code: 
  
  
  
  
  
  
  """simple docstring"""
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(		self   :  Optional[Any]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  int = 8							,						UpperCamelCase_   :  str = "lanczos"							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  bool = False							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  np.ndarray   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						(images * 2_5_5).round().astype('uint8'   )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    __magic_name__										=						[Image.fromarray(image.squeeze()							,						mode='L'   ) for image in images]
             else:
                    __magic_name__										=						[Image.fromarray(UpperCamelCase_   ) for image in images]
  
             return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[List[PIL.Image.Image], PIL.Image.Image]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                    __magic_name__										=						[images]
             __magic_name__										=						[np.array(UpperCamelCase_   ).astype(np.floataa   ) / 255.0 for image in images]
             __magic_name__										=						np.stack(UpperCamelCase_							,						axis=0   )
  
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  np.ndarray   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[..., None]
  
             __magic_name__										=						torch.from_numpy(images.transpose(0							,						3							,						1							,						2   )   )
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  torch.FloatTensor   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						images.cpu().permute(0							,						2							,						3							,						1   ).float().numpy()
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return 2.0 * images - 1.0
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return (images / 2 + 0.5).clamp(0							,						1   )
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  PIL.Image.Image   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						image.convert('RGB'   )
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  PIL.Image.Image							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  Optional[int] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if height is None:
                    __magic_name__										=						image.height
             if width is None:
                    __magic_name__										=						image.width
  
             __magic_name__							,     __magic_name__										=						(
                 x - x % self.config.vae_scale_factor for x in (width, height)
             )  # resize to integer multiple of vae_scale_factor
             __magic_name__										=						image.resize((width, height)							,						resample=PIL_INTERPOLATION[self.config.resample]   )
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  Optional[int] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						(PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                    __magic_name__										=						[image]
             elif not (isinstance(UpperCamelCase_							,						UpperCamelCase_   ) and all(isinstance(UpperCamelCase_							,						UpperCamelCase_   ) for i in image   )):
                    raise ValueError(
                        f"""Input is in incorrect format: {[type(UpperCamelCase_   ) for i in image]}. Currently, we only support {", ".join(UpperCamelCase_   )}"""   )
  
             if isinstance(image[0]							,						PIL.Image.Image   ):
                    if self.config.do_convert_rgb:
                           __magic_name__										=						[self.convert_to_rgb(UpperCamelCase_   ) for i in image]
                    if self.config.do_resize:
                           __magic_name__										=						[self.resize(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   ) for i in image]
                    __magic_name__										=						self.pil_to_numpy(UpperCamelCase_   )  # to np
                    __magic_name__										=						self.numpy_to_pt(UpperCamelCase_   )  # to pt
  
             elif isinstance(image[0]							,						np.ndarray   ):
                    __magic_name__										=						np.concatenate(UpperCamelCase_							,						axis=0   ) if image[0].ndim == 4 else np.stack(UpperCamelCase_							,						axis=0   )
                    __magic_name__										=						self.numpy_to_pt(UpperCamelCase_   )
                    __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						image.shape
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )
  
             elif isinstance(image[0]							,						torch.Tensor   ):
                    __magic_name__										=						torch.cat(UpperCamelCase_							,						axis=0   ) if image[0].ndim == 4 else torch.stack(UpperCamelCase_							,						axis=0   )
                    __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						image.shape
  
                    # don't need any preprocess if the image is latents
                    if channel == 4:
                           return image
  
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )
  
          # expected range [0,1], normalize to [-1,1]
             __magic_name__										=						self.config.do_normalize
             if image.min() < 0:
                    warnings.warn(
                        'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                        f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""							,						UpperCamelCase_							,						)
                    __magic_name__										=						False
  
             if do_normalize:
                    __magic_name__										=						self.normalize(UpperCamelCase_   )
  
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  torch.FloatTensor							,						UpperCamelCase_   :  str = "pil"							,						UpperCamelCase_   :  Optional[List[bool]] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						torch.Tensor   ):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(UpperCamelCase_   )}. We only support pytorch tensor"""   )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    __magic_name__										=						(
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'							,						'1.0.0'							,						UpperCamelCase_							,						standard_warn=UpperCamelCase_   )
                    __magic_name__										=						'np'
  
             if output_type == "latent":
                    return image
  
             if do_denormalize is None:
                    __magic_name__										=						[self.config.do_normalize] * image.shape[0]
  
             __magic_name__										=						torch.stack(
                 [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )
  
             if output_type == "pt":
                    return image
  
             __magic_name__										=						self.pt_to_numpy(UpperCamelCase_   )
  
             if output_type == "np":
                    return image
  
             if output_type == "pil":
                    return self.numpy_to_pil(UpperCamelCase_   )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(		self   :  List[str]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  int = 8							,						UpperCamelCase_   :  str = "lanczos"							,						UpperCamelCase_   :  bool = True							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						(images * 2_5_5).round().astype('uint8'   )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    __magic_name__										=						[Image.fromarray(image.squeeze()							,						mode='L'   ) for image in images]
             else:
                    __magic_name__										=						[Image.fromarray(image[:, :, :3]   ) for image in images]
  
             return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						images[:, :, :, 3:]
             if images.shape[-1] == 6:
                    __magic_name__										=						(images_depth * 2_5_5).round().astype('uint8'   )
                    __magic_name__										=						[
                        Image.fromarray(self.rgblike_to_depthmap(UpperCamelCase_   )							,						mode='I;16'   ) for image_depth in images_depth
                    ]
             elif images.shape[-1] == 4:
                    __magic_name__										=						(images_depth * 6_5_5_3_5.0).astype(np.uintaa   )
                    __magic_name__										=						[Image.fromarray(UpperCamelCase_							,						mode='I;16'   ) for image_depth in images_depth]
             else:
                    raise Exception('Not supported'   )
  
             return pil_images
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  torch.FloatTensor							,						UpperCamelCase_   :  str = "pil"							,						UpperCamelCase_   :  Optional[List[bool]] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						torch.Tensor   ):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(UpperCamelCase_   )}. We only support pytorch tensor"""   )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    __magic_name__										=						(
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'							,						'1.0.0'							,						UpperCamelCase_							,						standard_warn=UpperCamelCase_   )
                    __magic_name__										=						'np'
  
             if do_denormalize is None:
                    __magic_name__										=						[self.config.do_normalize] * image.shape[0]
  
             __magic_name__										=						torch.stack(
                 [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )
  
             __magic_name__										=						self.pt_to_numpy(UpperCamelCase_   )
  
             if output_type == "np":
                    if image.shape[-1] == 6:
                           __magic_name__										=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]							,						axis=0   )
                    else:
                           __magic_name__										=						image[:, :, :, 3:]
                    return image[:, :, :, :3], image_depth
  
             if output_type == "pil":
                    return self.numpy_to_pil(UpperCamelCase_   ), self.numpy_to_depth(UpperCamelCase_   )
             else:
                    raise Exception(f"""This type {output_type} is not supported"""   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						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
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 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)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    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]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test