
"""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      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						CONFIG_NAME




							@register_to_config
							def __init__(				self  ,							snake_case_ = True  ,							snake_case_ = 8  ,							snake_case_ = "lanczos"  ,							snake_case_ = True  ,							snake_case_ = False  ,							)   ->       Tuple:
													super().__init__()




							@staticmethod
							def  A__		(				snake_case_							)   ->       PIL.Image.Image:
													if images.ndim == 3:
																			__lowerCAmelCase        =       images[None, ...]
													__lowerCAmelCase        =       (images * 255).round().astype("""uint8"""							)
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			__lowerCAmelCase        =       [Image.fromarray(image.squeeze()  ,							mode="""L"""							) for image in images]
													else:
																			__lowerCAmelCase        =       [Image.fromarray(snake_case_							) for image in images]

													return pil_images




							@staticmethod
							def  A__		(				snake_case_							)   ->       np.ndarray:
													if not isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       [images]
													__lowerCAmelCase        =       [np.array(snake_case_							).astype(np.floataa							) / 255.0 for image in images]
													__lowerCAmelCase        =       np.stack(snake_case_  ,							axis=0							)

													return images




							@staticmethod
							def  A__		(				snake_case_							)   ->       torch.FloatTensor:
													if images.ndim == 3:
																			__lowerCAmelCase        =       images[..., None]

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




							@staticmethod
							def  A__		(				snake_case_							)   ->       np.ndarray:
													__lowerCAmelCase        =       images.cpu().permute(0  ,							2  ,							3  ,							1							).float().numpy()
													return images




							@staticmethod
							def  A__		(				snake_case_							)   ->       Dict:
													return 2.0 * images - 1.0




							@staticmethod
							def  A__		(				snake_case_							)   ->       List[str]:
													return (images / 2 + 0.5).clamp(0  ,							1							)




							@staticmethod
							def  A__		(				snake_case_							)   ->       PIL.Image.Image:
													__lowerCAmelCase        =       image.convert("""RGB"""							)
													return image




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       PIL.Image.Image:
													if height is None:
																			__lowerCAmelCase        =       image.height
													if width is None:
																			__lowerCAmelCase        =       image.width

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




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       torch.Tensor:
													__lowerCAmelCase        =       (PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       [image]
													elif not (isinstance(snake_case_  ,							snake_case_							) and all(isinstance(snake_case_  ,							snake_case_							) for i in image							)):
																			raise ValueError(
																			    f"""Input is in incorrect format: {[type(snake_case_							) for i in image]}. Currently, we only support {", ".join(snake_case_							)}"""							)

													if isinstance(image[0]  ,							PIL.Image.Image							):
																			if self.config.do_convert_rgb:
																									__lowerCAmelCase        =       [self.convert_to_rgb(snake_case_							) for i in image]
																			if self.config.do_resize:
																									__lowerCAmelCase        =       [self.resize(snake_case_  ,							snake_case_  ,							snake_case_							) for i in image]
																			__lowerCAmelCase        =       self.pil_to_numpy(snake_case_							)  # to np
																			__lowerCAmelCase        =       self.numpy_to_pt(snake_case_							)  # to pt

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

													if do_normalize:
																			__lowerCAmelCase        =       self.normalize(snake_case_							)

													return image




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = "pil"  ,							snake_case_ = None  ,							)   ->       Union[str, Any]:
													if not isinstance(snake_case_  ,							torch.Tensor							):
																			raise ValueError(
																			    f"""Input for postprocessing is in incorrect format: {type(snake_case_							)}. We only support pytorch tensor"""							)
													if output_type not in ["latent", "pt", "np", "pil"]:
																			__lowerCAmelCase        =       (
																			    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"""  ,							snake_case_  ,							standard_warn=snake_case_							)
																			__lowerCAmelCase        =       """np"""

													if output_type == "latent":
																			return image

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

													__lowerCAmelCase        =       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

													__lowerCAmelCase        =       self.pt_to_numpy(snake_case_							)

													if output_type == "np":
																			return image

													if output_type == "pil":
																			return self.numpy_to_pil(snake_case_							)
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						CONFIG_NAME




							@register_to_config
							def __init__(				self  ,							snake_case_ = True  ,							snake_case_ = 8  ,							snake_case_ = "lanczos"  ,							snake_case_ = True  ,							)   ->       Any:
													super().__init__()




							@staticmethod
							def  A__		(				snake_case_							)   ->       int:
													if images.ndim == 3:
																			__lowerCAmelCase        =       images[None, ...]
													__lowerCAmelCase        =       (images * 255).round().astype("""uint8"""							)
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			__lowerCAmelCase        =       [Image.fromarray(image.squeeze()  ,							mode="""L"""							) for image in images]
													else:
																			__lowerCAmelCase        =       [Image.fromarray(image[:, :, :3]							) for image in images]

													return pil_images




							@staticmethod
							def  A__		(				snake_case_							)   ->       Optional[int]:
													return image[:, :, 1] * 2**8 + image[:, :, 2]




							def  A__		(				self  ,							snake_case_							)   ->       Tuple:
													if images.ndim == 3:
																			__lowerCAmelCase        =       images[None, ...]
													__lowerCAmelCase        =       images[:, :, :, 3:]
													if images.shape[-1] == 6:
																			__lowerCAmelCase        =       (images_depth * 255).round().astype("""uint8"""							)
																			__lowerCAmelCase        =       [
																			    Image.fromarray(self.rgblike_to_depthmap(snake_case_							)  ,							mode="""I;16"""							) for image_depth in images_depth
																			]
													elif images.shape[-1] == 4:
																			__lowerCAmelCase        =       (images_depth * 65_535.0).astype(np.uintaa							)
																			__lowerCAmelCase        =       [Image.fromarray(snake_case_  ,							mode="""I;16"""							) for image_depth in images_depth]
													else:
																			raise Exception("""Not supported"""							)

													return pil_images




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = "pil"  ,							snake_case_ = None  ,							)   ->       int:
													if not isinstance(snake_case_  ,							torch.Tensor							):
																			raise ValueError(
																			    f"""Input for postprocessing is in incorrect format: {type(snake_case_							)}. We only support pytorch tensor"""							)
													if output_type not in ["latent", "pt", "np", "pil"]:
																			__lowerCAmelCase        =       (
																			    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"""  ,							snake_case_  ,							standard_warn=snake_case_							)
																			__lowerCAmelCase        =       """np"""

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

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

													__lowerCAmelCase        =       self.pt_to_numpy(snake_case_							)

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

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



"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
