code: 
  
  
  
  
  
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  def   _A (	SCREAMING_SNAKE_CASE  :     torch.Tensor , SCREAMING_SNAKE_CASE  :     int , SCREAMING_SNAKE_CASE  :     bool = False , SCREAMING_SNAKE_CASE  :     float = 1 , SCREAMING_SNAKE_CASE  :     float = 1 , SCREAMING_SNAKE_CASE  :     int = 10_000 , ):
  
  
  
  
  
    """simple docstring"""
  
  
  
    assert len(timesteps.shape		) == 1, "Timesteps should be a 1d-array"
  
    a__			:      Optional[int]            =embedding_dim // 2
    a__			:      Tuple            =-math.log(SCREAMING_SNAKE_CASE		) * torch.arange(
        start=0 , end=SCREAMING_SNAKE_CASE , dtype=torch.floataa , device=timesteps.device		)
    a__			:      Any            =exponent / (half_dim - downscale_freq_shift)
  
    a__			:      List[Any]            =torch.exp(SCREAMING_SNAKE_CASE		)
    a__			:      Union[str, Any]            =timesteps[:, None].float() * emb[None, :]
  
    # scale embeddings
    a__			:      Tuple            =scale * emb
  
    # concat sine and cosine embeddings
    a__			:      Dict            =torch.cat([torch.sin(SCREAMING_SNAKE_CASE		), torch.cos(SCREAMING_SNAKE_CASE		)] , dim=-1		)
  
    # flip sine and cosine embeddings
    if flip_sin_to_cos:
      a__			:      Optional[int]            =torch.cat([emb[:, half_dim:], emb[:, :half_dim]] , dim=-1		)
  
    # zero pad
    if embedding_dim % 2 == 1:
      a__			:      Tuple            =torch.nn.functional.pad(SCREAMING_SNAKE_CASE , (0, 1, 0, 0)		)
    return emb
  def   _A (	SCREAMING_SNAKE_CASE  :     Dict , SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     List[Any]=False , SCREAMING_SNAKE_CASE  :     str=0		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      List[Any]            =np.arange(SCREAMING_SNAKE_CASE , dtype=np.floataa		)
    a__			:      str            =np.arange(SCREAMING_SNAKE_CASE , dtype=np.floataa		)
    a__			:      List[Any]            =np.meshgrid(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)  # here w goes first
    a__			:      Union[str, Any]            =np.stack(SCREAMING_SNAKE_CASE , axis=0		)
  
    a__			:      List[Any]            =grid.reshape([2, 1, grid_size, grid_size]		)
    a__			:      Optional[Any]            =get_ad_sincos_pos_embed_from_grid(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    if cls_token and extra_tokens > 0:
      a__			:      Tuple            =np.concatenate([np.zeros([extra_tokens, embed_dim]		), pos_embed] , axis=0		)
    return pos_embed
  def   _A (	SCREAMING_SNAKE_CASE  :     Optional[int] , SCREAMING_SNAKE_CASE  :     Tuple		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if embed_dim % 2 != 0:
      raise ValueError("embed_dim must be divisible by 2"		)
  
    # use half of dimensions to encode grid_h
    a__			:      Any            =get_ad_sincos_pos_embed_from_grid(embed_dim // 2 , grid[0]		)  # (H*W, D/2)
    a__			:      Any            =get_ad_sincos_pos_embed_from_grid(embed_dim // 2 , grid[1]		)  # (H*W, D/2)
  
    a__			:      str            =np.concatenate([emb_h, emb_w] , axis=1		)  # (H*W, D)
    return emb
  def   _A (	SCREAMING_SNAKE_CASE  :     List[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if embed_dim % 2 != 0:
      raise ValueError("embed_dim must be divisible by 2"		)
  
    a__			:      List[Any]            =np.arange(embed_dim // 2 , dtype=np.floataa		)
    omega /= embed_dim / 2.0
    a__			:      Union[str, Any]            =1.0 / 10_000**omega  # (D/2,)
  
    a__			:      str            =pos.reshape(-1		)  # (M,)
    a__			:      Optional[Any]            =np.einsum("m,d->md" , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)  # (M, D/2), outer product
  
    a__			:      List[str]            =np.sin(SCREAMING_SNAKE_CASE		)  # (M, D/2)
    a__			:      Dict            =np.cos(SCREAMING_SNAKE_CASE		)  # (M, D/2)
  
    a__			:      Union[str, Any]            =np.concatenate([emb_sin, emb_cos] , axis=1		)  # (M, D)
    return emb
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=2_2_4	,   lowerCAmelCase__=2_2_4	,   lowerCAmelCase__=1_6	,   lowerCAmelCase__=3	,   lowerCAmelCase__=7_6_8	,   lowerCAmelCase__=False	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   )     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      List[Any]            =(height // patch_size) * (width // patch_size)
          a__			:      int            =flatten
          a__			:      Dict            =layer_norm
  
          a__			:      Tuple            =nn.Convad(
              lowerCAmelCase__	,   lowerCAmelCase__	,   kernel_size=(patch_size, patch_size)	,   stride=lowerCAmelCase__	,   bias=lowerCAmelCase__							)
          if layer_norm:
            a__			:      Dict            =nn.LayerNorm(lowerCAmelCase__	,   elementwise_affine=lowerCAmelCase__	,   eps=1E-6							)
          else:
            a__			:      Union[str, Any]            =None
  
          a__			:      Optional[int]            =get_ad_sincos_pos_embed(lowerCAmelCase__	,   int(num_patches**0.5							)							)
          self.register_buffer("pos_embed"	,   torch.from_numpy(lowerCAmelCase__							).float().unsqueeze(0							)	,   persistent=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =self.proj(lowerCAmelCase__							)
          if self.flatten:
            a__			:      int            =latent.flatten(2							).transpose(1	,   2							)  # BCHW -> BNC
          if self.layer_norm:
            a__			:      Tuple            =self.norm(lowerCAmelCase__							)
          return latent + self.pos_embed
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = "silu"	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__=None	,   )     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      Any            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
  
          if cond_proj_dim is not None:
            a__			:      Optional[Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__	,   bias=lowerCAmelCase__							)
          else:
            a__			:      List[Any]            =None
  
          a__			:      Optional[int]            =get_activation(lowerCAmelCase__							)
  
          if out_dim is not None:
            a__			:      Union[str, Any]            =out_dim
          else:
            a__			:      int            =time_embed_dim
          a__			:      Union[str, Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
  
          if post_act_fn is None:
            a__			:      int            =None
          else:
            a__			:      Any            =get_activation(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if condition is not None:
            a__			:      Dict            =sample + self.cond_proj(lowerCAmelCase__							)
          a__			:      Any            =self.linear_a(lowerCAmelCase__							)
  
          if self.act is not None:
            a__			:      Dict            =self.act(lowerCAmelCase__							)
  
          a__			:      str            =self.linear_a(lowerCAmelCase__							)
  
          if self.post_act is not None:
            a__			:      Optional[int]            =self.post_act(lowerCAmelCase__							)
          return sample
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      str            =num_channels
          a__			:      Optional[int]            =flip_sin_to_cos
          a__			:      int            =downscale_freq_shift
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =get_timestep_embedding(
              lowerCAmelCase__	,   self.num_channels	,   flip_sin_to_cos=self.flip_sin_to_cos	,   downscale_freq_shift=self.downscale_freq_shift	,   )
          return t_emb
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 2_5_6	,   lowerCAmelCase__ = 1.0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   lowerCAmelCase__=False							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      str            =nn.Parameter(torch.randn(lowerCAmelCase__							) * scale	,   requires_grad=lowerCAmelCase__							)
          a__			:      Optional[Any]            =log
          a__			:      List[Any]            =flip_sin_to_cos
  
          if set_W_to_weight:
            # to delete later
            a__			:      Optional[int]            =nn.Parameter(torch.randn(lowerCAmelCase__							) * scale	,   requires_grad=lowerCAmelCase__							)
  
            a__			:      int            =self.W
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.log:
            a__			:      List[Any]            =torch.log(lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =x[:, None] * self.weight[None, :] * 2 * np.pi
  
          if self.flip_sin_to_cos:
            a__			:      List[str]            =torch.cat([torch.cos(lowerCAmelCase__							), torch.sin(lowerCAmelCase__							)]	,   dim=-1							)
          else:
            a__			:      Optional[int]            =torch.cat([torch.sin(lowerCAmelCase__							), torch.cos(lowerCAmelCase__							)]	,   dim=-1							)
          return out
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   )     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      Optional[Any]            =height
          a__			:      Tuple            =width
          a__			:      Dict            =num_embed
          a__			:      int            =embed_dim
  
          a__			:      Optional[Any]            =nn.Embedding(self.num_embed	,   lowerCAmelCase__							)
          a__			:      Union[str, Any]            =nn.Embedding(self.height	,   lowerCAmelCase__							)
          a__			:      List[Any]            =nn.Embedding(self.width	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =self.emb(lowerCAmelCase__							)
  
          a__			:      List[Any]            =self.height_emb(torch.arange(self.height	,   device=index.device							).view(1	,   self.height							)							)
  
          # 1 x H x D -> 1 x H x 1 x D
          a__			:      List[str]            =height_emb.unsqueeze(2							)
  
          a__			:      Optional[int]            =self.width_emb(torch.arange(self.width	,   device=index.device							).view(1	,   self.width							)							)
  
          # 1 x W x D -> 1 x 1 x W x D
          a__			:      Union[str, Any]            =width_emb.unsqueeze(1							)
  
          a__			:      Tuple            =height_emb + width_emb
  
          # 1 x H x W x D -> 1 x L xD
          a__			:      Optional[int]            =pos_emb.view(1	,   self.height * self.width	,   -1							)
  
          a__			:      int            =emb + pos_emb[:, : emb.shape[1], :]
  
          return emb
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      Any            =dropout_prob > 0
          a__			:      Tuple            =nn.Embedding(num_classes + use_cfg_embedding	,   lowerCAmelCase__							)
          a__			:      Union[str, Any]            =num_classes
          a__			:      Optional[int]            =dropout_prob
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if force_drop_ids is None:
            a__			:      Any            =torch.rand(labels.shape[0]	,   device=labels.device							) < self.dropout_prob
          else:
            a__			:      Dict            =torch.tensor(force_drop_ids == 1							)
          a__			:      Any            =torch.where(lowerCAmelCase__	,   self.num_classes	,   lowerCAmelCase__							)
          return labels
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =self.dropout_prob > 0
          if (self.training and use_dropout) or (force_drop_ids is not None):
            a__			:      Any            =self.token_drop(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Dict            =self.embedding_table(lowerCAmelCase__							)
          return embeddings
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 1_0_2_4	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 1_0	,   )     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      List[Any]            =num_image_text_embeds
          a__			:      Union[str, Any]            =nn.Linear(lowerCAmelCase__	,   self.num_image_text_embeds * cross_attention_dim							)
          a__			:      Any            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =text_embeds.shape[0]
  
          # image
          a__			:      Dict            =self.image_embeds(lowerCAmelCase__							)
          a__			:      Optional[Any]            =image_text_embeds.reshape(lowerCAmelCase__	,   self.num_image_text_embeds	,   -1							)
  
          # text
          a__			:      List[Any]            =self.text_proj(lowerCAmelCase__							)
  
          return torch.cat([image_text_embeds, text_embeds]	,   dim=1							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 3_2	,   )     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      List[Any]            =num_image_text_embeds
          a__			:      Any            =nn.Linear(lowerCAmelCase__	,   self.num_image_text_embeds * cross_attention_dim							)
          a__			:      str            =nn.LayerNorm(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =image_embeds.shape[0]
  
          # image
          a__			:      Tuple            =self.image_embeds(lowerCAmelCase__							)
          a__			:      Optional[int]            =image_embeds.reshape(lowerCAmelCase__	,   self.num_image_text_embeds	,   -1							)
          a__			:      Optional[int]            =self.norm(lowerCAmelCase__							)
          return image_embeds
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__=0.1							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      List[Any]            =Timesteps(num_channels=2_5_6	,   flip_sin_to_cos=lowerCAmelCase__	,   downscale_freq_shift=1							)
          a__			:      int            =TimestepEmbedding(in_channels=2_5_6	,   time_embed_dim=lowerCAmelCase__							)
          a__			:      List[Any]            =LabelEmbedding(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =self.time_proj(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__							)							)  # (N, D)
  
          a__			:      List[Any]            =self.class_embedder(lowerCAmelCase__							)  # (N, D)
  
          a__			:      int            =timesteps_emb + class_labels  # (N, D)
  
          return conditioning
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = 6_4							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      int            =nn.LayerNorm(lowerCAmelCase__							)
          a__			:      Tuple            =AttentionPooling(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Optional[Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Any            =nn.LayerNorm(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =self.norma(lowerCAmelCase__							)
          a__			:      int            =self.pool(lowerCAmelCase__							)
          a__			:      Optional[Any]            =self.proj(lowerCAmelCase__							)
          a__			:      Optional[int]            =self.norma(lowerCAmelCase__							)
          return hidden_states
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 1_5_3_6							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      Dict            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Union[str, Any]            =nn.LayerNorm(lowerCAmelCase__							)
          a__			:      str            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =self.text_proj(lowerCAmelCase__							)
          a__			:      Optional[int]            =self.text_norm(lowerCAmelCase__							)
  
          # image
          a__			:      Any            =self.image_proj(lowerCAmelCase__							)
  
          return time_image_embeds + time_text_embeds
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 1_5_3_6							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      str            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Tuple            =nn.LayerNorm(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =self.image_proj(lowerCAmelCase__							)
          a__			:      Dict            =self.image_norm(lowerCAmelCase__							)
          return time_image_embeds
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = 7_6_8	,   lowerCAmelCase__ = 1_5_3_6							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      Union[str, Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Union[str, Any]            =nn.LayerNorm(lowerCAmelCase__							)
          a__			:      int            =nn.Sequential(
              nn.Convad(3	,   1_6	,   3	,   padding=1							)	,   nn.SiLU()	,   nn.Convad(1_6	,   1_6	,   3	,   padding=1							)	,   nn.SiLU()	,   nn.Convad(1_6	,   3_2	,   3	,   padding=1	,   stride=2							)	,   nn.SiLU()	,   nn.Convad(3_2	,   3_2	,   3	,   padding=1							)	,   nn.SiLU()	,   nn.Convad(3_2	,   9_6	,   3	,   padding=1	,   stride=2							)	,   nn.SiLU()	,   nn.Convad(9_6	,   9_6	,   3	,   padding=1							)	,   nn.SiLU()	,   nn.Convad(9_6	,   2_5_6	,   3	,   padding=1	,   stride=2							)	,   nn.SiLU()	,   nn.Convad(2_5_6	,   4	,   3	,   padding=1							)	,   )
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.image_proj(lowerCAmelCase__							)
          a__			:      Optional[int]            =self.image_norm(lowerCAmelCase__							)
          a__			:      List[Any]            =self.input_hint_block(lowerCAmelCase__							)
          return time_image_embeds, hint
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
          a__			:      Dict            =dtype
          a__			:      Dict            =nn.Parameter(torch.randn(1	,   lowerCAmelCase__							) / embed_dim**0.5							)
          a__			:      Union[str, Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__	,   dtype=self.dtype							)
          a__			:      Tuple            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__	,   dtype=self.dtype							)
          a__			:      Union[str, Any]            =nn.Linear(lowerCAmelCase__	,   lowerCAmelCase__	,   dtype=self.dtype							)
          a__			:      str            =num_heads
          a__			:      Optional[Any]            =embed_dim // self.num_heads
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__      ,							a__      ,							a__			:      Optional[Any]            =x.size()
  
          def shape(lowerCAmelCase__							):
            # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
            a__			:      Dict            =x.view(lowerCAmelCase__	,   -1	,   self.num_heads	,   self.dim_per_head							)
            # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
            a__			:      Union[str, Any]            =x.transpose(1	,   2							)
            # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
            a__			:      Any            =x.reshape(bs * self.num_heads	,   -1	,   self.dim_per_head							)
            # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
            a__			:      Union[str, Any]            =x.transpose(1	,   2							)
            return x
  
          a__			:      int            =x.mean(dim=1	,   keepdim=lowerCAmelCase__							) + self.positional_embedding.to(x.dtype							)
          a__			:      int            =torch.cat([class_token, x]	,   dim=1							)  # (bs, length+1, width)
  
          # (bs*n_heads, class_token_length, dim_per_head)
          a__			:      int            =shape(self.q_proj(lowerCAmelCase__							)							)
          # (bs*n_heads, length+class_token_length, dim_per_head)
          a__			:      Optional[Any]            =shape(self.k_proj(lowerCAmelCase__							)							)
          a__			:      Optional[int]            =shape(self.v_proj(lowerCAmelCase__							)							)
  
          # (bs*n_heads, class_token_length, length+class_token_length):
          a__			:      str            =1 / math.sqrt(math.sqrt(self.dim_per_head							)							)
          a__			:      int            =torch.einsum("bct,bcs->bts"	,   q * scale	,   k * scale							)  # More stable with f16 than dividing afterwards
          a__			:      List[str]            =torch.softmax(weight.float()	,   dim=-1							).type(weight.dtype							)
  
          # (bs*n_heads, dim_per_head, class_token_length)
          a__			:      Union[str, Any]            =torch.einsum("bts,bcs->bct"	,   lowerCAmelCase__	,   lowerCAmelCase__							)
  
          # (bs, length+1, width)
          a__			:      str            =a.reshape(lowerCAmelCase__	,   -1	,   1							).transpose(1	,   2							)
  
          return a[:, 0, :]  # cls_token
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
    a__			:      str            =[]
  
    for character in message_part:
      tmp.append(character_to_number[character]		)
  
    for each in tmp:
      one += each[0]
      two += each[1]
      three += each[2]
  
    return one + two + three
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__			:      Any            ="", ""
    a__			:      Optional[int]            =[]
  
    for character in message_part:
      this_part += character_to_number[character]
  
    for digit in this_part:
      tmp += digit
      if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
        result.append(SCREAMING_SNAKE_CASE		)
        a__			:      str            =""
  
    return result[0], result[1], result[2]
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
    a__			:      List[str]            =message.replace(" " , ""		).upper()
  
    # Check length and characters
    if len(SCREAMING_SNAKE_CASE		) != 27:
      raise KeyError("Length of alphabet has to be 27."		)
    for each in message:
      if each not in alphabet:
        raise ValueError("Each message character has to be included in alphabet!"		)
  
      # Generate dictionares
    a__			:      Any            =(
        "111",
        "112",
        "113",
        "121",
        "122",
        "123",
        "131",
        "132",
        "133",
        "211",
        "212",
        "213",
        "221",
        "222",
        "223",
        "231",
        "232",
        "233",
        "311",
        "312",
        "313",
        "321",
        "322",
        "323",
        "331",
        "332",
        "333",
    )
    a__			:      Tuple            ={}
    a__			:      List[str]            ={}
    for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
      a__			:      Dict            =number
      a__			:      Union[str, Any]            =letter
  
    return message, alphabet, character_to_number, number_to_character
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__      ,							a__			:      Union[str, Any]            ="", ""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      encrypted_numeric += __encrypt_part(
          message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
    return encrypted
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[Any]            =[]
    a__			:      Any            =""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
      for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
        decrypted_numeric.append(a[j] + b[j] + c[j]		)
  
    for each in decrypted_numeric:
      decrypted += number_to_character[each]
  
    return decrypted
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
       UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
  
  
  