






"""simple docstring"""
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class      UpperCamelCase_			(nn.Module	):





	def __init__( self   :					int      ,      lowerCAmelCase_   :					int = 16      ,      lowerCAmelCase_   :					int = 88      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					float = 0.0      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "geglu"      ,      lowerCAmelCase_   :					Optional[int] = None      ,      )		->     int:
				super().__init__()
				UpperCAmelCase_	:					Optional[Any]     	=   nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowerCAmelCase_      ,      attention_head_dim=lowerCAmelCase_      ,      in_channels=lowerCAmelCase_      ,      num_layers=lowerCAmelCase_      ,      dropout=lowerCAmelCase_      ,      norm_num_groups=lowerCAmelCase_      ,      cross_attention_dim=lowerCAmelCase_      ,      attention_bias=lowerCAmelCase_      ,      sample_size=lowerCAmelCase_      ,      num_vector_embeds=lowerCAmelCase_      ,      activation_fn=lowerCAmelCase_      ,      num_embeds_ada_norm=lowerCAmelCase_      ,      )
				        for _ in range(2		)
				    ]		)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				UpperCAmelCase_	:					Union[str, Any]     	=   0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				UpperCAmelCase_	:					List[str]     	=   [77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				UpperCAmelCase_	:					Tuple     	=   [1, 0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Dict:
				UpperCAmelCase_	:					Dict     	=   hidden_states

				UpperCAmelCase_	:					Optional[Any]     	=   []
				UpperCAmelCase_	:					Dict     	=   0
				# attention_mask is not used yet
				for i in range(2		):
							# for each of the two transformers, pass the corresponding condition tokens
							UpperCAmelCase_	:					List[Any]     	=   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							UpperCAmelCase_	:					int     	=   self.transformer_index_for_condition[i]
							UpperCAmelCase_	:					Union[str, Any]     	=   self.transformers[transformer_index](
							    lowerCAmelCase_      ,      encoder_hidden_states=lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      cross_attention_kwargs=lowerCAmelCase_      ,      return_dict=lowerCAmelCase_      ,      )[0]
							encoded_states.append(encoded_state - input_states		)
							tokens_start += self.condition_lengths[i]

				UpperCAmelCase_	:					str     	=   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				UpperCAmelCase_	:					str     	=   output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowerCAmelCase_		)









'''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()



