



import numpy as np



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			lowercase__         = np.floor(_UpperCAmelCase     )
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			lowercase__         = int(n_draws - floored.sum()     )
			if need_to_add > 0:
						lowercase__         = continuous - floored
						lowercase__         = np.sort(np.unique(_UpperCAmelCase     )     )[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((lowercase__     )     ,				     )         = 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
									lowercase__         = min(len(_UpperCAmelCase     )  ,     _UpperCAmelCase     )
									lowercase__         = 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 __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_=10     ):
			lowercase__     ,				lowercase__         = np.unique(_UpperCAmelCase  ,     return_inverse=_UpperCAmelCase     )
			lowercase__         = classes.shape[0]
			lowercase__         = 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)     )
			lowercase__         = np.split(np.argsort(_UpperCAmelCase  ,     kind="mergesort"     )  ,     np.cumsum(_UpperCAmelCase     )[:-1]     )
			for _ in range(_UpperCAmelCase     ):
						lowercase__         = approximate_mode(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase     )
						lowercase__         = class_counts - n_i
						lowercase__         = approximate_mode(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase     )

						lowercase__         = []
						lowercase__         = []

						for i in range(_UpperCAmelCase     ):
									lowercase__         = rng.permutation(class_counts[i]     )
									lowercase__         = 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]]     )
						lowercase__         = rng.permutation(_UpperCAmelCase     )
						lowercase__         = rng.permutation(_UpperCAmelCase     )

						yield train, test











from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available

@dataclass
class 					__A   (  a			):
				__A            = 42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





