






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


_a       =    logging.get_logger(__name__)





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= ["pixel_values"]

	def __init__(				self			,       UpperCAmelCase = True			,       UpperCAmelCase = None			,       UpperCAmelCase = PILImageResampling.BILINEAR			,       UpperCAmelCase = True			,       UpperCAmelCase = 1 / 255			,       UpperCAmelCase = True			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = PILImageResampling.BILINEAR			,       UpperCAmelCase = None			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     get_size_dict(UpperCAmelCase       )
							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(
							    UpperCAmelCase			,       size=(size['height'], size['width'])			,       resample=UpperCAmelCase			,       data_format=UpperCAmelCase			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = None			,       **UpperCAmelCase       ):
							"""simple docstring"""







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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = None			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







							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 = None			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       UpperCAmelCase = ChannelDimension.FIRST			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







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

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

							_UpperCAmelCase							   =     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.'       )

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

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

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

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

							_UpperCAmelCase							   =     [to_channel_dimension_format(UpperCAmelCase			,       UpperCAmelCase       ) for image in images]

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








import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
