



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 	a__       (       lowerCamelCase_       ):


					"""simple docstring"""



					A__  :				List[str]					 =				CONFIG_NAME

					@register_to_config
					def __init__(		self     :str      ,  lowercase__     :bool = True      ,  lowercase__     :int = 8      ,  lowercase__     :str = "lanczos"      ,  lowercase__     :bool = True      ,  lowercase__     :bool = False      ,  ):
										super().__init__()

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :np.ndarray    ):



										if images.ndim == 3:
															lowercase						   =       images[None, ...]
										lowercase						   =       (images * 255).round().astype('uint8'    )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															lowercase						   =       [Image.fromarray(image.squeeze()      ,  mode='L'    ) for image in images]
										else:
															lowercase						   =       [Image.fromarray(__UpperCAmelCase    ) for image in images]

										return pil_images

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :Union[List[PIL.Image.Image], PIL.Image.Image]    ):



										if not isinstance(__UpperCAmelCase      ,  __UpperCAmelCase    ):
															lowercase						   =       [images]
										lowercase						   =       [np.array(__UpperCAmelCase    ).astype(np.floataa    ) / 255.0 for image in images]
										lowercase						   =       np.stack(__UpperCAmelCase      ,  axis=0    )

										return images

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :np.ndarray    ):



										if images.ndim == 3:
															lowercase						   =       images[..., None]

										lowercase						   =       torch.from_numpy(images.transpose(0      ,  3      ,  1      ,  2    )    )
										return images

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :torch.FloatTensor    ):
										lowercase						   =       images.cpu().permute(0      ,  2      ,  3      ,  1    ).float().numpy()
										return images

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :List[Any]    ):
										return 2.0 * images - 1.0

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :List[Any]    ):
										return (images / 2 + 0.5).clamp(0      ,  1    )

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :PIL.Image.Image    ):
										lowercase						   =       image.convert('RGB'    )
										return image

					def 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  lowercase__     :PIL.Image.Image      ,  lowercase__     :Optional[int] = None      ,  lowercase__     :Optional[int] = None      ,  ):



										if height is None:
															lowercase						   =       image.height
										if width is None:
															lowercase						   =       image.width

										lowercase   ,      lowercase						   =       (
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										lowercase						   =       image.resize((width, height)      ,  resample=PIL_INTERPOLATION[self.config.resample]    )
										return image

					def 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  lowercase__     :Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]      ,  lowercase__     :Optional[int] = None      ,  lowercase__     :Optional[int] = None      ,  ):
										lowercase						   =       (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(__UpperCAmelCase      ,  __UpperCAmelCase    ):
															lowercase						   =       [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:
																				lowercase						   =       [self.convert_to_rgb(__UpperCAmelCase    ) for i in image]
															if self.config.do_resize:
																				lowercase						   =       [self.resize(__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase    ) for i in image]
															lowercase						   =       self.pil_to_numpy(__UpperCAmelCase    )  # to np
															lowercase						   =       self.numpy_to_pt(__UpperCAmelCase    )  # to pt

										elif isinstance(image[0]      ,  np.ndarray    ):
															lowercase						   =       np.concatenate(__UpperCAmelCase      ,  axis=0    ) if image[0].ndim == 4 else np.stack(__UpperCAmelCase      ,  axis=0    )
															lowercase						   =       self.numpy_to_pt(__UpperCAmelCase    )
															lowercase   ,      lowercase   ,      lowercase   ,      lowercase						   =       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    ):
															lowercase						   =       torch.cat(__UpperCAmelCase      ,  axis=0    ) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase      ,  axis=0    )
															lowercase   ,      lowercase   ,      lowercase   ,      lowercase						   =       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]
										lowercase						   =       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      ,  )
															lowercase						   =       False

										if do_normalize:
															lowercase						   =       self.normalize(__UpperCAmelCase    )

										return image



					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :torch.FloatTensor      ,  lowercase__     :str = "pil"      ,  lowercase__     :Optional[List[bool]] = None      ,  ):



										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"]:
															lowercase						   =       (
															    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    )
															lowercase						   =       'np'

										if output_type == "latent":
															return image

										if do_denormalize is None:
															lowercase						   =       [self.config.do_normalize] * image.shape[0]

										lowercase						   =       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

										lowercase						   =       self.pt_to_numpy(__UpperCAmelCase    )

										if output_type == "np":
															return image

										if output_type == "pil":
															return self.numpy_to_pil(__UpperCAmelCase    )




class 	a__       (       lowerCamelCase_       ):


					"""simple docstring"""



					A__  :				Optional[int]					 =				CONFIG_NAME

					@register_to_config
					def __init__(		self     :Optional[int]      ,  lowercase__     :bool = True      ,  lowercase__     :int = 8      ,  lowercase__     :str = "lanczos"      ,  lowercase__     :bool = True      ,  ):
										super().__init__()

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :List[str]    ):



										if images.ndim == 3:
															lowercase						   =       images[None, ...]
										lowercase						   =       (images * 255).round().astype('uint8'    )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															lowercase						   =       [Image.fromarray(image.squeeze()      ,  mode='L'    ) for image in images]
										else:
															lowercase						   =       [Image.fromarray(image[:, :, :3]    ) for image in images]

										return pil_images

					@staticmethod
					def 				__UpperCAmelCase		(		lowercase__     :Dict    ):
										return image[:, :, 1] * 2**8 + image[:, :, 2]

					def 				__UpperCAmelCase		(		self     :List[str]      ,  lowercase__     :List[Any]    ):



										if images.ndim == 3:
															lowercase						   =       images[None, ...]
										lowercase						   =       images[:, :, :, 3:]
										if images.shape[-1] == 6:
															lowercase						   =       (images_depth * 255).round().astype('uint8'    )
															lowercase						   =       [
															    Image.fromarray(self.rgblike_to_depthmap(__UpperCAmelCase    )      ,  mode='I;16'    ) for image_depth in images_depth
															]
										elif images.shape[-1] == 4:
															lowercase						   =       (images_depth * 6_5535.0).astype(np.uintaa    )
															lowercase						   =       [Image.fromarray(__UpperCAmelCase      ,  mode='I;16'    ) for image_depth in images_depth]
										else:
															raise Exception('Not supported'    )

										return pil_images



					def 				__UpperCAmelCase		(		self     :Dict      ,  lowercase__     :torch.FloatTensor      ,  lowercase__     :str = "pil"      ,  lowercase__     :Optional[List[bool]] = None      ,  ):



										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"]:
															lowercase						   =       (
															    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    )
															lowercase						   =       'np'

										if do_denormalize is None:
															lowercase						   =       [self.config.do_normalize] * image.shape[0]

										lowercase						   =       torch.stack(
										    [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

										lowercase						   =       self.pt_to_numpy(__UpperCAmelCase    )

										if output_type == "np":
															if image.shape[-1] == 6:
																				lowercase						   =       np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]      ,  axis=0    )
															else:
																				lowercase						   =       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"""    )












from collections import Counter




def  a_		(							__snake_case					)	->			int:

			'''simple docstring'''


			return sum(socks_by_color // 2 for socks_by_color in Counter(__snake_case					).values()					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__a			:	List[Any]							     =		[int(x) for x in input("""Enter socks by color :""").rstrip().split()]
				print(f"""sock_merchant({colors}) = {sock_merchant(colors)}""")
