowerCamelCase					)





