"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class       _lowercase		( __lowerCamelCase   ,   __lowerCamelCase	):

				def __init__(  self							:     Dict  ):


										'''simple docstring'''
										super().__init__()

										self.register_buffer("""watermark_image"""							,     torch.zeros((6_2, 6_2, 4)  )  )
										UpperCAmelCase__       						=		None

				def 							A			(  self							:     str							,     _lowercase							:     List[PIL.Image.Image]							,     _lowercase							:     Optional[int]=None  ):


										'''simple docstring'''
										# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

										UpperCAmelCase__       						=		images[0].height
										UpperCAmelCase__       						=		images[0].width

										UpperCAmelCase__       						=		sample_size or h

										UpperCAmelCase__       						=		min(h / sample_size							,     w / sample_size  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		(int(h / coef  ), int(w / coef  )) if coef < 1 else (h, w)

										UpperCAmelCase__						,   UpperCAmelCase__       						=		1_0_2_4**2, img_w * img_h
										UpperCAmelCase__       						=		(Sa / Sa) ** 0.5
										UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__       						=		int(K * 6_2  ), img_w - int(1_4 * K  ), img_h - int(1_4 * K  )

										if self.watermark_image_as_pil is None:
																UpperCAmelCase__       						=		self.watermark_image.to(torch.uinta  ).cpu().numpy()
																UpperCAmelCase__       						=		Image.fromarray(_lowercase							,     mode="""RGBA"""  )
																UpperCAmelCase__       						=		watermark_image

										UpperCAmelCase__       						=		self.watermark_image_as_pil.resize(
										    (wm_size, wm_size)							,     PIL_INTERPOLATION["""bicubic"""]							,     reducing_gap=_lowercase  )

										for pil_img in images:
																pil_img.paste(_lowercase							,     box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)							,     mask=wm_img.split()[-1]  )

										return images


"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# 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(UpperCAmelCase__      )      )[::-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(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		rng.choice(UpperCAmelCase__		,      size=UpperCAmelCase__		,      replace=UpperCAmelCase__      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )







def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		np.bincount(UpperCAmelCase__      )
						if np.min(UpperCAmelCase__      ) < 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(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		class_indices[i].take(UpperCAmelCase__		,      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(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

