


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						(     lowercase__  ):
						UpperCamelCase__							:						Tuple   =CONFIG_NAME






						@register_to_config
						def __init__(      self		:  Union[str, Any]    ,     lowercase_		:  bool = True    ,     lowercase_		:  int = 8    ,     lowercase_		:  str = "lanczos"    ,     lowercase_		:  bool = True    ,     lowercase_		:  bool = False    ,     )      ->		int:
													"""simple docstring"""




													super().__init__()






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  np.ndarray				)      ->		PIL.Image.Image:
													"""simple docstring"""




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

													return pil_images






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  Union[List[PIL.Image.Image], PIL.Image.Image]				)      ->		np.ndarray:
													"""simple docstring"""




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

													return images






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  np.ndarray				)      ->		torch.FloatTensor:
													"""simple docstring"""




													if images.ndim == 3:
																				_lowerCamelCase :	Tuple						=images[..., None]

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






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  torch.FloatTensor				)      ->		np.ndarray:
													"""simple docstring"""




													_lowerCamelCase :	str						=images.cpu().permute(0    ,     2    ,     3    ,     1				).float().numpy()
													return images






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  str				)      ->		Union[str, Any]:
													"""simple docstring"""




													return 2.0 * images - 1.0






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  Any				)      ->		Union[str, Any]:
													"""simple docstring"""




													return (images / 2 + 0.5).clamp(0    ,     1				)






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  PIL.Image.Image				)      ->		PIL.Image.Image:
													"""simple docstring"""




													_lowerCamelCase :	List[Any]						=image.convert('RGB'				)
													return image






						def  lowerCamelCase (      self		:  List[Any]    ,     lowercase_		:  PIL.Image.Image    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Optional[int] = None    ,     )      ->		PIL.Image.Image:
													"""simple docstring"""




													if height is None:
																				_lowerCamelCase :	Tuple						=image.height
													if width is None:
																				_lowerCamelCase :	Optional[int]						=image.width

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






						def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Optional[int] = None    ,     )      ->		torch.Tensor:
													"""simple docstring"""




													_lowerCamelCase :	List[Any]						=(PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(_A    ,     _A				):
																				_lowerCamelCase :	Optional[int]						=[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:
																											_lowerCamelCase :	Optional[Any]						=[self.convert_to_rgb(_A				) for i in image]
																				if self.config.do_resize:
																											_lowerCamelCase :	int						=[self.resize(_A    ,     _A    ,     _A				) for i in image]
																				_lowerCamelCase :	Dict						=self.pil_to_numpy(_A				)  # to np
																				_lowerCamelCase :	int						=self.numpy_to_pt(_A				)  # to pt

													elif isinstance(image[0]    ,     np.ndarray				):
																				_lowerCamelCase :	str						=np.concatenate(_A    ,     axis=0				) if image[0].ndim == 4 else np.stack(_A    ,     axis=0				)
																				_lowerCamelCase :	Any						=self.numpy_to_pt(_A				)
																				_lowerCamelCase :	Tuple						=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 :	Optional[Any]						=torch.cat(_A    ,     axis=0				) if image[0].ndim == 4 else torch.stack(_A    ,     axis=0				)
																				_lowerCamelCase :	List[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]
													_lowerCamelCase :	str						=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    ,     )
																				_lowerCamelCase :	str						=False

													if do_normalize:
																				_lowerCamelCase :	int						=self.normalize(_A				)

													return image






						def  lowerCamelCase (      self		:  Union[str, Any]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  str = "pil"    ,     lowercase_		:  Optional[List[bool]] = None    ,     )      ->		Optional[Any]:
													"""simple docstring"""




													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"]:
																				_lowerCamelCase :	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				)
																				_lowerCamelCase :	Union[str, Any]						='np'

													if output_type == "latent":
																				return image

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

													_lowerCamelCase :	Optional[int]						=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 :	Any						=self.pt_to_numpy(_A				)

													if output_type == "np":
																				return image

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




class A						(     lowercase__  ):
						UpperCamelCase__							:						Optional[int]   =CONFIG_NAME






						@register_to_config
						def __init__(      self		:  List[str]    ,     lowercase_		:  bool = True    ,     lowercase_		:  int = 8    ,     lowercase_		:  str = "lanczos"    ,     lowercase_		:  bool = True    ,     )      ->		Tuple:
													"""simple docstring"""




													super().__init__()






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  Union[str, Any]				)      ->		Optional[int]:
													"""simple docstring"""




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

													return pil_images






						@staticmethod
						def  lowerCamelCase (      lowercase_		:  Optional[int]				)      ->		Optional[Any]:
													"""simple docstring"""




													return image[:, :, 1] * 2**8 + image[:, :, 2]






						def  lowerCamelCase (      self		:  Union[str, Any]    ,     lowercase_		:  Dict				)      ->		Dict:
													"""simple docstring"""




													if images.ndim == 3:
																				_lowerCamelCase :	Tuple						=images[None, ...]
													_lowerCamelCase :	Any						=images[:, :, :, 3:]
													if images.shape[-1] == 6:
																				_lowerCamelCase :	List[str]						=(images_depth * 255).round().astype('uint8'				)
																				_lowerCamelCase :	Any						=[
																				    Image.fromarray(self.rgblike_to_depthmap(_A				)    ,     mode='I;16'				) for image_depth in images_depth
																				]
													elif images.shape[-1] == 4:
																				_lowerCamelCase :	Optional[Any]						=(images_depth * 6_5535.0).astype(np.uintaa				)
																				_lowerCamelCase :	Any						=[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]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  str = "pil"    ,     lowercase_		:  Optional[List[bool]] = None    ,     )      ->		int:
													"""simple docstring"""




													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"]:
																				_lowerCamelCase :	List[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				)
																				_lowerCamelCase :	List[str]						='np'

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

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

													_lowerCamelCase :	Optional[int]						=self.pt_to_numpy(_A				)

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






from collections.abc import Callable

lowerCamelCase		      = Callable[[float], float]  # type alias for a real -> real function




def 			a_   (					SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   int	,    ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=float(SCREAMING_SNAKE_CASE__  )
       while True:
              try:
                     _lowerCamelCase :	int						=prev_guess - function(SCREAMING_SNAKE_CASE__  ) / derivative(SCREAMING_SNAKE_CASE__  )
              except ZeroDivisionError:
                     raise ZeroDivisionError('Could not find root'  ) from None
              if abs(prev_guess - next_guess  ) < 10**-5:
                     return next_guess
              _lowerCamelCase :	Union[str, Any]						=next_guess




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return (x**3) - (2 * x) - 5




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


