






'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							42
class 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase      ):


    '''simple docstring'''

    @register_to_config
    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int = 3				,     __lowerCamelCase     :     int = 3				,     __lowerCamelCase     :     Tuple[str] = ("DownEncoderBlock2D",)				,     __lowerCamelCase     :     Tuple[str] = ("UpDecoderBlock2D",)				,     __lowerCamelCase     :     Tuple[int] = (64,)				,     __lowerCamelCase     :     int = 1				,     __lowerCamelCase     :     str = "silu"				,     __lowerCamelCase     :     int = 3				,     __lowerCamelCase     :     int = 32				,     __lowerCamelCase     :     int = 256				,     __lowerCamelCase     :     int = 32				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     float = 0.1_8_2_1_5				,     __lowerCamelCase     :     str = "group"				,     )							->							Tuple:

     '''simple docstring'''




     super().__init__()

     # pass init params to Encoder
     lowerCamelCase__  =     Encoder(
         in_channels=__lowerCamelCase				,     out_channels=__lowerCamelCase				,     down_block_types=__lowerCamelCase				,     block_out_channels=__lowerCamelCase				,     layers_per_block=__lowerCamelCase				,     act_fn=__lowerCamelCase				,     norm_num_groups=__lowerCamelCase				,     double_z=__lowerCamelCase				,     )

     lowerCamelCase__  =     vq_embed_dim if vq_embed_dim is not None else latent_channels

     lowerCamelCase__  =     nn.Convad(__lowerCamelCase				,     __lowerCamelCase				,     1     )
     lowerCamelCase__  =     VectorQuantizer(__lowerCamelCase				,     __lowerCamelCase				,     beta=0.2_5				,     remap=__lowerCamelCase				,     sane_index_shape=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Convad(__lowerCamelCase				,     __lowerCamelCase				,     1     )

     # pass init params to Decoder
     lowerCamelCase__  =     Decoder(
         in_channels=__lowerCamelCase				,     out_channels=__lowerCamelCase				,     up_block_types=__lowerCamelCase				,     block_out_channels=__lowerCamelCase				,     layers_per_block=__lowerCamelCase				,     act_fn=__lowerCamelCase				,     norm_num_groups=__lowerCamelCase				,     norm_type=__lowerCamelCase				,     )

    @apply_forward_hook
    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     torch.FloatTensor				,     __lowerCamelCase     :     bool = True     )							->							VQEncoderOutput:

     '''simple docstring'''




     lowerCamelCase__  =     self.encoder(__lowerCamelCase     )
     lowerCamelCase__  =     self.quant_conv(__lowerCamelCase     )

     if not return_dict:
      return (h,)

     return VQEncoderOutput(latents=__lowerCamelCase     )

    @apply_forward_hook
    def    a__				(					self     :     int				,     __lowerCamelCase     :     torch.FloatTensor				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = True     )							->							Union[DecoderOutput, torch.FloatTensor]:

     '''simple docstring'''




     if not force_not_quantize:
      lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     self.quantize(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     h
     lowerCamelCase__  =     self.post_quant_conv(__lowerCamelCase     )
     lowerCamelCase__  =     self.decoder(__lowerCamelCase				,     quant if self.config.norm_type == "spatial" else None     )

     if not return_dict:
      return (dec,)

     return DecoderOutput(sample=__lowerCamelCase     )




    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     torch.FloatTensor				,     __lowerCamelCase     :     bool = True     )							->							Union[DecoderOutput, torch.FloatTensor]:

     '''simple docstring'''




     lowerCamelCase__  =     sample
     lowerCamelCase__  =     self.encode(__lowerCamelCase     ).latents
     lowerCamelCase__  =     self.decode(__lowerCamelCase     ).sample

     if not return_dict:
      return (dec,)

     return DecoderOutput(sample=__lowerCamelCase     )









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

