
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


__magic_name__  :  Optional[Any]     			=   logging.get_logger(__name__)


class 							lowerCamelCase       (		snake_case__					):



  """simple docstring"""





  lowerCAmelCase_								=		["""pixel_values"""]

  def __init__(					self						,    __UpperCamelCase = True						,    __UpperCamelCase = None						,    __UpperCamelCase = PILImageResampling.BILINEAR						,    __UpperCamelCase = True						,    __UpperCamelCase = 1 / 255						,    __UpperCamelCase = True						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    **__UpperCamelCase						,    ):
      super().__init__(**__UpperCamelCase					)
      A_								=					size if size is not None else {"height": 224, "width": 224}
      A_								=					get_size_dict(__UpperCamelCase					)
      A_								=					do_resize
      A_								=					do_rescale
      A_								=					do_normalize
      A_								=					size
      A_								=					resample
      A_								=					rescale_factor
      A_								=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      A_								=					image_std if image_std is not None else IMAGENET_STANDARD_STD

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = PILImageResampling.BILINEAR						,    __UpperCamelCase = None						,    **__UpperCamelCase						,    ):
      A_								=					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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = None						,    **__UpperCamelCase					):
      return rescale(__UpperCamelCase						,    scale=__UpperCamelCase						,    data_format=__UpperCamelCase						,    **__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = None						,    **__UpperCamelCase						,    ):
      return normalize(__UpperCamelCase						,    mean=__UpperCamelCase						,    std=__UpperCamelCase						,    data_format=__UpperCamelCase						,    **__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = ChannelDimension.FIRST						,    **__UpperCamelCase						,    ):
      A_								=					do_resize if do_resize is not None else self.do_resize
      A_								=					do_rescale if do_rescale is not None else self.do_rescale
      A_								=					do_normalize if do_normalize is not None else self.do_normalize
      A_								=					resample if resample is not None else self.resample
      A_								=					rescale_factor if rescale_factor is not None else self.rescale_factor
      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_								=					size if size is not None else self.size
      A_								=					get_size_dict(__UpperCamelCase					)

      A_								=					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.
      A_								=					[to_numpy_array(__UpperCamelCase					) for image in images]

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

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

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

      A_								=					[to_channel_dimension_format(__UpperCamelCase						,    __UpperCamelCase					) for image in images]

      A_								=					{"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
