




'''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      _a    (      __a					):
       __a							:	Union[str, Any]  							=   CONFIG_NAME
       @register_to_config
       def __init__(      self		:      Dict  ,							lowercase		:      bool = True  ,							lowercase		:      int = 8  ,							lowercase		:      str = "lanczos"  ,							lowercase		:      bool = True  ,							lowercase		:      bool = False  ,							):



           '''simple docstring'''

           super().__init__()
       @staticmethod
       def        A (      lowercase		:      np.ndarray							):



           '''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(lowercase							) for image in images]

           return pil_images
       @staticmethod
       def        A (      lowercase		:      Union[List[PIL.Image.Image], PIL.Image.Image]							):



           '''simple docstring'''

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

           return images
       @staticmethod
       def        A (      lowercase		:      np.ndarray							):



           '''simple docstring'''

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

           UpperCAmelCase     			=						torch.from_numpy(images.transpose(0  ,							3  ,							1  ,							2							)							)
           return images
       @staticmethod
       def        A (      lowercase		:      torch.FloatTensor							):



           '''simple docstring'''

           UpperCAmelCase     			=						images.cpu().permute(0  ,							2  ,							3  ,							1							).float().numpy()
           return images
       @staticmethod
       def        A (      lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           return 2.0 * images - 1.0
       @staticmethod
       def        A (      lowercase		:      int							):



           '''simple docstring'''

           return (images / 2 + 0.5).clamp(0  ,							1							)
       @staticmethod
       def        A (      lowercase		:      PIL.Image.Image							):



           '''simple docstring'''

           UpperCAmelCase     			=						image.convert('''RGB'''							)
           return image
       def        A (      self		:      Dict  ,							lowercase		:      PIL.Image.Image  ,							lowercase		:      Optional[int] = None  ,							lowercase		:      Optional[int] = None  ,							):



           '''simple docstring'''

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

           UpperCAmelCase			,	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        A (      self		:      List[str]  ,							lowercase		:      Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,							lowercase		:      Optional[int] = None  ,							lowercase		:      Optional[int] = None  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						(PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(lowercase  ,							lowercase							):
               UpperCAmelCase     			=						[image]
           elif not (isinstance(lowercase  ,							lowercase							) and all(isinstance(lowercase  ,							lowercase							) for i in image							)):
               raise ValueError(
                   f"Input is in incorrect format: {[type(lowercase							) for i in image]}. Currently, we only support {', '.join(lowercase							)}"							)

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

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

           if do_normalize:
               UpperCAmelCase     			=						self.normalize(lowercase							)

           return image







       def        A (      self		:      Tuple  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      str = "pil"  ,							lowercase		:      Optional[List[bool]] = None  ,							):



           '''simple docstring'''

           if not isinstance(lowercase  ,							torch.Tensor							):
               raise ValueError(
                   f"Input for postprocessing is in incorrect format: {type(lowercase							)}. 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'''  ,							lowercase  ,							standard_warn=lowercase							)
               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(lowercase							)

           if output_type == "np":
               return image

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






class      _a    (      __a					):
       __a							:	List[str]  							=   CONFIG_NAME
       @register_to_config
       def __init__(      self		:      Optional[Any]  ,							lowercase		:      bool = True  ,							lowercase		:      int = 8  ,							lowercase		:      str = "lanczos"  ,							lowercase		:      bool = True  ,							):



           '''simple docstring'''

           super().__init__()
       @staticmethod
       def        A (      lowercase		:      List[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(image[:, :, :3]							) for image in images]

           return pil_images
       @staticmethod
       def        A (      lowercase		:      Dict							):



           '''simple docstring'''

           return image[:, :, 1] * 2**8 + image[:, :, 2]
       def        A (      self		:      Optional[Any]  ,							lowercase		:      Dict							):



           '''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(lowercase							)  ,							mode='''I;16'''							) for image_depth in images_depth
               ]
           elif images.shape[-1] == 4:
               UpperCAmelCase     			=						(images_depth * 6_5535.0).astype(np.uintaa							)
               UpperCAmelCase     			=						[Image.fromarray(lowercase  ,							mode='''I;16'''							) for image_depth in images_depth]
           else:
               raise Exception('''Not supported'''							)

           return pil_images







       def        A (      self		:      Union[str, Any]  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      str = "pil"  ,							lowercase		:      Optional[List[bool]] = None  ,							):



           '''simple docstring'''

           if not isinstance(lowercase  ,							torch.Tensor							):
               raise ValueError(
                   f"Input for postprocessing is in incorrect format: {type(lowercase							)}. 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'''  ,							lowercase  ,							standard_warn=lowercase							)
               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(lowercase							)

           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(lowercase							), self.numpy_to_depth(lowercase							)
           else:
               raise Exception(f"This type {output_type} is not supported"							)










'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




