
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   lowerCAmelCase__	(      a__     ):
		a__			:		torch.FloatTensor





class   lowerCAmelCase__	(      a__    ,      a__     ):






		@register_to_config
		def __init__(						self						:				Union[str, Any]				,     SCREAMING_SNAKE_CASE__						:				List[Any] = 3				,     SCREAMING_SNAKE_CASE__						:				Tuple = 3				,     SCREAMING_SNAKE_CASE__						:				Dict = ("DownEncoderBlock2D",)				,     SCREAMING_SNAKE_CASE__						:				Tuple = ("UpDecoderBlock2D",)				,     SCREAMING_SNAKE_CASE__						:				Tuple = (64,)				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any] = 1				,     SCREAMING_SNAKE_CASE__						:				int = "silu"				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any] = 3				,     SCREAMING_SNAKE_CASE__						:				Optional[Any] = 32				,     SCREAMING_SNAKE_CASE__						:				Optional[int] = 2_56				,     SCREAMING_SNAKE_CASE__						:				List[str] = 32				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any] = None				,     SCREAMING_SNAKE_CASE__						:				str = 0.18215				,     SCREAMING_SNAKE_CASE__						:				Dict = "group"				,     )	->				Union[str, Any]:
						super().__init__()

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

						__lowerCamelCase          =      vq_embed_dim if vq_embed_dim is not None else latent_channels

						__lowerCamelCase          =      nn.Convad(__a				,     __a				,     1    )
						__lowerCamelCase          =      VectorQuantizer(__a				,     __a				,     beta=0.25				,     remap=__a				,     sane_index_shape=__a    )
						__lowerCamelCase          =      nn.Convad(__a				,     __a				,     1    )

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






		@apply_forward_hook
		def 					__A			(						self						:				Optional[Any]				,     SCREAMING_SNAKE_CASE__						:				List[str]				,     SCREAMING_SNAKE_CASE__						:				str = True    )	->				VQEncoderOutput:
						__lowerCamelCase          =      self.encoder(__a    )
						__lowerCamelCase          =      self.quant_conv(__a    )

						if not return_dict:
										return (h,)

						return VQEncoderOutput(latents=__a    )






		@apply_forward_hook
		def 					__A			(						self						:				Tuple				,     SCREAMING_SNAKE_CASE__						:				List[Any]				,     SCREAMING_SNAKE_CASE__						:				Any = False				,     SCREAMING_SNAKE_CASE__						:				Dict = True    )	->				Union[DecoderOutput, torch.FloatTensor]:
						# also go through quantization layer
						if not force_not_quantize:
										__lowerCamelCase          =      self.quantize(__a    )
						else:
										__lowerCamelCase          =      h
						__lowerCamelCase          =      self.post_quant_conv(__a    )
						__lowerCamelCase          =      self.decoder(__a				,     quant if self.config.norm_type == '''spatial''' else None    )

						if not return_dict:
										return (dec,)

						return DecoderOutput(sample=__a    )







		def 					__A			(						self						:				Optional[int]				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any]				,     SCREAMING_SNAKE_CASE__						:				Tuple = True    )	->				Union[DecoderOutput, torch.FloatTensor]:
						__lowerCamelCase          =      sample
						__lowerCamelCase          =      self.encode(__a    ).latents
						__lowerCamelCase          =      self.decode(__a    ).sample

						if not return_dict:
										return (dec,)

						return DecoderOutput(sample=__a    )









'''simple docstring'''







import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a							)      ->				Tuple:
          if dst_width < 0 or dst_height < 0:
               raise ValueError("""Destination width/height should be > 0"""							)

          snake_case			:      str        =			img
          snake_case			:      str        =			img.shape[1]
          snake_case			:      Union[str, Any]        =			img.shape[0]
          snake_case			:      Optional[int]        =			dst_width
          snake_case			:      List[str]        =			dst_height

          snake_case			:      List[str]        =			self.src_w / self.dst_w
          snake_case			:      int        =			self.src_h / self.dst_h

          snake_case			:      List[Any]        =			(
              np.ones((self.dst_h, self.dst_w, 3)							,np.uinta							) * 255
          )







     def 			snake_case_      (				self							)      ->				int:
          for i in range(self.dst_h							):
               for j in range(self.dst_w							):
                    snake_case			:      Optional[Any]        =			self.img[self.get_y(__a							)][self.get_x(__a							)]







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_x * x							)







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_y * y							)


if __name__ == "__main__":
      lowercase,	lowercase      :   Optional[int]      =       800, 600
      lowercase      :   Union[str, Any]      =       imread("""image_data/lena.jpg""", 1)
      lowercase      :   int      =       NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()



