'''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		:					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"

_lowercase		:					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"

_lowercase		:					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        __magic_name__		(       datasets.Metric):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										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							:    int							,     lowercase_							:    Any							,     lowercase_							:    Optional[int]							,     lowercase_							:    List[Any]=1.0       ):
										lowercase_   :     int									=			{p["""id"""]: p["""no_answer_probability"""] for p in predictions}
										lowercase_   :     str									=			[{"""paragraphs""": [{"""qas""": references}]}]
										lowercase_   :     List[Any]									=			{p["""id"""]: p["""prediction_text"""] for p in predictions}

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

										lowercase_		,		lowercase_   :     str									=			get_raw_scores(lowercase_							,     lowercase_       )
										lowercase_   :     int									=			apply_no_ans_threshold(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )
										lowercase_   :     Optional[Any]									=			apply_no_ans_threshold(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )
										lowercase_   :     Optional[int]									=			make_eval_dict(lowercase_							,     lowercase_       )

										if has_ans_qids:
																	lowercase_   :     Tuple									=			make_eval_dict(lowercase_							,     lowercase_							,     qid_list=lowercase_       )
																	merge_eval(lowercase_							,     lowercase_							,     """HasAns"""       )
										if no_ans_qids:
																	lowercase_   :     int									=			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_       )






'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase		:					Optional[int]													=		logging.get_logger(__name__)


if is_vision_available():
						import PIL





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







			def __init__(      self							:    Any							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    bool = True							,     lowercase_							:    Union[int, float] = 1 / 255							,     lowercase_							:    bool = True							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = True							,     **lowercase_							:    Optional[Any]							,     ):
										super().__init__(**lowercase_       )
										lowercase_   :     List[Any]									=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_       )

										lowercase_   :     Tuple									=			do_resize
										lowercase_   :     int									=			size
										lowercase_   :     List[Any]									=			resample
										lowercase_   :     str									=			do_center_crop
										lowercase_   :     Optional[int]									=			crop_size
										lowercase_   :     str									=			do_rescale
										lowercase_   :     List[Any]									=			rescale_factor
										lowercase_   :     Dict									=			do_normalize
										lowercase_   :     List[str]									=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowercase_   :     Any									=			image_std if image_std is not None else OPENAI_CLIP_STD
										lowercase_   :     List[str]									=			do_convert_rgb







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Dict							,     ):
										lowercase_   :     Tuple									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			get_resize_output_image_size(
										    lowercase_							,     size=(size["""height"""], size["""width"""])							,     default_to_square=lowercase_       )
										return resize(lowercase_							,     size=lowercase_							,     resample=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Any							,     ):
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										return center_crop(lowercase_							,     size=(size["""height"""], size["""width"""])							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[int, float]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    List[Any]							,     ):
										return rescale(lowercase_							,     scale=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Optional[Any]							,     ):
										return normalize(lowercase_							,     mean=lowercase_							,     std=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    ImageInput							,     lowercase_							:    bool = None							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = None							,     lowercase_							:    bool = None							,     lowercase_							:    int = None							,     lowercase_							:    bool = None							,     lowercase_							:    float = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[str, TensorType]] = None							,     lowercase_							:    Optional[ChannelDimension] = ChannelDimension.FIRST							,     **lowercase_							:    Optional[int]							,     ):
										lowercase_   :     Optional[Any]									=			do_resize if do_resize is not None else self.do_resize
										lowercase_   :     Union[str, Any]									=			size if size is not None else self.size
										lowercase_   :     Optional[int]									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     int									=			resample if resample is not None else self.resample
										lowercase_   :     str									=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_   :     int									=			crop_size if crop_size is not None else self.crop_size
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										lowercase_   :     Dict									=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_   :     List[str]									=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_   :     Optional[Any]									=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_   :     List[Any]									=			image_mean if image_mean is not None else self.image_mean
										lowercase_   :     str									=			image_std if image_std is not None else self.image_std
										lowercase_   :     Any									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowercase_   :     int									=			make_list_of_images(lowercase_       )

										if not valid_images(lowercase_       ):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

										if do_resize and size is None:
																	raise ValueError("""Size must be specified if do_resize is True."""       )

										if do_center_crop and crop_size is None:
																	raise ValueError("""Crop size must be specified if do_center_crop is True."""       )

										if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

										if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																	lowercase_   :     Dict									=			[convert_to_rgb(lowercase_       ) for image in images]

										# All transformations expect numpy arrays.
										lowercase_   :     Any									=			[to_numpy_array(lowercase_       ) for image in images]

										if do_resize:
																	lowercase_   :     Union[str, Any]									=			[self.resize(image=lowercase_							,     size=lowercase_							,     resample=lowercase_       ) for image in images]

										if do_center_crop:
																	lowercase_   :     List[str]									=			[self.center_crop(image=lowercase_							,     size=lowercase_       ) for image in images]

										if do_rescale:
																	lowercase_   :     int									=			[self.rescale(image=lowercase_							,     scale=lowercase_       ) for image in images]

										if do_normalize:
																	lowercase_   :     List[Any]									=			[self.normalize(image=lowercase_							,     mean=lowercase_							,     std=lowercase_       ) for image in images]

										lowercase_   :     List[Any]									=			[to_channel_dimension_format(lowercase_							,     lowercase_       ) for image in images]

										lowercase_   :     Any									=			{"""pixel_values""": images}
										return BatchFeature(data=lowercase_							,     tensor_type=lowercase_       )





