


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	(lowerCAmelCase__		):
    _lowercase    							=      CONFIG_NAME







    @register_to_config
    def __init__(	self:				str,A_:				Optional[int] = True,A_:				Any = 8,A_:				str = "lanczos",A_:				Optional[Any] = True,A_:				int = False,):






         '''simple docstring'''




         super().__init__()







    @staticmethod
    def 						snake_case_					(	A_:				Union[str, Any]			):






         '''simple docstring'''




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

         return pil_images







    @staticmethod
    def 						snake_case_					(	A_:				Any			):






         '''simple docstring'''




         if not isinstance(UpperCamelCase__,UpperCamelCase__			):
              __UpperCamelCase				=	[images]
         __UpperCamelCase				=	[np.array(UpperCamelCase__			).astype(np.floataa			) / 2_5_5.0 for image in images]
         __UpperCamelCase				=	np.stack(UpperCamelCase__,axis=0			)

         return images







    @staticmethod
    def 						snake_case_					(	A_:				Union[str, Any]			):






         '''simple docstring'''




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

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







    @staticmethod
    def 						snake_case_					(	A_:				Dict			):






         '''simple docstring'''




         __UpperCamelCase				=	images.cpu().permute(0,2,3,1			).float().numpy()
         return images







    @staticmethod
    def 						snake_case_					(	A_:				Union[str, Any]			):






         '''simple docstring'''




         return 2.0 * images - 1.0







    @staticmethod
    def 						snake_case_					(	A_:				Optional[int]			):






         '''simple docstring'''




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







    @staticmethod
    def 						snake_case_					(	A_:				int			):






         '''simple docstring'''




         __UpperCamelCase				=	image.convert('RGB'			)
         return image







    def 						snake_case_					(	self:				List[str],A_:				int,A_:				Dict = None,A_:				List[Any] = None,):






         '''simple docstring'''




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

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







    def 						snake_case_					(	self:				Tuple,A_:				Any,A_:				Union[str, Any] = None,A_:				Optional[Any] = None,):






         '''simple docstring'''




         __UpperCamelCase				=	(PIL.Image.Image, np.ndarray, torch.Tensor)
         if isinstance(UpperCamelCase__,UpperCamelCase__			):
              __UpperCamelCase				=	[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:
                   __UpperCamelCase				=	[self.convert_to_rgb(UpperCamelCase__			) for i in image]
              if self.config.do_resize:
                   __UpperCamelCase				=	[self.resize(UpperCamelCase__,UpperCamelCase__,UpperCamelCase__			) for i in image]
              __UpperCamelCase				=	self.pil_to_numpy(UpperCamelCase__			)  # to np
              __UpperCamelCase				=	self.numpy_to_pt(UpperCamelCase__			)  # to pt

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

         if do_normalize:
              __UpperCamelCase				=	self.normalize(UpperCamelCase__			)

         return image







    def 						snake_case_					(	self:				str,A_:				List[str],A_:				Optional[int] = "pil",A_:				Optional[Any] = 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"]:
              __UpperCamelCase				=	(
                  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__			)
              __UpperCamelCase				=	"np"

         if output_type == "latent":
              return image

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

         __UpperCamelCase				=	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

         __UpperCamelCase				=	self.pt_to_numpy(UpperCamelCase__			)

         if output_type == "np":
              return image

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



class 					__lowerCamelCase	(lowerCAmelCase__		):
    _lowercase    							=      CONFIG_NAME







    @register_to_config
    def __init__(	self:				Optional[int],A_:				int = True,A_:				str = 8,A_:				Optional[Any] = "lanczos",A_:				Any = True,):






         '''simple docstring'''




         super().__init__()







    @staticmethod
    def 						snake_case_					(	A_:				Dict			):






         '''simple docstring'''




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

         return pil_images







    @staticmethod
    def 						snake_case_					(	A_:				Any			):






         '''simple docstring'''




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







    def 						snake_case_					(	self:				int,A_:				Union[str, Any]			):






         '''simple docstring'''




         if images.ndim == 3:
              __UpperCamelCase				=	images[None, ...]
         __UpperCamelCase				=	images[:, :, :, 3:]
         if images.shape[-1] == 6:
              __UpperCamelCase				=	(images_depth * 255).round().astype('uint8'			)
              __UpperCamelCase				=	[
                  Image.fromarray(self.rgblike_to_depthmap(UpperCamelCase__			),mode='I;16'			) for image_depth in images_depth
              ]
         elif images.shape[-1] == 4:
              __UpperCamelCase				=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
              __UpperCamelCase				=	[Image.fromarray(UpperCamelCase__,mode='I;16'			) for image_depth in images_depth]
         else:
              raise Exception('Not supported'			)

         return pil_images







    def 						snake_case_					(	self:				str,A_:				Union[str, Any],A_:				List[Any] = "pil",A_:				str = 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"]:
              __UpperCamelCase				=	(
                  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__			)
              __UpperCamelCase				=	"np"

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

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

         __UpperCamelCase				=	self.pt_to_numpy(UpperCamelCase__			)

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






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


