

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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE					:  List[Any]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["pixel_values"]






  def __init__(							self						:		str     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		int = 0.9     ,       __lowerCamelCase						:		PILImageResampling = PILImageResampling.BICUBIC     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		Union[int, float] = 1 / 255     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       **__lowerCamelCase						:		Tuple     ,       )		->							None:
       super().__init__(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   size if size is not None else {'''shortest_edge''': 224}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   do_resize
       SCREAMING_SNAKE_CASE__   =   size
       SCREAMING_SNAKE_CASE__   =   crop_pct
       SCREAMING_SNAKE_CASE__   =   resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop
       SCREAMING_SNAKE_CASE__   =   crop_size
       SCREAMING_SNAKE_CASE__   =   do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Dict[str, int]     ,       __lowerCamelCase						:		Optional[float] = None     ,       __lowerCamelCase						:		PILImageResampling = PILImageResampling.BICUBIC     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		str     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
            raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
       if crop_pct is not None:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__   =   int(size['''shortest_edge'''] / crop_pct )
            elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                      SCREAMING_SNAKE_CASE__   =   int(size['''height'''] / crop_pct )
                 else:
                      SCREAMING_SNAKE_CASE__   =   (int(size['''height'''] / crop_pct ), int(size['''width'''] / crop_pct ))
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(__lowerCamelCase ) )

            SCREAMING_SNAKE_CASE__   =   get_resize_output_image_size(__lowerCamelCase     ,       size=__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       else:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__   =   get_resize_output_image_size(__lowerCamelCase     ,       size=size['''shortest_edge''']     ,       default_to_square=__lowerCamelCase )
            elif "height" in size and "width" in size:
                 SCREAMING_SNAKE_CASE__   =   (size['''height'''], size['''width'''])
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(__lowerCamelCase ) )

       return resize(__lowerCamelCase     ,       size=__lowerCamelCase     ,       resample=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Dict[str, int]     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		str     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''' )
       return center_crop(__lowerCamelCase     ,       size=(size['''height'''], size['''width'''])     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Union[int, float]     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		Optional[int]     ,       )		->							str:
       return rescale(__lowerCamelCase     ,       scale=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






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







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		ImageInput     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		int = None     ,       __lowerCamelCase						:		PILImageResampling = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		float = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		ChannelDimension = ChannelDimension.FIRST     ,       **__lowerCamelCase						:		List[str]     ,       )		->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__   =   do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__   =   crop_pct if crop_pct is not None else self.crop_pct
       SCREAMING_SNAKE_CASE__   =   resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__   =   do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__   =   size if size is not None else self.size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   make_list_of_images(__lowerCamelCase )

       if not valid_images(__lowerCamelCase ):
            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 or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_center_crop and crop_pct is None:
            raise ValueError('''Crop_pct 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.''' )

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

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

       if do_center_crop:
            SCREAMING_SNAKE_CASE__   =   [self.center_crop(image=__lowerCamelCase     ,       size=__lowerCamelCase ) for image in images]

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

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

       SCREAMING_SNAKE_CASE__   =   [to_channel_dimension_format(__lowerCamelCase     ,       __lowerCamelCase ) for image in images]

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





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@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}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 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.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






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






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''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 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


