





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


if is_vision_available():
 import PIL


__A       : int						    = logging.get_logger(__name__)







class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = ['pixel_values']




     def __init__(						self							,    snake_case_ = True							,    snake_case_ = None							,    snake_case_ = True							,    snake_case_ = None							,    snake_case_ = PILImageResampling.BICUBIC							,    snake_case_ = True							,    snake_case_ = 1 / 255							,    snake_case_ = True							,    snake_case_ = None							,    snake_case_ = None							,    **snake_case_							,    ):
         super().__init__(**snake_case_       )
         _A    =       crop_size if crop_size is not None else {'height': 256, 'width': 256}
         _A    =       get_size_dict(snake_case_							,    param_name='crop_size'       )
         _A    =       size if size is not None else {'height': 224, 'width': 224}
         _A    =       get_size_dict(snake_case_       )

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




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_ = None							,    snake_case_ = None							,    **snake_case_							,    ):
         _A    =       self.size if size is None else size
         _A    =       get_size_dict(snake_case_       )
         _A    =       get_size_dict(snake_case_							,    param_name='crop_size'       )

         _A, _A    =       get_image_size(snake_case_       )
         _A    =       min(snake_case_							,    snake_case_       )
         _A    =       (size['height'] / crop_size['height']) * min_dim
         _A    =       (size['width'] / crop_size['width']) * min_dim
         return center_crop(snake_case_							,    size=(cropped_height, cropped_width)							,    data_format=snake_case_							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_ = PIL.Image.BICUBIC							,    snake_case_ = None							,    **snake_case_							,    ):
         _A    =       get_size_dict(snake_case_       )
         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(
             snake_case_							,    size=(size['height'], size['width'])							,    resample=snake_case_							,    data_format=snake_case_							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_ = None							,    **snake_case_							,    ):
         return rescale(snake_case_							,    scale=snake_case_							,    data_format=snake_case_							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_ = None							,    **snake_case_							,    ):
         return normalize(snake_case_							,    mean=snake_case_							,    std=snake_case_							,    data_format=snake_case_							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = ChannelDimension.FIRST							,    **snake_case_							,    ):
         _A    =       do_center_crop if do_center_crop is not None else self.do_center_crop
         _A    =       crop_size if crop_size is not None else self.crop_size
         _A    =       get_size_dict(snake_case_							,    param_name='crop_size'       )
         _A    =       do_resize if do_resize is not None else self.do_resize
         _A    =       size if size is not None else self.size
         _A    =       get_size_dict(snake_case_       )
         _A    =       resample if resample is not None else self.resample
         _A    =       do_rescale if do_rescale is not None else self.do_rescale
         _A    =       rescale_factor if rescale_factor is not None else self.rescale_factor
         _A    =       do_normalize if do_normalize is not None else self.do_normalize
         _A    =       image_mean if image_mean is not None else self.image_mean
         _A    =       image_std if image_std is not None else self.image_std

         _A    =       make_list_of_images(snake_case_       )

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

         if do_center_crop and crop_size is None:
             raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'       )

         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.'       )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'       )

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

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

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

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

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

         _A    =       [to_channel_dimension_format(snake_case_							,    snake_case_       ) for image in images]

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












from sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





