code: 
  
  
  
  
  
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = False      ,		lowercase_ = 1      ,		lowercase_ = 1      ,		lowercase_ = 10000      ,		)			->						str:
         assert len(timesteps.shape  ) == 1, "Timesteps should be a 1d-array"
  
         _snake_case      :							Optional[int]     				=						embedding_dim // 2
         _snake_case      :							Tuple     				=						-math.log(lowercase_  ) * torch.arange(
             start=0      ,		end=lowercase_      ,		dtype=torch.floataa      ,		device=timesteps.device  )
         _snake_case      :							Any     				=						exponent / (half_dim - downscale_freq_shift)
  
         _snake_case      :							List[Any]     				=						torch.exp(lowercase_  )
         _snake_case      :							Union[str, Any]     				=						timesteps[:, None].float() * emb[None, :]
  
         # scale embeddings
         _snake_case      :							Tuple     				=						scale * emb
  
         # concat sine and cosine embeddings
         _snake_case      :							Dict     				=						torch.cat([torch.sin(lowercase_  ), torch.cos(lowercase_  )]      ,		dim=-1  )
  
         # flip sine and cosine embeddings
         if flip_sin_to_cos:
                _snake_case      :							Optional[int]     				=						torch.cat([emb[:, half_dim:], emb[:, :half_dim]]      ,		dim=-1  )
  
         # zero pad
         if embedding_dim % 2 == 1:
                _snake_case      :							Tuple     				=						torch.nn.functional.pad(lowercase_      ,		(0, 1, 0, 0)  )
         return emb
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=False      ,		lowercase_=0  )			->						Optional[int]:
         _snake_case      :							List[Any]     				=						np.arange(lowercase_      ,		dtype=np.floataa  )
         _snake_case      :							str     				=						np.arange(lowercase_      ,		dtype=np.floataa  )
         _snake_case      :							List[Any]     				=						np.meshgrid(lowercase_      ,		lowercase_  )  # here w goes first
         _snake_case      :							Union[str, Any]     				=						np.stack(lowercase_      ,		axis=0  )
  
         _snake_case      :							List[Any]     				=						grid.reshape([2, 1, grid_size, grid_size]  )
         _snake_case      :							Optional[Any]     				=						get_ad_sincos_pos_embed_from_grid(lowercase_      ,		lowercase_  )
         if cls_token and extra_tokens > 0:
                _snake_case      :							Tuple     				=						np.concatenate([np.zeros([extra_tokens, embed_dim]  ), pos_embed]      ,		axis=0  )
         return pos_embed
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						List[str]:
  
  
  
         if embed_dim % 2 != 0:
                raise ValueError('''embed_dim must be divisible by 2'''  )
  
         # use half of dimensions to encode grid_h
         _snake_case      :							Any     				=						get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,		grid[0]  )  # (H*W, D/2)
         _snake_case      :							Any     				=						get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,		grid[1]  )  # (H*W, D/2)
  
         _snake_case      :							str     				=						np.concatenate([emb_h, emb_w]      ,		axis=1  )  # (H*W, D)
         return emb
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Tuple:
  
  
  
         if embed_dim % 2 != 0:
                raise ValueError('''embed_dim must be divisible by 2'''  )
  
         _snake_case      :							List[Any]     				=						np.arange(embed_dim // 2      ,		dtype=np.floataa  )
         omega /= embed_dim / 2.0
         _snake_case      :							Union[str, Any]     				=						1.0 / 10000**omega  # (D/2,)
  
         _snake_case      :							str     				=						pos.reshape(-1  )  # (M,)
         _snake_case      :							Optional[Any]     				=						np.einsum('''m,d->md'''      ,		lowercase_      ,		lowercase_  )  # (M, D/2), outer product
  
         _snake_case      :							List[str]     				=						np.sin(lowercase_  )  # (M, D/2)
         _snake_case      :							Dict     				=						np.cos(lowercase_  )  # (M, D/2)
  
         _snake_case      :							Union[str, Any]     				=						np.concatenate([emb_sin, emb_cos]      ,		axis=1  )  # (M, D)
         return emb
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_=224     ,  lowercase_=224     ,  lowercase_=16     ,  lowercase_=3     ,  lowercase_=768     ,  lowercase_=False     ,  lowercase_=True     ,  lowercase_=True     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							List[Any]     				=						(height // patch_size) * (width // patch_size)
               _snake_case      :							int     				=						flatten
               _snake_case      :							Dict     				=						layer_norm
  
               _snake_case      :							Tuple     				=						nn.Convad(
                   lowerCAmelCase__     ,  lowerCAmelCase__     ,  kernel_size=(patch_size, patch_size)     ,  stride=lowerCAmelCase__     ,  bias=lowerCAmelCase__					)
               if layer_norm:
                      _snake_case      :							Dict     				=						nn.LayerNorm(lowerCAmelCase__     ,  elementwise_affine=lowerCAmelCase__     ,  eps=1E-6					)
               else:
                      _snake_case      :							Union[str, Any]     				=						None
  
               _snake_case      :							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    __a (					self     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						self.proj(lowerCAmelCase__					)
               if self.flatten:
                      _snake_case      :							int     				=						latent.flatten(2					).transpose(1     ,  2					)  # BCHW -> BNC
               if self.layer_norm:
                      _snake_case      :							Tuple     				=						self.norm(lowerCAmelCase__					)
               return latent + self.pos_embed
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = "silu"     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_=None     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							Any     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
  
               if cond_proj_dim is not None:
                      _snake_case      :							Optional[Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__     ,  bias=lowerCAmelCase__					)
               else:
                      _snake_case      :							List[Any]     				=						None
  
               _snake_case      :							Optional[int]     				=						get_activation(lowerCAmelCase__					)
  
               if out_dim is not None:
                      _snake_case      :							Union[str, Any]     				=						out_dim
               else:
                      _snake_case      :							int     				=						time_embed_dim
               _snake_case      :							Union[str, Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
  
               if post_act_fn is None:
                      _snake_case      :							int     				=						None
               else:
                      _snake_case      :							Any     				=						get_activation(lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_=None					)      ->   int:
  
  
  
               '''simple docstring'''
  
               if condition is not None:
                      _snake_case      :							Dict     				=						sample + self.cond_proj(lowerCAmelCase__					)
               _snake_case      :							Any     				=						self.linear_a(lowerCAmelCase__					)
  
               if self.act is not None:
                      _snake_case      :							Dict     				=						self.act(lowerCAmelCase__					)
  
               _snake_case      :							str     				=						self.linear_a(lowerCAmelCase__					)
  
               if self.post_act is not None:
                      _snake_case      :							Optional[int]     				=						self.post_act(lowerCAmelCase__					)
               return sample
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							str     				=						num_channels
               _snake_case      :							Optional[int]     				=						flip_sin_to_cos
               _snake_case      :							int     				=						downscale_freq_shift
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							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 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 256     ,  lowercase_ = 1.0     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=False					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							str     				=						nn.Parameter(torch.randn(lowerCAmelCase__					) * scale     ,  requires_grad=lowerCAmelCase__					)
               _snake_case      :							Optional[Any]     				=						log
               _snake_case      :							List[Any]     				=						flip_sin_to_cos
  
               if set_W_to_weight:
                      # to delete later
                      _snake_case      :							Optional[int]     				=						nn.Parameter(torch.randn(lowerCAmelCase__					) * scale     ,  requires_grad=lowerCAmelCase__					)
  
                      _snake_case      :							int     				=						self.W
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               if self.log:
                      _snake_case      :							List[Any]     				=						torch.log(lowerCAmelCase__					)
  
               _snake_case      :							Union[str, Any]     				=						x[:, None] * self.weight[None, :] * 2 * np.pi
  
               if self.flip_sin_to_cos:
                      _snake_case      :							List[str]     				=						torch.cat([torch.cos(lowerCAmelCase__					), torch.sin(lowerCAmelCase__					)]     ,  dim=-1					)
               else:
                      _snake_case      :							Optional[int]     				=						torch.cat([torch.sin(lowerCAmelCase__					), torch.cos(lowerCAmelCase__					)]     ,  dim=-1					)
               return out
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							Optional[Any]     				=						height
               _snake_case      :							Tuple     				=						width
               _snake_case      :							Dict     				=						num_embed
               _snake_case      :							int     				=						embed_dim
  
               _snake_case      :							Optional[Any]     				=						nn.Embedding(self.num_embed     ,  lowerCAmelCase__					)
               _snake_case      :							Union[str, Any]     				=						nn.Embedding(self.height     ,  lowerCAmelCase__					)
               _snake_case      :							List[Any]     				=						nn.Embedding(self.width     ,  lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.emb(lowerCAmelCase__					)
  
               _snake_case      :							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
               _snake_case      :							List[str]     				=						height_emb.unsqueeze(2					)
  
               _snake_case      :							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
               _snake_case      :							Union[str, Any]     				=						width_emb.unsqueeze(1					)
  
               _snake_case      :							Tuple     				=						height_emb + width_emb
  
               # 1 x H x W x D -> 1 x L xD
               _snake_case      :							Optional[int]     				=						pos_emb.view(1     ,  self.height * self.width     ,  -1					)
  
               _snake_case      :							int     				=						emb + pos_emb[:, : emb.shape[1], :]
  
               return emb
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							Any     				=						dropout_prob > 0
               _snake_case      :							Tuple     				=						nn.Embedding(num_classes + use_cfg_embedding     ,  lowerCAmelCase__					)
               _snake_case      :							Union[str, Any]     				=						num_classes
               _snake_case      :							Optional[int]     				=						dropout_prob
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_=None					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               if force_drop_ids is None:
                      _snake_case      :							Any     				=						torch.rand(labels.shape[0]     ,  device=labels.device					) < self.dropout_prob
               else:
                      _snake_case      :							Dict     				=						torch.tensor(force_drop_ids == 1					)
               _snake_case      :							Any     				=						torch.where(lowerCAmelCase__     ,  self.num_classes     ,  lowerCAmelCase__					)
               return labels
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_=None					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.dropout_prob > 0
               if (self.training and use_dropout) or (force_drop_ids is not None):
                      _snake_case      :							Any     				=						self.token_drop(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Dict     				=						self.embedding_table(lowerCAmelCase__					)
               return embeddings
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 1024     ,  lowercase_ = 768     ,  lowercase_ = 768     ,  lowercase_ = 10     ,  )      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							List[Any]     				=						num_image_text_embeds
               _snake_case      :							Union[str, Any]     				=						nn.Linear(lowerCAmelCase__     ,  self.num_image_text_embeds * cross_attention_dim					)
               _snake_case      :							Any     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						text_embeds.shape[0]
  
               # image
               _snake_case      :							Dict     				=						self.image_embeds(lowerCAmelCase__					)
               _snake_case      :							Optional[Any]     				=						image_text_embeds.reshape(lowerCAmelCase__     ,  self.num_image_text_embeds     ,  -1					)
  
               # text
               _snake_case      :							List[Any]     				=						self.text_proj(lowerCAmelCase__					)
  
               return torch.cat([image_text_embeds, text_embeds]     ,  dim=1					)
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 768     ,  lowercase_ = 768     ,  lowercase_ = 32     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							List[Any]     				=						num_image_text_embeds
               _snake_case      :							Any     				=						nn.Linear(lowerCAmelCase__     ,  self.num_image_text_embeds * cross_attention_dim					)
               _snake_case      :							str     				=						nn.LayerNorm(lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						image_embeds.shape[0]
  
               # image
               _snake_case      :							Tuple     				=						self.image_embeds(lowerCAmelCase__					)
               _snake_case      :							Optional[int]     				=						image_embeds.reshape(lowerCAmelCase__     ,  self.num_image_text_embeds     ,  -1					)
               _snake_case      :							Optional[int]     				=						self.norm(lowerCAmelCase__					)
               return image_embeds
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=0.1					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               super().__init__()
  
               _snake_case      :							List[Any]     				=						Timesteps(num_channels=256     ,  flip_sin_to_cos=lowerCAmelCase__     ,  downscale_freq_shift=1					)
               _snake_case      :							int     				=						TimestepEmbedding(in_channels=256     ,  time_embed_dim=lowerCAmelCase__					)
               _snake_case      :							List[Any]     				=						LabelEmbedding(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=None					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.time_proj(lowerCAmelCase__					)
               _snake_case      :							Union[str, Any]     				=						self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__					)					)  # (N, D)
  
               _snake_case      :							List[Any]     				=						self.class_embedder(lowerCAmelCase__					)  # (N, D)
  
               _snake_case      :							int     				=						timesteps_emb + class_labels  # (N, D)
  
               return conditioning
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = 64					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							int     				=						nn.LayerNorm(lowerCAmelCase__					)
               _snake_case      :							Tuple     				=						AttentionPooling(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Optional[Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Any     				=						nn.LayerNorm(lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.norma(lowerCAmelCase__					)
               _snake_case      :							int     				=						self.pool(lowerCAmelCase__					)
               _snake_case      :							Optional[Any]     				=						self.proj(lowerCAmelCase__					)
               _snake_case      :							Optional[int]     				=						self.norma(lowerCAmelCase__					)
               return hidden_states
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 768     ,  lowercase_ = 768     ,  lowercase_ = 1536					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							Dict     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Union[str, Any]     				=						nn.LayerNorm(lowerCAmelCase__					)
               _snake_case      :							str     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.text_proj(lowerCAmelCase__					)
               _snake_case      :							Optional[int]     				=						self.text_norm(lowerCAmelCase__					)
  
               # image
               _snake_case      :							Any     				=						self.image_proj(lowerCAmelCase__					)
  
               return time_image_embeds + time_text_embeds
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 768     ,  lowercase_ = 1536					)      ->   int:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							str     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Tuple     				=						nn.LayerNorm(lowerCAmelCase__					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.image_proj(lowerCAmelCase__					)
               _snake_case      :							Dict     				=						self.image_norm(lowerCAmelCase__					)
               return time_image_embeds
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_ = 768     ,  lowercase_ = 1536					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							Union[str, Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__					)
               _snake_case      :							Union[str, Any]     				=						nn.LayerNorm(lowerCAmelCase__					)
               _snake_case      :							int     				=						nn.Sequential(
                   nn.Convad(3     ,  16     ,  3     ,  padding=1					)     ,  nn.SiLU()     ,  nn.Convad(16     ,  16     ,  3     ,  padding=1					)     ,  nn.SiLU()     ,  nn.Convad(16     ,  32     ,  3     ,  padding=1     ,  stride=2					)     ,  nn.SiLU()     ,  nn.Convad(32     ,  32     ,  3     ,  padding=1					)     ,  nn.SiLU()     ,  nn.Convad(32     ,  96     ,  3     ,  padding=1     ,  stride=2					)     ,  nn.SiLU()     ,  nn.Convad(96     ,  96     ,  3     ,  padding=1					)     ,  nn.SiLU()     ,  nn.Convad(96     ,  256     ,  3     ,  padding=1     ,  stride=2					)     ,  nn.SiLU()     ,  nn.Convad(256     ,  4     ,  3     ,  padding=1					)     ,  )
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						self.image_proj(lowerCAmelCase__					)
               _snake_case      :							Optional[int]     				=						self.image_norm(lowerCAmelCase__					)
               _snake_case      :							List[Any]     				=						self.input_hint_block(lowerCAmelCase__					)
               return time_image_embeds, hint
  
  
  class 				A    (nn.Module				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=None					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							Dict     				=						dtype
               _snake_case      :							Dict     				=						nn.Parameter(torch.randn(1     ,  lowerCAmelCase__					) / embed_dim**0.5					)
               _snake_case      :							Union[str, Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__     ,  dtype=self.dtype					)
               _snake_case      :							Tuple     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__     ,  dtype=self.dtype					)
               _snake_case      :							Union[str, Any]     				=						nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__     ,  dtype=self.dtype					)
               _snake_case      :							str     				=						num_heads
               _snake_case      :							Optional[Any]     				=						embed_dim // self.num_heads
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						x.size()
  
               def shape(lowercase_					):
                      # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                      _snake_case      :							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)
                      _snake_case      :							Union[str, Any]     				=						x.transpose(1     ,  2					)
                      # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                      _snake_case      :							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)
                      _snake_case      :							Union[str, Any]     				=						x.transpose(1     ,  2					)
                      return x
  
               _snake_case      :							int     				=						x.mean(dim=1     ,  keepdim=lowerCAmelCase__					) + self.positional_embedding.to(x.dtype					)
               _snake_case      :							int     				=						torch.cat([class_token, x]     ,  dim=1					)  # (bs, length+1, width)
  
               # (bs*n_heads, class_token_length, dim_per_head)
               _snake_case      :							int     				=						shape(self.q_proj(lowerCAmelCase__					)					)
               # (bs*n_heads, length+class_token_length, dim_per_head)
               _snake_case      :							Optional[Any]     				=						shape(self.k_proj(lowerCAmelCase__					)					)
               _snake_case      :							Optional[int]     				=						shape(self.v_proj(lowerCAmelCase__					)					)
  
               # (bs*n_heads, class_token_length, length+class_token_length):
               _snake_case      :							str     				=						1 / math.sqrt(math.sqrt(self.dim_per_head					)					)
               _snake_case      :							int     				=						torch.einsum('''bct,bcs->bts'''     ,  q * scale     ,  k * scale					)  # More stable with f16 than dividing afterwards
               _snake_case      :							List[str]     				=						torch.softmax(weight.float()     ,  dim=-1					).type(weight.dtype					)
  
               # (bs*n_heads, dim_per_head, class_token_length)
               _snake_case      :							Union[str, Any]     				=						torch.einsum('''bts,bcs->bct'''     ,  lowerCAmelCase__     ,  lowerCAmelCase__					)
  
               # (bs, length+1, width)
               _snake_case      :							str     				=						a.reshape(lowerCAmelCase__     ,  -1     ,  1					).transpose(1     ,  2					)
  
               return a[:, 0, :]  # cls_token
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						vertices
               _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               print('''Vertex \t Distance from Source'''					)
               for vertex in range(self.vertices					):
                      print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						1E7
               _snake_case      :							Tuple     				=						0
  
               # Search not nearest vertex not in the shortest path tree
               for vertex in range(self.vertices					):
                      if distances_from_source[vertex] < minimum and visited[vertex] is False:
                             _snake_case      :							Dict     				=						distances_from_source[vertex]
                             _snake_case      :							int     				=						vertex
               return min_index
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
               _snake_case      :							List[Any]     				=						0
               _snake_case      :							List[str]     				=						[False] * self.vertices
  
               for _ in range(self.vertices					):
                      _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                      _snake_case      :							List[str]     				=						True
  
                      # Update dist value of the adjacent vertices
                      # of the picked vertex only if the current
                      # distance is greater than new distance and
                      # the vertex in not in the shortest path tree
                      for v in range(self.vertices					):
                             if (
                                 self.graph[u][v] > 0
                                 and visited[v] is False
                                 and distances[v] > distances[u] + self.graph[u][v]
                             ):
                                    _snake_case      :							Any     				=						distances[u] + self.graph[u][v]
  
               self.print_solution(lowercase_					)
  
  
  if __name__ == "__main__":
     lowerCAmelCase_   =   Graph(9)
     lowerCAmelCase_   =   [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)
  
  
  
  
  
  
  