code: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					List[Any]      , __lowerCamelCase				:					List[str] = False      , __lowerCamelCase				:					str = 1      , __lowerCamelCase				:					Optional[int] = 1      , __lowerCamelCase				:					Optional[Any] = 1_0000      , )		->Tuple:
      assert len(timesteps.shape					) == 1, "Timesteps should be a 1d-array"
  
      _SCREAMING_SNAKE_CASE             =    embedding_dim // 2
      _SCREAMING_SNAKE_CASE             =    -math.log(UpperCamelCase_					) * torch.arange(
          start=0      , end=UpperCamelCase_      , dtype=torch.floataa      , device=timesteps.device					)
      _SCREAMING_SNAKE_CASE             =    exponent / (half_dim - downscale_freq_shift)
  
      _SCREAMING_SNAKE_CASE             =    torch.exp(UpperCamelCase_					)
      _SCREAMING_SNAKE_CASE             =    timesteps[:, None].float() * emb[None, :]
  
      # scale embeddings
      _SCREAMING_SNAKE_CASE             =    scale * emb
  
      # concat sine and cosine embeddings
      _SCREAMING_SNAKE_CASE             =    torch.cat([torch.sin(UpperCamelCase_					), torch.cos(UpperCamelCase_					)]      , dim=-1					)
  
      # flip sine and cosine embeddings
      if flip_sin_to_cos:
          _SCREAMING_SNAKE_CASE             =    torch.cat([emb[:, half_dim:], emb[:, :half_dim]]      , dim=-1					)
  
      # zero pad
      if embedding_dim % 2 == 1:
          _SCREAMING_SNAKE_CASE             =    torch.nn.functional.pad(UpperCamelCase_      , (0, 1, 0, 0)					)
      return emb
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Tuple=False      , __lowerCamelCase				:					Dict=0					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE             =    np.arange(UpperCamelCase_      , dtype=np.floataa					)
      _SCREAMING_SNAKE_CASE             =    np.arange(UpperCamelCase_      , dtype=np.floataa					)
      _SCREAMING_SNAKE_CASE             =    np.meshgrid(UpperCamelCase_      , UpperCamelCase_					)  # here w goes first
      _SCREAMING_SNAKE_CASE             =    np.stack(UpperCamelCase_      , axis=0					)
  
      _SCREAMING_SNAKE_CASE             =    grid.reshape([2, 1, grid_size, grid_size]					)
      _SCREAMING_SNAKE_CASE             =    get_ad_sincos_pos_embed_from_grid(UpperCamelCase_      , UpperCamelCase_					)
      if cls_token and extra_tokens > 0:
          _SCREAMING_SNAKE_CASE             =    np.concatenate([np.zeros([extra_tokens, embed_dim]					), pos_embed]      , axis=0					)
      return pos_embed
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					List[str]					)		->Any:
  
  
  
  
  
  
      if embed_dim % 2 != 0:
          raise ValueError("""embed_dim must be divisible by 2"""					)
  
      # use half of dimensions to encode grid_h
      _SCREAMING_SNAKE_CASE             =    get_ad_sincos_pos_embed_from_grid(embed_dim // 2      , grid[0]					)  # (H*W, D/2)
      _SCREAMING_SNAKE_CASE             =    get_ad_sincos_pos_embed_from_grid(embed_dim // 2      , grid[1]					)  # (H*W, D/2)
  
      _SCREAMING_SNAKE_CASE             =    np.concatenate([emb_h, emb_w]      , axis=1					)  # (H*W, D)
      return emb
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					List[str]					)		->Optional[int]:
  
  
  
  
  
  
      if embed_dim % 2 != 0:
          raise ValueError("""embed_dim must be divisible by 2"""					)
  
      _SCREAMING_SNAKE_CASE             =    np.arange(embed_dim // 2      , dtype=np.floataa					)
      omega /= embed_dim / 2.0
      _SCREAMING_SNAKE_CASE             =    1.0 / 1_0000**omega  # (D/2,)
  
      _SCREAMING_SNAKE_CASE             =    pos.reshape(-1					)  # (M,)
      _SCREAMING_SNAKE_CASE             =    np.einsum("""m,d->md"""      , UpperCamelCase_      , UpperCamelCase_					)  # (M, D/2), outer product
  
      _SCREAMING_SNAKE_CASE             =    np.sin(UpperCamelCase_					)  # (M, D/2)
      _SCREAMING_SNAKE_CASE             =    np.cos(UpperCamelCase_					)  # (M, D/2)
  
      _SCREAMING_SNAKE_CASE             =    np.concatenate([emb_sin, emb_cos]      , axis=1					)  # (M, D)
      return emb
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A=224				,	A=224				,	A=16				,	A=3				,	A=768				,	A=False				,	A=True				,	A=True				,	)	->     Any:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    (height // patch_size) * (width // patch_size)
          _SCREAMING_SNAKE_CASE             =    flatten
          _SCREAMING_SNAKE_CASE             =    layer_norm
  
          _SCREAMING_SNAKE_CASE             =    nn.Convad(
              __snake_case				,	__snake_case				,	kernel_size=(patch_size, patch_size)				,	stride=__snake_case				,	bias=__snake_case )
          if layer_norm:
              _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case				,	elementwise_affine=__snake_case				,	eps=1e-6 )
          else:
              _SCREAMING_SNAKE_CASE             =    None
  
          _SCREAMING_SNAKE_CASE             =    get_ad_sincos_pos_embed(__snake_case				,	int(num_patches**0.5 ) )
          self.register_buffer("""pos_embed"""				,	torch.from_numpy(__snake_case ).float().unsqueeze(0 )				,	persistent=__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.proj(__snake_case )
          if self.flatten:
              _SCREAMING_SNAKE_CASE             =    latent.flatten(2 ).transpose(1				,	2 )  # BCHW -> BNC
          if self.layer_norm:
              _SCREAMING_SNAKE_CASE             =    self.norm(__snake_case )
          return latent + self.pos_embed
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A = "silu"				,	A = None				,	A = None				,	A=None				,	)	->     List[str]:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
  
          if cond_proj_dim is not None:
              _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case				,	bias=__snake_case )
          else:
              _SCREAMING_SNAKE_CASE             =    None
  
          _SCREAMING_SNAKE_CASE             =    get_activation(__snake_case )
  
          if out_dim is not None:
              _SCREAMING_SNAKE_CASE             =    out_dim
          else:
              _SCREAMING_SNAKE_CASE             =    time_embed_dim
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
  
          if post_act_fn is None:
              _SCREAMING_SNAKE_CASE             =    None
          else:
              _SCREAMING_SNAKE_CASE             =    get_activation(__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A=None )	->     Any:
          if condition is not None:
              _SCREAMING_SNAKE_CASE             =    sample + self.cond_proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.linear_a(__snake_case )
  
          if self.act is not None:
              _SCREAMING_SNAKE_CASE             =    self.act(__snake_case )
  
          _SCREAMING_SNAKE_CASE             =    self.linear_a(__snake_case )
  
          if self.post_act is not None:
              _SCREAMING_SNAKE_CASE             =    self.post_act(__snake_case )
          return sample
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A )	->     Optional[int]:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    flip_sin_to_cos
          _SCREAMING_SNAKE_CASE             =    downscale_freq_shift
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    get_timestep_embedding(
              __snake_case				,	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 ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 256				,	A = 1.0				,	A=True				,	A=True				,	A=False )	->     Optional[Any]:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    nn.Parameter(torch.randn(__snake_case ) * scale				,	requires_grad=__snake_case )
          _SCREAMING_SNAKE_CASE             =    log
          _SCREAMING_SNAKE_CASE             =    flip_sin_to_cos
  
          if set_W_to_weight:
              # to delete later
              _SCREAMING_SNAKE_CASE             =    nn.Parameter(torch.randn(__snake_case ) * scale				,	requires_grad=__snake_case )
  
              _SCREAMING_SNAKE_CASE             =    self.W
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     str:
          if self.log:
              _SCREAMING_SNAKE_CASE             =    torch.log(__snake_case )
  
          _SCREAMING_SNAKE_CASE             =    x[:, None] * self.weight[None, :] * 2 * np.pi
  
          if self.flip_sin_to_cos:
              _SCREAMING_SNAKE_CASE             =    torch.cat([torch.cos(__snake_case ), torch.sin(__snake_case )]				,	dim=-1 )
          else:
              _SCREAMING_SNAKE_CASE             =    torch.cat([torch.sin(__snake_case ), torch.cos(__snake_case )]				,	dim=-1 )
          return out
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A				,	A				,	)	->     List[str]:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    height
          _SCREAMING_SNAKE_CASE             =    width
          _SCREAMING_SNAKE_CASE             =    num_embed
          _SCREAMING_SNAKE_CASE             =    embed_dim
  
          _SCREAMING_SNAKE_CASE             =    nn.Embedding(self.num_embed				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.Embedding(self.height				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.Embedding(self.width				,	__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    self.emb(__snake_case )
  
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    height_emb.unsqueeze(2 )
  
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    width_emb.unsqueeze(1 )
  
          _SCREAMING_SNAKE_CASE             =    height_emb + width_emb
  
          # 1 x H x W x D -> 1 x L xD
          _SCREAMING_SNAKE_CASE             =    pos_emb.view(1				,	self.height * self.width				,	-1 )
  
          _SCREAMING_SNAKE_CASE             =    emb + pos_emb[:, : emb.shape[1], :]
  
          return emb
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A )	->     Dict:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    dropout_prob > 0
          _SCREAMING_SNAKE_CASE             =    nn.Embedding(num_classes + use_cfg_embedding				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    num_classes
          _SCREAMING_SNAKE_CASE             =    dropout_prob
  
  
      def     snake_case_(	self				,	A				,	A=None )	->     Optional[Any]:
          if force_drop_ids is None:
              _SCREAMING_SNAKE_CASE             =    torch.rand(labels.shape[0]				,	device=labels.device ) < self.dropout_prob
          else:
              _SCREAMING_SNAKE_CASE             =    torch.tensor(force_drop_ids == 1 )
          _SCREAMING_SNAKE_CASE             =    torch.where(__snake_case				,	self.num_classes				,	__snake_case )
          return labels
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A=None )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.dropout_prob > 0
          if (self.training and use_dropout) or (force_drop_ids is not None):
              _SCREAMING_SNAKE_CASE             =    self.token_drop(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.embedding_table(__snake_case )
          return embeddings
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 1024				,	A = 768				,	A = 768				,	A = 10				,	)	->     int:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    num_image_text_embeds
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	self.num_image_text_embeds * cross_attention_dim )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    text_embeds.shape[0]
  
          # image
          _SCREAMING_SNAKE_CASE             =    self.image_embeds(__snake_case )
          _SCREAMING_SNAKE_CASE             =    image_text_embeds.reshape(__snake_case				,	self.num_image_text_embeds				,	-1 )
  
          # text
          _SCREAMING_SNAKE_CASE             =    self.text_proj(__snake_case )
  
          return torch.cat([image_text_embeds, text_embeds]				,	dim=1 )
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 768				,	A = 768				,	A = 32				,	)	->     int:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    num_image_text_embeds
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	self.num_image_text_embeds * cross_attention_dim )
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    image_embeds.shape[0]
  
          # image
          _SCREAMING_SNAKE_CASE             =    self.image_embeds(__snake_case )
          _SCREAMING_SNAKE_CASE             =    image_embeds.reshape(__snake_case				,	self.num_image_text_embeds				,	-1 )
          _SCREAMING_SNAKE_CASE             =    self.norm(__snake_case )
          return image_embeds
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A=0.1 )	->     List[str]:
          super().__init__()
  
          _SCREAMING_SNAKE_CASE             =    Timesteps(num_channels=256				,	flip_sin_to_cos=__snake_case				,	downscale_freq_shift=1 )
          _SCREAMING_SNAKE_CASE             =    TimestepEmbedding(in_channels=256				,	time_embed_dim=__snake_case )
          _SCREAMING_SNAKE_CASE             =    LabelEmbedding(__snake_case				,	__snake_case				,	__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A				,	A=None )	->     str:
          _SCREAMING_SNAKE_CASE             =    self.time_proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.timestep_embedder(timesteps_proj.to(dtype=__snake_case ) )  # (N, D)
  
          _SCREAMING_SNAKE_CASE             =    self.class_embedder(__snake_case )  # (N, D)
  
          _SCREAMING_SNAKE_CASE             =    timesteps_emb + class_labels  # (N, D)
  
          return conditioning
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A = 64 )	->     str:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
          _SCREAMING_SNAKE_CASE             =    AttentionPooling(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.norma(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.pool(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.norma(__snake_case )
          return hidden_states
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 768				,	A = 768				,	A = 1536 )	->     Any:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A )	->     Dict:
          # text
          _SCREAMING_SNAKE_CASE             =    self.text_proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.text_norm(__snake_case )
  
          # image
          _SCREAMING_SNAKE_CASE             =    self.image_proj(__snake_case )
  
          return time_image_embeds + time_text_embeds
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 768				,	A = 1536 )	->     Tuple:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     int:
          # image
          _SCREAMING_SNAKE_CASE             =    self.image_proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.image_norm(__snake_case )
          return time_image_embeds
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A = 768				,	A = 1536 )	->     Dict:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case )
          _SCREAMING_SNAKE_CASE             =    nn.LayerNorm(__snake_case )
          _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A )	->     Dict:
          # image
          _SCREAMING_SNAKE_CASE             =    self.image_proj(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.image_norm(__snake_case )
          _SCREAMING_SNAKE_CASE             =    self.input_hint_block(__snake_case )
          return time_image_embeds, hint
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A=None )	->     int:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    dtype
          _SCREAMING_SNAKE_CASE             =    nn.Parameter(torch.randn(1				,	__snake_case ) / embed_dim**0.5 )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case				,	dtype=self.dtype )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case				,	dtype=self.dtype )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(__snake_case				,	__snake_case				,	dtype=self.dtype )
          _SCREAMING_SNAKE_CASE             =    num_heads
          _SCREAMING_SNAKE_CASE             =    embed_dim // self.num_heads
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     int:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    x.size()
  
          def shape(A ):
              # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
              _SCREAMING_SNAKE_CASE             =    x.view(__snake_case				,	-1				,	self.num_heads				,	self.dim_per_head )
              # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
              _SCREAMING_SNAKE_CASE             =    x.transpose(1				,	2 )
              # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
              _SCREAMING_SNAKE_CASE             =    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)
              _SCREAMING_SNAKE_CASE             =    x.transpose(1				,	2 )
              return x
  
          _SCREAMING_SNAKE_CASE             =    x.mean(dim=1				,	keepdim=__snake_case ) + self.positional_embedding.to(x.dtype )
          _SCREAMING_SNAKE_CASE             =    torch.cat([class_token, x]				,	dim=1 )  # (bs, length+1, width)
  
          # (bs*n_heads, class_token_length, dim_per_head)
          _SCREAMING_SNAKE_CASE             =    shape(self.q_proj(__snake_case ) )
          # (bs*n_heads, length+class_token_length, dim_per_head)
          _SCREAMING_SNAKE_CASE             =    shape(self.k_proj(__snake_case ) )
          _SCREAMING_SNAKE_CASE             =    shape(self.v_proj(__snake_case ) )
  
          # (bs*n_heads, class_token_length, length+class_token_length):
          _SCREAMING_SNAKE_CASE             =    1 / math.sqrt(math.sqrt(self.dim_per_head ) )
          _SCREAMING_SNAKE_CASE             =    torch.einsum("""bct,bcs->bts"""				,	q * scale				,	k * scale )  # More stable with f16 than dividing afterwards
          _SCREAMING_SNAKE_CASE             =    torch.softmax(weight.float()				,	dim=-1 ).type(weight.dtype )
  
          # (bs*n_heads, dim_per_head, class_token_length)
          _SCREAMING_SNAKE_CASE             =    torch.einsum("""bts,bcs->bct"""				,	__snake_case				,	__snake_case )
  
          # (bs, length+1, width)
          _SCREAMING_SNAKE_CASE             =    a.reshape(__snake_case				,	-1				,	1 ).transpose(1				,	2 )
  
          return a[:, 0, :]  # cls_token
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import safs
  
  
  class       a_  (       safs.SaFileSystem ):
  
      '''simple docstring'''
  
  
  
  