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 							_snake_case			(      __snake_case       :			torch.Tensor	,   __snake_case       :			int	,   __snake_case       :			bool = False	,   __snake_case       :			float = 1	,   __snake_case       :			float = 1	,   __snake_case       :			int = 10000	,   ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"
  
     _lowerCamelCase  :				List[Any]      =		embedding_dim // 2
     _lowerCamelCase  :				int      =		-math.log(__snake_case      ) * torch.arange(
         start=0	,   end=__snake_case	,   dtype=torch.floataa	,   device=timesteps.device      )
     _lowerCamelCase  :				Dict      =		exponent / (half_dim - downscale_freq_shift)
  
     _lowerCamelCase  :				Optional[Any]      =		torch.exp(__snake_case      )
     _lowerCamelCase  :				List[Any]      =		timesteps[:, None].float() * emb[None, :]
  
     # scale embeddings
     _lowerCamelCase  :				Optional[Any]      =		scale * emb
  
     # concat sine and cosine embeddings
     _lowerCamelCase  :				str      =		torch.cat([torch.sin(__snake_case      ), torch.cos(__snake_case      )]	,   dim=-1      )
  
     # flip sine and cosine embeddings
     if flip_sin_to_cos:
        _lowerCamelCase  :				List[Any]      =		torch.cat([emb[:, half_dim:], emb[:, :half_dim]]	,   dim=-1      )
  
     # zero pad
     if embedding_dim % 2 == 1:
        _lowerCamelCase  :				Dict      =		torch.nn.functional.pad(__snake_case	,   (0, 1, 0, 0)      )
     return emb
  
  def 							_snake_case			(      __snake_case       :			Optional[int]	,   __snake_case       :			Optional[Any]	,   __snake_case       :			List[Any]=False	,   __snake_case       :			int=0      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		np.arange(__snake_case	,   dtype=np.floataa      )
     _lowerCamelCase  :				Any      =		np.arange(__snake_case	,   dtype=np.floataa      )
     _lowerCamelCase  :				int      =		np.meshgrid(__snake_case	,   __snake_case      )  # here w goes first
     _lowerCamelCase  :				List[str]      =		np.stack(__snake_case	,   axis=0      )
  
     _lowerCamelCase  :				Any      =		grid.reshape([2, 1, grid_size, grid_size]      )
     _lowerCamelCase  :				Tuple      =		get_ad_sincos_pos_embed_from_grid(__snake_case	,   __snake_case      )
     if cls_token and extra_tokens > 0:
        _lowerCamelCase  :				Optional[Any]      =		np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]	,   axis=0      )
     return pos_embed
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if embed_dim % 2 != 0:
        raise ValueError("""embed_dim must be divisible by 2"""      )
  
     # use half of dimensions to encode grid_h
     _lowerCamelCase  :				int      =		get_ad_sincos_pos_embed_from_grid(embed_dim // 2	,   grid[0]      )  # (H*W, D/2)
     _lowerCamelCase  :				List[Any]      =		get_ad_sincos_pos_embed_from_grid(embed_dim // 2	,   grid[1]      )  # (H*W, D/2)
  
     _lowerCamelCase  :				Tuple      =		np.concatenate([emb_h, emb_w]	,   axis=1      )  # (H*W, D)
     return emb
  
  def 							_snake_case			(      __snake_case       :			Optional[int]	,   __snake_case       :			Any      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if embed_dim % 2 != 0:
        raise ValueError("""embed_dim must be divisible by 2"""      )
  
     _lowerCamelCase  :				Union[str, Any]      =		np.arange(embed_dim // 2	,   dtype=np.floataa      )
     omega /= embed_dim / 2.0
     _lowerCamelCase  :				Any      =		1.0 / 10000**omega  # (D/2,)
  
     _lowerCamelCase  :				Optional[int]      =		pos.reshape(-1      )  # (M,)
     _lowerCamelCase  :				int      =		np.einsum("""m,d->md"""	,   __snake_case	,   __snake_case      )  # (M, D/2), outer product
  
     _lowerCamelCase  :				str      =		np.sin(__snake_case      )  # (M, D/2)
     _lowerCamelCase  :				str      =		np.cos(__snake_case      )  # (M, D/2)
  
     _lowerCamelCase  :				Dict      =		np.concatenate([emb_sin, emb_cos]	,   axis=1      )  # (M, D)
     return emb
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=224			,      SCREAMING_SNAKE_CASE=224			,      SCREAMING_SNAKE_CASE=16			,      SCREAMING_SNAKE_CASE=3			,      SCREAMING_SNAKE_CASE=768			,      SCREAMING_SNAKE_CASE=False			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=True			,      )     ->     Optional[Any]:
        super().__init__()
  
        _lowerCamelCase  :				Optional[Any]      =		(height // patch_size) * (width // patch_size)
        _lowerCamelCase  :				List[Any]      =		flatten
        _lowerCamelCase  :				List[Any]      =		layer_norm
  
        _lowerCamelCase  :				int      =		nn.Convad(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      kernel_size=(patch_size, patch_size)			,      stride=SCREAMING_SNAKE_CASE			,      bias=SCREAMING_SNAKE_CASE)
        if layer_norm:
           _lowerCamelCase  :				Any      =		nn.LayerNorm(SCREAMING_SNAKE_CASE			,      elementwise_affine=SCREAMING_SNAKE_CASE			,      eps=1e-6)
        else:
           _lowerCamelCase  :				Union[str, Any]      =		None
  
        _lowerCamelCase  :				Optional[Any]      =		get_ad_sincos_pos_embed(SCREAMING_SNAKE_CASE			,      int(num_patches**0.5))
        self.register_buffer("""pos_embed"""			,      torch.from_numpy(SCREAMING_SNAKE_CASE).float().unsqueeze(0)			,      persistent=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     int:
        _lowerCamelCase  :				Any      =		self.proj(SCREAMING_SNAKE_CASE)
        if self.flatten:
           _lowerCamelCase  :				Optional[Any]      =		latent.flatten(2).transpose(1			,      2)  # BCHW -> BNC
        if self.layer_norm:
           _lowerCamelCase  :				Optional[Any]      =		self.norm(SCREAMING_SNAKE_CASE)
        return latent + self.pos_embed
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = "silu"			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE=None			,      )     ->     Optional[Any]:
        super().__init__()
  
        _lowerCamelCase  :				Optional[int]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        if cond_proj_dim is not None:
           _lowerCamelCase  :				Optional[Any]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      bias=SCREAMING_SNAKE_CASE)
        else:
           _lowerCamelCase  :				Tuple      =		None
  
        _lowerCamelCase  :				Any      =		get_activation(SCREAMING_SNAKE_CASE)
  
        if out_dim is not None:
           _lowerCamelCase  :				Optional[Any]      =		out_dim
        else:
           _lowerCamelCase  :				Any      =		time_embed_dim
        _lowerCamelCase  :				List[str]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        if post_act_fn is None:
           _lowerCamelCase  :				Any      =		None
        else:
           _lowerCamelCase  :				Tuple      =		get_activation(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     List[str]:
        if condition is not None:
           _lowerCamelCase  :				Any      =		sample + self.cond_proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		self.linear_a(SCREAMING_SNAKE_CASE)
  
        if self.act is not None:
           _lowerCamelCase  :				List[Any]      =		self.act(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				int      =		self.linear_a(SCREAMING_SNAKE_CASE)
  
        if self.post_act is not None:
           _lowerCamelCase  :				Tuple      =		self.post_act(SCREAMING_SNAKE_CASE)
        return sample
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        super().__init__()
        _lowerCamelCase  :				int      =		num_channels
        _lowerCamelCase  :				str      =		flip_sin_to_cos
        _lowerCamelCase  :				Dict      =		downscale_freq_shift
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				Tuple      =		get_timestep_embedding(
            SCREAMING_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   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 256			,      SCREAMING_SNAKE_CASE = 1.0			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=False)     ->     Any:
        super().__init__()
        _lowerCamelCase  :				Tuple      =		nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE) * scale			,      requires_grad=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		log
        _lowerCamelCase  :				int      =		flip_sin_to_cos
  
        if set_W_to_weight:
           # to delete later
           _lowerCamelCase  :				Optional[Any]      =		nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE) * scale			,      requires_grad=SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase  :				Any      =		self.W
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        if self.log:
           _lowerCamelCase  :				Optional[int]      =		torch.log(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Any      =		x[:, None] * self.weight[None, :] * 2 * np.pi
  
        if self.flip_sin_to_cos:
           _lowerCamelCase  :				Any      =		torch.cat([torch.cos(SCREAMING_SNAKE_CASE), torch.sin(SCREAMING_SNAKE_CASE)]			,      dim=-1)
        else:
           _lowerCamelCase  :				Any      =		torch.cat([torch.sin(SCREAMING_SNAKE_CASE), torch.cos(SCREAMING_SNAKE_CASE)]			,      dim=-1)
        return out
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      )     ->     int:
        super().__init__()
  
        _lowerCamelCase  :				Tuple      =		height
        _lowerCamelCase  :				List[Any]      =		width
        _lowerCamelCase  :				Tuple      =		num_embed
        _lowerCamelCase  :				Tuple      =		embed_dim
  
        _lowerCamelCase  :				Dict      =		nn.Embedding(self.num_embed			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		nn.Embedding(self.height			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		nn.Embedding(self.width			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     List[Any]:
        _lowerCamelCase  :				List[Any]      =		self.emb(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				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
        _lowerCamelCase  :				Optional[Any]      =		height_emb.unsqueeze(2)
  
        _lowerCamelCase  :				Any      =		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
        _lowerCamelCase  :				Union[str, Any]      =		width_emb.unsqueeze(1)
  
        _lowerCamelCase  :				Optional[Any]      =		height_emb + width_emb
  
        # 1 x H x W x D -> 1 x L xD
        _lowerCamelCase  :				Any      =		pos_emb.view(1			,      self.height * self.width			,      -1)
  
        _lowerCamelCase  :				Any      =		emb + pos_emb[:, : emb.shape[1], :]
  
        return emb
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        super().__init__()
        _lowerCamelCase  :				Optional[Any]      =		dropout_prob > 0
        _lowerCamelCase  :				Tuple      =		nn.Embedding(num_classes + use_cfg_embedding			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		num_classes
        _lowerCamelCase  :				int      =		dropout_prob
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     int:
        if force_drop_ids is None:
           _lowerCamelCase  :				str      =		torch.rand(labels.shape[0]			,      device=labels.device) < self.dropout_prob
        else:
           _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(force_drop_ids == 1)
        _lowerCamelCase  :				Any      =		torch.where(SCREAMING_SNAKE_CASE			,      self.num_classes			,      SCREAMING_SNAKE_CASE)
        return labels
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     Tuple:
        _lowerCamelCase  :				Optional[Any]      =		self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
           _lowerCamelCase  :				Optional[int]      =		self.token_drop(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[int]      =		self.embedding_table(SCREAMING_SNAKE_CASE)
        return embeddings
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 1024			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 10			,      )     ->     List[str]:
        super().__init__()
  
        _lowerCamelCase  :				Optional[Any]      =		num_image_text_embeds
        _lowerCamelCase  :				Tuple      =		nn.Linear(SCREAMING_SNAKE_CASE			,      self.num_image_text_embeds * cross_attention_dim)
        _lowerCamelCase  :				Dict      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        _lowerCamelCase  :				int      =		text_embeds.shape[0]
  
        # image
        _lowerCamelCase  :				List[str]      =		self.image_embeds(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		image_text_embeds.reshape(SCREAMING_SNAKE_CASE			,      self.num_image_text_embeds			,      -1)
  
        # text
        _lowerCamelCase  :				List[Any]      =		self.text_proj(SCREAMING_SNAKE_CASE)
  
        return torch.cat([image_text_embeds, text_embeds]			,      dim=1)
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 32			,      )     ->     List[Any]:
        super().__init__()
  
        _lowerCamelCase  :				Optional[int]      =		num_image_text_embeds
        _lowerCamelCase  :				int      =		nn.Linear(SCREAMING_SNAKE_CASE			,      self.num_image_text_embeds * cross_attention_dim)
        _lowerCamelCase  :				int      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     int:
        _lowerCamelCase  :				Any      =		image_embeds.shape[0]
  
        # image
        _lowerCamelCase  :				Optional[int]      =		self.image_embeds(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		image_embeds.reshape(SCREAMING_SNAKE_CASE			,      self.num_image_text_embeds			,      -1)
        _lowerCamelCase  :				Union[str, Any]      =		self.norm(SCREAMING_SNAKE_CASE)
        return image_embeds
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=0.1)     ->     int:
        super().__init__()
  
        _lowerCamelCase  :				Any      =		Timesteps(num_channels=256			,      flip_sin_to_cos=SCREAMING_SNAKE_CASE			,      downscale_freq_shift=1)
        _lowerCamelCase  :				Any      =		TimestepEmbedding(in_channels=256			,      time_embed_dim=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		LabelEmbedding(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     List[Any]:
        _lowerCamelCase  :				Optional[Any]      =		self.time_proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE))  # (N, D)
  
        _lowerCamelCase  :				Union[str, Any]      =		self.class_embedder(SCREAMING_SNAKE_CASE)  # (N, D)
  
        _lowerCamelCase  :				List[Any]      =		timesteps_emb + class_labels  # (N, D)
  
        return conditioning
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = 64)     ->     Optional[Any]:
        super().__init__()
        _lowerCamelCase  :				int      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		AttentionPooling(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				int      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[Any]      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				Tuple      =		self.norma(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		self.pool(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		self.proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		self.norma(SCREAMING_SNAKE_CASE)
        return hidden_states
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 1536)     ->     List[Any]:
        super().__init__()
        _lowerCamelCase  :				str      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[Any]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Any:
        # text
        _lowerCamelCase  :				Union[str, Any]      =		self.text_proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[Any]      =		self.text_norm(SCREAMING_SNAKE_CASE)
  
        # image
        _lowerCamelCase  :				int      =		self.image_proj(SCREAMING_SNAKE_CASE)
  
        return time_image_embeds + time_text_embeds
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 1536)     ->     Optional[int]:
        super().__init__()
        _lowerCamelCase  :				int      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        # image
        _lowerCamelCase  :				Any      =		self.image_proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		self.image_norm(SCREAMING_SNAKE_CASE)
        return time_image_embeds
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 768			,      SCREAMING_SNAKE_CASE = 1536)     ->     Optional[int]:
        super().__init__()
        _lowerCamelCase  :				Optional[Any]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		nn.LayerNorm(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		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 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        # image
        _lowerCamelCase  :				List[str]      =		self.image_proj(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		self.image_norm(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Any      =		self.input_hint_block(SCREAMING_SNAKE_CASE)
        return time_image_embeds, hint
  
  
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     Optional[int]:
        super().__init__()
        _lowerCamelCase  :				Any      =		dtype
        _lowerCamelCase  :				int      =		nn.Parameter(torch.randn(1			,      SCREAMING_SNAKE_CASE) / embed_dim**0.5)
        _lowerCamelCase  :				Optional[Any]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      dtype=self.dtype)
        _lowerCamelCase  :				Any      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      dtype=self.dtype)
        _lowerCamelCase  :				Optional[Any]      =		nn.Linear(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      dtype=self.dtype)
        _lowerCamelCase  :				int      =		num_heads
        _lowerCamelCase  :				List[str]      =		embed_dim // self.num_heads
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		x.size()
  
        def shape(SCREAMING_SNAKE_CASE):
           # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
           _lowerCamelCase  :				Optional[Any]      =		x.view(SCREAMING_SNAKE_CASE			,      -1			,      self.num_heads			,      self.dim_per_head)
           # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
           _lowerCamelCase  :				int      =		x.transpose(1			,      2)
           # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
           _lowerCamelCase  :				Optional[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)
           _lowerCamelCase  :				str      =		x.transpose(1			,      2)
           return x
  
        _lowerCamelCase  :				List[str]      =		x.mean(dim=1			,      keepdim=SCREAMING_SNAKE_CASE) + self.positional_embedding.to(x.dtype)
        _lowerCamelCase  :				List[str]      =		torch.cat([class_token, x]			,      dim=1)  # (bs, length+1, width)
  
        # (bs*n_heads, class_token_length, dim_per_head)
        _lowerCamelCase  :				Union[str, Any]      =		shape(self.q_proj(SCREAMING_SNAKE_CASE))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        _lowerCamelCase  :				Dict      =		shape(self.k_proj(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Any      =		shape(self.v_proj(SCREAMING_SNAKE_CASE))
  
        # (bs*n_heads, class_token_length, length+class_token_length):
        _lowerCamelCase  :				int      =		1 / math.sqrt(math.sqrt(self.dim_per_head))
        _lowerCamelCase  :				Optional[int]      =		torch.einsum("""bct,bcs->bts"""			,      q * scale			,      k * scale)  # More stable with f16 than dividing afterwards
        _lowerCamelCase  :				Optional[int]      =		torch.softmax(weight.float()			,      dim=-1).type(weight.dtype)
  
        # (bs*n_heads, dim_per_head, class_token_length)
        _lowerCamelCase  :				Tuple      =		torch.einsum("""bts,bcs->bct"""			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        # (bs, length+1, width)
        _lowerCamelCase  :				str      =		a.reshape(SCREAMING_SNAKE_CASE			,      -1			,      1).transpose(1			,      2)
  
        return a[:, 0, :]  # cls_token
  
  
style_context: 
  
  
  """simple docstring"""
  from itertools import permutations
  
  def 							_snake_case			(      __snake_case       :			int = 5      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if gon_side < 3 or gon_side > 5:
        raise ValueError("""gon_side must be in the range [3, 5]"""      )
  
     # Since it's 16, we know 10 is on the outer ring
     # Put the big numbers at the end so that they are never the first number
     _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
     _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )
  
     for perm in permutations(small_numbers + big_numbers      ):
        _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
        if is_magic_gon(__snake_case      ):
           return int("""""".join(str(__snake_case      ) for n in numbers      )      )
  
     _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
     raise ValueError(__snake_case      )
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
     _lowerCamelCase  :				Dict      =		perm[0:3]
     perm.append(perm[1]      )
  
     _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2
  
     for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
        _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
        _lowerCamelCase  :				str      =		result[3 * i - 1]
        _lowerCamelCase  :				Dict      =		perm[2 * i + 2]
  
     return result
  
  def 							_snake_case			(      __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if len(__snake_case      ) % 3 != 0:
        raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )
  
     if min(numbers[::3]      ) != numbers[0]:
        return False
  
     _lowerCamelCase  :				str      =		sum(numbers[:3]      )
  
     return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )
  
  
  if __name__ == "__main__":
   print(solution())
  
  