






"""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"""   )







"""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