




"""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  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =CONFIG_NAME

				@register_to_config
				def __init__(				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = True ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = 8 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = "lanczos" ,SCREAMING_SNAKE_CASE_				:       List[Any] = True ,SCREAMING_SNAKE_CASE_				:       List[str] = False ,):
										super().__init__()

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):





										if images.ndim == 3:
																snake_case__    :   List[Any]							     =   images[None, ...]
										snake_case__    :   List[Any]							     =   (images * 2_5_5).round().astype('''uint8''' )
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																snake_case__    :   str							     =   [Image.fromarray(image.squeeze() ,mode='''L''' ) for image in images]
										else:
																snake_case__    :   Optional[Any]							     =   [Image.fromarray(__A ) for image in images]

										return pil_images

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Optional[int] ):





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

										return images

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Tuple ):





										if images.ndim == 3:
																snake_case__    :   Union[str, Any]							     =   images[..., None]

										snake_case__    :   List[str]							     =   torch.from_numpy(images.transpose(0 ,3 ,1 ,2 ) )
										return images

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										snake_case__    :   Union[str, Any]							     =   images.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
										return images

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       int ):
										return 2.0 * images - 1.0

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Tuple ):
										return (images / 2 + 0.5).clamp(0 ,1 )

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										snake_case__    :   List[Any]							     =   image.convert('''RGB''' )
										return image

				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[Any] = None ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = None ,):





										if height is None:
																snake_case__    :   Dict							     =   image.height
										if width is None:
																snake_case__    :   Union[str, Any]							     =   image.width

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

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Any = None ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = None ,):
										snake_case__    :   Any							     =   (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(__A ,__A ):
																snake_case__    :   List[str]							     =   [image]
										elif not (isinstance(__A ,__A ) and all(isinstance(__A ,__A ) for i in image )):
																raise ValueError(
																    F'''Input is in incorrect format: {[type(__A ) for i in image]}. Currently, we only support {', '.join(__A )}''' )

										if isinstance(image[0] ,PIL.Image.Image ):
																if self.config.do_convert_rgb:
																						snake_case__    :   Union[str, Any]							     =   [self.convert_to_rgb(__A ) for i in image]
																if self.config.do_resize:
																						snake_case__    :   List[Any]							     =   [self.resize(__A ,__A ,__A ) for i in image]
																snake_case__    :   Tuple							     =   self.pil_to_numpy(__A )  # to np
																snake_case__    :   List[Any]							     =   self.numpy_to_pt(__A )  # to pt

										elif isinstance(image[0] ,np.ndarray ):
																snake_case__    :   int							     =   np.concatenate(__A ,axis=0 ) if image[0].ndim == 4 else np.stack(__A ,axis=0 )
																snake_case__    :   Tuple							     =   self.numpy_to_pt(__A )
																snake_case__    :   Dict							     =   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 ):
																snake_case__    :   str							     =   torch.cat(__A ,axis=0 ) if image[0].ndim == 4 else torch.stack(__A ,axis=0 )
																snake_case__    :   Any							     =   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]
										snake_case__    :   Any							     =   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()}]''' ,__A ,)
																snake_case__    :   List[Any]							     =   False

										if do_normalize:
																snake_case__    :   Dict							     =   self.normalize(__A )

										return image





				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       List[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = None ,):





										if not isinstance(__A ,torch.Tensor ):
																raise ValueError(
																    F'''Input for postprocessing is in incorrect format: {type(__A )}. We only support pytorch tensor''' )
										if output_type not in ["latent", "pt", "np", "pil"]:
																snake_case__    :   List[str]							     =   (
																    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''' ,__A ,standard_warn=__A )
																snake_case__    :   Any							     =   "np"

										if output_type == "latent":
																return image

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

										snake_case__    :   str							     =   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

										snake_case__    :   int							     =   self.pt_to_numpy(__A )

										if output_type == "np":
																return image

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

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =CONFIG_NAME

				@register_to_config
				def __init__(				self				:       Any ,SCREAMING_SNAKE_CASE_				:       str = True ,SCREAMING_SNAKE_CASE_				:       List[str] = 8 ,SCREAMING_SNAKE_CASE_				:       List[Any] = "lanczos" ,SCREAMING_SNAKE_CASE_				:       Any = True ,):
										super().__init__()

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       int ):





										if images.ndim == 3:
																snake_case__    :   List[str]							     =   images[None, ...]
										snake_case__    :   Tuple							     =   (images * 2_5_5).round().astype('''uint8''' )
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																snake_case__    :   List[Any]							     =   [Image.fromarray(image.squeeze() ,mode='''L''' ) for image in images]
										else:
																snake_case__    :   Any							     =   [Image.fromarray(image[:, :, :3] ) for image in images]

										return pil_images

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										return image[:, :, 1] * 2**8 + image[:, :, 2]

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):





										if images.ndim == 3:
																snake_case__    :   Union[str, Any]							     =   images[None, ...]
										snake_case__    :   int							     =   images[:, :, :, 3:]
										if images.shape[-1] == 6:
																snake_case__    :   List[str]							     =   (images_depth * 2_5_5).round().astype('''uint8''' )
																snake_case__    :   Optional[int]							     =   [
																    Image.fromarray(self.rgblike_to_depthmap(__A ) ,mode='''I;16''' ) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																snake_case__    :   Tuple							     =   (images_depth * 65535.0).astype(np.uintaa )
																snake_case__    :   int							     =   [Image.fromarray(__A ,mode='''I;16''' ) for image_depth in images_depth]
										else:
																raise Exception('''Not supported''' )

										return pil_images





				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] = "pil" ,SCREAMING_SNAKE_CASE_				:       Tuple = None ,):





										if not isinstance(__A ,torch.Tensor ):
																raise ValueError(
																    F'''Input for postprocessing is in incorrect format: {type(__A )}. We only support pytorch tensor''' )
										if output_type not in ["latent", "pt", "np", "pil"]:
																snake_case__    :   Optional[Any]							     =   (
																    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''' ,__A ,standard_warn=__A )
																snake_case__    :   str							     =   "np"

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

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

										snake_case__    :   int							     =   self.pt_to_numpy(__A )

										if output_type == "np":
																if image.shape[-1] == 6:
																						snake_case__    :   Optional[Any]							     =   np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image] ,axis=0 )
																else:
																						snake_case__    :   Union[str, Any]							     =   image[:, :, :, 3:]
																return image[:, :, :, :3], image_depth

										if output_type == "pil":
																return self.numpy_to_pil(__A ), self.numpy_to_depth(__A )
										else:
																raise Exception(F'''This type {output_type} is not supported''' )








"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Any							     =   data
										snake_case__    :   Node | None							     =   None





				def __str__(				self				:       Union[str, Any] ):
										return F'''{self.data}'''







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Optional[int] ):
										snake_case__    :   Node | None							     =   None
										snake_case__    :   Node | None							     =   None

				def __iter__(				self				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   self.front
										while node:
																yield node.data
																snake_case__    :   Optional[int]							     =   node.next

				def __len__(				self				:       Any ):
										return len(tuple(iter(self ) ) )

				def __str__(				self				:       Union[str, Any] ):
										return " <- ".join(str(SCREAMING_SNAKE_CASE_ ) for item in self )

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self ) == 0

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
										if self.is_empty():
																snake_case__    :   List[Any]							     =   node
										else:
																assert isinstance(self.rear ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   node
																snake_case__    :   int							     =   node

				def  _lowerCamelCase   (				self				:       int ):
										if self.is_empty():
																raise IndexError('''dequeue from empty queue''' )
										assert isinstance(self.front ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   self.front
										snake_case__    :   str							     =   self.front.next
										if self.front is None:
																snake_case__    :   Optional[Any]							     =   None
										return node.data





				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   None


if __name__ == "__main__":
							from doctest import testmod

							testmod()


