

'''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,
)


UpperCAmelCase							=					'''\
@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}
}
'''

UpperCAmelCase							=					'''
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.
'''

UpperCAmelCase							=					'''
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 					lowerCAmelCase       ( datasets.Metric				):







       def      snake_case				(							self     :	int ):







            """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     :	Union[str, Any]  , __lowercase     :	str  , __lowercase     :	List[str]  , __lowercase     :	str=1.0 ):







            """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(__lowercase )  # 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(__lowercase  , __lowercase )
            __lowercase     						=apply_no_ans_threshold(__lowercase  , __lowercase  , __lowercase  , __lowercase )
            __lowercase     						=apply_no_ans_threshold(__lowercase  , __lowercase  , __lowercase  , __lowercase )
            __lowercase     						=make_eval_dict(__lowercase  , __lowercase )

            if has_ans_qids:
                 __lowercase     						=make_eval_dict(__lowercase  , __lowercase  , qid_list=__lowercase )
                 merge_eval(__lowercase  , __lowercase  , 'HasAns' )
            if no_ans_qids:
                 __lowercase     						=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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







       def __init__(							self     :	List[Any]  , __lowercase     :	bool = True  , __lowercase     :	Optional[Dict[str, int]] = None  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __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     :	List[Any]  , ):







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            return rescale(__lowercase  , scale=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







            return normalize(__lowercase  , mean=__lowercase  , std=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=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_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

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

            if do_resize:
                 __lowercase     						=[self.resize(image=__lowercase  , size=__lowercase  , resample=__lowercase ) for image in images]

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

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

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

            __lowercase     						={'pixel_values': images}
            return BatchFeature(data=__lowercase  , tensor_type=__lowercase )


