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 				_SCREAMING_SNAKE_CASE( __lowerCamelCase							,   __lowerCamelCase						):
   def __init__(     self     )						->Optional[int]:
        super().__init__()

        self.register_buffer('''watermark_image'''				,  torch.zeros((62, 62, 4)     )     )
        a	:     Dict						      =    None


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None     )						->int:
        a	:     List[Any]						      =    images[0].height
        a	:     Union[str, Any]						      =    images[0].width

        a	:     List[Any]						      =    sample_size or h

        a	:     List[str]						      =    min(h / sample_size				,  w / sample_size     )
        a      ,				a	:     List[Any]						      =    (int(h / coef     ), int(w / coef     )) if coef < 1 else (h, w)

        a      ,				a	:     List[Any]						      =    10_24**2, img_w * img_h
        a	:     Union[str, Any]						      =    (Sa / Sa) ** 0.5
        a      ,				a      ,				a	:     List[Any]						      =    int(K * 62     ), img_w - int(14 * K     ), img_h - int(14 * K     )

        if self.watermark_image_as_pil is None:
             a	:     Union[str, Any]						      =    self.watermark_image.to(torch.uinta     ).cpu().numpy()
             a	:     Any						      =    Image.fromarray(_lowercase				,  mode='''RGBA'''     )
             a	:     Dict						      =    watermark_image

        a	:     Any						      =    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

