





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













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}""")






