



'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCAmelCase						:		List[str]						      = logging.get_logger(__name__)


if is_vision_available():
	import PIL






class        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  List[str]   =				["""pixel_values"""]



					def __init__(     self      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					UpperCamelCase = PILImageResampling.BILINEAR      ,					UpperCamelCase = True      ,					UpperCamelCase = False      ,					UpperCamelCase = True      ,					UpperCamelCase = True      ,					UpperCamelCase = 1 / 255      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      None:
						super().__init__(**UpperCamelCase  )

						__lowerCAmelCase       				=	size if size is not None else {"height": 2560, "width": 1920}
						if isinstance(UpperCamelCase      ,					(tuple, list)  ):
							# The previous feature extractor size parameter was in (width, height) format
							__lowerCAmelCase       				=	size[::-1]
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )

						__lowerCAmelCase       				=	do_resize
						__lowerCAmelCase       				=	size
						__lowerCAmelCase       				=	resample
						__lowerCAmelCase       				=	do_thumbnail
						__lowerCAmelCase       				=	do_align_long_axis
						__lowerCAmelCase       				=	do_pad
						__lowerCAmelCase       				=	do_rescale
						__lowerCAmelCase       				=	rescale_factor
						__lowerCAmelCase       				=	do_normalize
						__lowerCAmelCase       				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase       				=	image_std if image_std is not None else IMAGENET_STANDARD_STD



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None  )  ->      np.ndarray:
						__lowerCAmelCase				,							__lowerCAmelCase       				=	get_image_size(UpperCamelCase  )
						__lowerCAmelCase				,							__lowerCAmelCase       				=	size["height"], size["width"]

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
							__lowerCAmelCase       				=	np.rotaa(UpperCamelCase      ,					3  )

						if data_format is not None:
							__lowerCAmelCase       				=	to_channel_dimension_format(UpperCamelCase      ,					UpperCamelCase  )

						return image



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = False      ,					UpperCamelCase = None      ,					)  ->      np.ndarray:
						__lowerCAmelCase				,							__lowerCAmelCase       				=	size["height"], size["width"]
						__lowerCAmelCase				,							__lowerCAmelCase       				=	get_image_size(UpperCamelCase  )

						__lowerCAmelCase       				=	output_width - input_width
						__lowerCAmelCase       				=	output_height - input_height

						if random_padding:
							__lowerCAmelCase       				=	np.random.randint(low=0      ,					high=delta_height + 1  )
							__lowerCAmelCase       				=	np.random.randint(low=0      ,					high=delta_width + 1  )
						else:
							__lowerCAmelCase       				=	delta_height // 2
							__lowerCAmelCase       				=	delta_width // 2

						__lowerCAmelCase       				=	delta_height - pad_top
						__lowerCAmelCase       				=	delta_width - pad_left

						__lowerCAmelCase       				=	((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(UpperCamelCase      ,					UpperCamelCase      ,					data_format=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					*UpperCamelCase      ,					**UpperCamelCase  )  ->      int:
						logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."  )
						return self.pad_image(*UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = PILImageResampling.BICUBIC      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						__lowerCAmelCase				,							__lowerCAmelCase       				=	get_image_size(UpperCamelCase  )
						__lowerCAmelCase				,							__lowerCAmelCase       				=	size["height"], size["width"]

						# We always resize to the smallest of either the input or output size.
						__lowerCAmelCase       				=	min(UpperCamelCase      ,					UpperCamelCase  )
						__lowerCAmelCase       				=	min(UpperCamelCase      ,					UpperCamelCase  )

						if height == input_height and width == input_width:
							return image

						if input_height > input_width:
							__lowerCAmelCase       				=	int(input_width * height / input_height  )
						elif input_width > input_height:
							__lowerCAmelCase       				=	int(input_height * width / input_width  )

						return resize(
						    UpperCamelCase      ,					size=(height, width)      ,					resample=UpperCamelCase      ,					reducing_gap=2.0      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = PILImageResampling.BICUBIC      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )
						__lowerCAmelCase       				=	min(size["height"]      ,					size["width"]  )
						__lowerCAmelCase       				=	get_resize_output_image_size(UpperCamelCase      ,					size=UpperCamelCase      ,					default_to_square=UpperCamelCase  )
						__lowerCAmelCase       				=	resize(UpperCamelCase      ,					size=UpperCamelCase      ,					resample=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )
						return resized_image



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      List[str]:
						return rescale(UpperCamelCase      ,					scale=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						return normalize(UpperCamelCase      ,					mean=UpperCamelCase      ,					std=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = False      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = ChannelDimension.FIRST      ,					**UpperCamelCase      ,					)  ->      PIL.Image.Image:
						__lowerCAmelCase       				=	do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase       				=	size if size is not None else self.size
						if isinstance(UpperCamelCase      ,					(tuple, list)  ):
							# Previous feature extractor had size in (width, height) format
							__lowerCAmelCase       				=	size[::-1]
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )
						__lowerCAmelCase       				=	resample if resample is not None else self.resample
						__lowerCAmelCase       				=	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
						__lowerCAmelCase       				=	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
						__lowerCAmelCase       				=	do_pad if do_pad is not None else self.do_pad
						__lowerCAmelCase       				=	do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase       				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase       				=	do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase       				=	image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase       				=	image_std if image_std is not None else self.image_std

						__lowerCAmelCase       				=	make_list_of_images(UpperCamelCase  )

						if not valid_images(UpperCamelCase  ):
							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."  )

						if do_pad and size is None:
							raise ValueError("Size must be specified if do_pad 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.
						__lowerCAmelCase       				=	[to_numpy_array(UpperCamelCase  ) for image in images]

						if do_align_long_axis:
							__lowerCAmelCase       				=	[self.align_long_axis(UpperCamelCase      ,					size=UpperCamelCase  ) for image in images]

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

						if do_thumbnail:
							__lowerCAmelCase       				=	[self.thumbnail(image=UpperCamelCase      ,					size=UpperCamelCase  ) for image in images]

						if do_pad:
							__lowerCAmelCase       				=	[self.pad_image(image=UpperCamelCase      ,					size=UpperCamelCase      ,					random_padding=UpperCamelCase  ) for image in images]

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

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

						__lowerCAmelCase       				=	[to_channel_dimension_format(UpperCamelCase      ,					UpperCamelCase  ) for image in images]

						__lowerCAmelCase       				=	{"pixel_values": images}
						return BatchFeature(data=UpperCamelCase      ,					tensor_type=UpperCamelCase  )




'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase						:		Optional[Any]						      = '''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


lowerCAmelCase						:		str						      = '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> 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])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


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






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



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True      ,					UpperCamelCase=None  )  ->      List[str]:
						return {
						    "accuracy": float(
						        accuracy_score(UpperCamelCase      ,					UpperCamelCase      ,					normalize=UpperCamelCase      ,					sample_weight=UpperCamelCase  )  )
						}