code: 
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_ = False    ,     UpperCAmelCase_ = 1    ,     UpperCAmelCase_ = 1    ,     UpperCAmelCase_ = 10000    ,     )						->							Tuple:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         assert len(timesteps.shape			) == 1, "Timesteps should be a 1d-array"
  
         _lowercase					:				Tuple           =			embedding_dim // 2
         _lowercase					:				Optional[Any]           =			-math.log(UpperCAmelCase_			) * torch.arange(
             start=0    ,     end=UpperCAmelCase_    ,     dtype=torch.floataa    ,     device=timesteps.device			)
         _lowercase					:				Any           =			exponent / (half_dim - downscale_freq_shift)
  
         _lowercase					:				Dict           =			torch.exp(UpperCAmelCase_			)
         _lowercase					:				List[str]           =			timesteps[:, None].float() * emb[None, :]
  
         # scale embeddings
         _lowercase					:				str           =			scale * emb
  
         # concat sine and cosine embeddings
         _lowercase					:				Tuple           =			torch.cat([torch.sin(UpperCAmelCase_			), torch.cos(UpperCAmelCase_			)]    ,     dim=-1			)
  
         # flip sine and cosine embeddings
         if flip_sin_to_cos:
                _lowercase					:				Tuple           =			torch.cat([emb[:, half_dim:], emb[:, :half_dim]]    ,     dim=-1			)
  
         # zero pad
         if embedding_dim % 2 == 1:
                _lowercase					:				Tuple           =			torch.nn.functional.pad(UpperCAmelCase_    ,     (0, 1, 0, 0)			)
         return emb
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=0			)						->							Optional[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			np.arange(UpperCAmelCase_    ,     dtype=np.floataa			)
         _lowercase					:				List[Any]           =			np.arange(UpperCAmelCase_    ,     dtype=np.floataa			)
         _lowercase					:				str           =			np.meshgrid(UpperCAmelCase_    ,     UpperCAmelCase_			)  # here w goes first
         _lowercase					:				Optional[Any]           =			np.stack(UpperCAmelCase_    ,     axis=0			)
  
         _lowercase					:				str           =			grid.reshape([2, 1, grid_size, grid_size]			)
         _lowercase					:				Union[str, Any]           =			get_ad_sincos_pos_embed_from_grid(UpperCAmelCase_    ,     UpperCAmelCase_			)
         if cls_token and extra_tokens > 0:
                _lowercase					:				Optional[int]           =			np.concatenate([np.zeros([extra_tokens, embed_dim]			), pos_embed]    ,     axis=0			)
         return pos_embed
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[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
         _lowercase					:				Union[str, Any]           =			get_ad_sincos_pos_embed_from_grid(embed_dim // 2    ,     grid[0]			)  # (H*W, D/2)
         _lowercase					:				List[str]           =			get_ad_sincos_pos_embed_from_grid(embed_dim // 2    ,     grid[1]			)  # (H*W, D/2)
  
         _lowercase					:				str           =			np.concatenate([emb_h, emb_w]    ,     axis=1			)  # (H*W, D)
         return emb
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if embed_dim % 2 != 0:
                raise ValueError('''embed_dim must be divisible by 2'''			)
  
         _lowercase					:				Optional[Any]           =			np.arange(embed_dim // 2    ,     dtype=np.floataa			)
         omega /= embed_dim / 2.0
         _lowercase					:				List[str]           =			1.0 / 10000**omega  # (D/2,)
  
         _lowercase					:				Tuple           =			pos.reshape(-1			)  # (M,)
         _lowercase					:				Optional[int]           =			np.einsum('''m,d->md'''    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)  # (M, D/2), outer product
  
         _lowercase					:				List[Any]           =			np.sin(UpperCAmelCase_			)  # (M, D/2)
         _lowercase					:				int           =			np.cos(UpperCAmelCase_			)  # (M, D/2)
  
         _lowercase					:				List[str]           =			np.concatenate([emb_sin, emb_cos]    ,     axis=1			)  # (M, D)
         return emb
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Dict=2_24	,      UpperCamelCase       :       Dict=2_24	,      UpperCamelCase       :       Optional[int]=16	,      UpperCamelCase       :       Optional[int]=3	,      UpperCamelCase       :       int=7_68	,      UpperCamelCase       :       Tuple=False	,      UpperCamelCase       :       Optional[Any]=True	,      UpperCamelCase       :       Any=True	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Optional[int]           =			(height // patch_size) * (width // patch_size)
              _lowercase					:				List[str]           =			flatten
              _lowercase					:				Optional[Any]           =			layer_norm
  
              _lowercase					:				int           =			nn.Convad(
                  UpperCamelCase	,      UpperCamelCase	,      kernel_size=(patch_size, patch_size)	,      stride=UpperCamelCase	,      bias=UpperCamelCase		)
              if layer_norm:
                     _lowercase					:				str           =			nn.LayerNorm(UpperCamelCase	,      elementwise_affine=UpperCamelCase	,      eps=1E-6		)
              else:
                     _lowercase					:				Union[str, Any]           =			None
  
              _lowercase					:				List[str]           =			get_ad_sincos_pos_embed(UpperCamelCase	,      int(num_patches**0.5		)		)
              self.register_buffer('''pos_embed'''	,      torch.from_numpy(UpperCamelCase		).float().unsqueeze(0		)	,      persistent=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any	,      UpperCamelCase       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			self.proj(UpperCamelCase		)
              if self.flatten:
                     _lowercase					:				Optional[Any]           =			latent.flatten(2		).transpose(1	,      2		)  # BCHW -> BNC
              if self.layer_norm:
                     _lowercase					:				Optional[Any]           =			self.norm(UpperCamelCase		)
              return latent + self.pos_embed
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str = "silu"	,      UpperCamelCase       :       int = None	,      UpperCamelCase       :       Optional[str] = None	,      UpperCamelCase       :       Any=None	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Optional[int]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
  
              if cond_proj_dim is not None:
                     _lowercase					:				Dict           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)
              else:
                     _lowercase					:				int           =			None
  
              _lowercase					:				List[str]           =			get_activation(UpperCamelCase		)
  
              if out_dim is not None:
                     _lowercase					:				List[Any]           =			out_dim
              else:
                     _lowercase					:				List[Any]           =			time_embed_dim
              _lowercase					:				List[Any]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
  
              if post_act_fn is None:
                     _lowercase					:				Union[str, Any]           =			None
              else:
                     _lowercase					:				str           =			get_activation(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str	,      UpperCamelCase       :       Tuple	,      UpperCamelCase       :       Any=None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if condition is not None:
                     _lowercase					:				Tuple           =			sample + self.cond_proj(UpperCamelCase		)
              _lowercase					:				Optional[int]           =			self.linear_a(UpperCamelCase		)
  
              if self.act is not None:
                     _lowercase					:				List[str]           =			self.act(UpperCamelCase		)
  
              _lowercase					:				Any           =			self.linear_a(UpperCamelCase		)
  
              if self.post_act is not None:
                     _lowercase					:				Optional[Any]           =			self.post_act(UpperCamelCase		)
              return sample
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       int	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       float		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				Dict           =			num_channels
              _lowercase					:				Tuple           =			flip_sin_to_cos
              _lowercase					:				List[Any]           =			downscale_freq_shift
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			get_timestep_embedding(
                  UpperCamelCase	,      self.num_channels	,      flip_sin_to_cos=self.flip_sin_to_cos	,      downscale_freq_shift=self.downscale_freq_shift	,      )
              return t_emb
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       int = 2_56	,      UpperCamelCase       :       float = 1.0	,      UpperCamelCase       :       List[str]=True	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       List[Any]=False		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				List[str]           =			nn.Parameter(torch.randn(UpperCamelCase		) * scale	,      requires_grad=UpperCamelCase		)
              _lowercase					:				List[Any]           =			log
              _lowercase					:				int           =			flip_sin_to_cos
  
              if set_W_to_weight:
                     # to delete later
                     _lowercase					:				Optional[int]           =			nn.Parameter(torch.randn(UpperCamelCase		) * scale	,      requires_grad=UpperCamelCase		)
  
                     _lowercase					:				Dict           =			self.W
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if self.log:
                     _lowercase					:				List[str]           =			torch.log(UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			x[:, None] * self.weight[None, :] * 2 * np.pi
  
              if self.flip_sin_to_cos:
                     _lowercase					:				Tuple           =			torch.cat([torch.cos(UpperCamelCase		), torch.sin(UpperCamelCase		)]	,      dim=-1		)
              else:
                     _lowercase					:				Optional[int]           =			torch.cat([torch.sin(UpperCamelCase		), torch.cos(UpperCamelCase		)]	,      dim=-1		)
              return out
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[Any]	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Any           =			height
              _lowercase					:				Optional[int]           =			width
              _lowercase					:				int           =			num_embed
              _lowercase					:				List[str]           =			embed_dim
  
              _lowercase					:				str           =			nn.Embedding(self.num_embed	,      UpperCamelCase		)
              _lowercase					:				List[str]           =			nn.Embedding(self.height	,      UpperCamelCase		)
              _lowercase					:				str           =			nn.Embedding(self.width	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.emb(UpperCamelCase		)
  
              _lowercase					:				Optional[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
              _lowercase					:				Dict           =			height_emb.unsqueeze(2		)
  
              _lowercase					:				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
              _lowercase					:				int           =			width_emb.unsqueeze(1		)
  
              _lowercase					:				List[Any]           =			height_emb + width_emb
  
              # 1 x H x W x D -> 1 x L xD
              _lowercase					:				int           =			pos_emb.view(1	,      self.height * self.width	,      -1		)
  
              _lowercase					:				Optional[Any]           =			emb + pos_emb[:, : emb.shape[1], :]
  
              return emb
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				Union[str, Any]           =			dropout_prob > 0
              _lowercase					:				Any           =			nn.Embedding(num_classes + use_cfg_embedding	,      UpperCamelCase		)
              _lowercase					:				str           =			num_classes
              _lowercase					:				Dict           =			dropout_prob
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       Tuple=None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if force_drop_ids is None:
                     _lowercase					:				str           =			torch.rand(labels.shape[0]	,      device=labels.device		) < self.dropout_prob
              else:
                     _lowercase					:				Optional[int]           =			torch.tensor(force_drop_ids == 1		)
              _lowercase					:				Optional[int]           =			torch.where(UpperCamelCase	,      self.num_classes	,      UpperCamelCase		)
              return labels
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       torch.LongTensor	,      UpperCamelCase       :       Dict=None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			self.dropout_prob > 0
              if (self.training and use_dropout) or (force_drop_ids is not None):
                     _lowercase					:				Optional[Any]           =			self.token_drop(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Dict           =			self.embedding_table(UpperCamelCase		)
              return embeddings
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[Any]	,      UpperCamelCase       :       int = 10_24	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 10	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Optional[int]           =			num_image_text_embeds
              _lowercase					:				Tuple           =			nn.Linear(UpperCamelCase	,      self.num_image_text_embeds * cross_attention_dim		)
              _lowercase					:				Dict           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       torch.FloatTensor	,      UpperCamelCase       :       torch.FloatTensor		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			text_embeds.shape[0]
  
              # image
              _lowercase					:				List[str]           =			self.image_embeds(UpperCamelCase		)
              _lowercase					:				str           =			image_text_embeds.reshape(UpperCamelCase	,      self.num_image_text_embeds	,      -1		)
  
              # text
              _lowercase					:				Dict           =			self.text_proj(UpperCamelCase		)
  
              return torch.cat([image_text_embeds, text_embeds]	,      dim=1		)
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 32	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Tuple           =			num_image_text_embeds
              _lowercase					:				List[Any]           =			nn.Linear(UpperCamelCase	,      self.num_image_text_embeds * cross_attention_dim		)
              _lowercase					:				str           =			nn.LayerNorm(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       torch.FloatTensor		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			image_embeds.shape[0]
  
              # image
              _lowercase					:				Optional[Any]           =			self.image_embeds(UpperCamelCase		)
              _lowercase					:				Optional[int]           =			image_embeds.reshape(UpperCamelCase	,      self.num_image_text_embeds	,      -1		)
              _lowercase					:				Tuple           =			self.norm(UpperCamelCase		)
              return image_embeds
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Union[str, Any]=0.1		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Optional[Any]           =			Timesteps(num_channels=2_56	,      flip_sin_to_cos=UpperCamelCase	,      downscale_freq_shift=1		)
              _lowercase					:				Tuple           =			TimestepEmbedding(in_channels=2_56	,      time_embed_dim=UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			LabelEmbedding(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       int=None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			self.time_proj(UpperCamelCase		)
              _lowercase					:				Tuple           =			self.timestep_embedder(timesteps_proj.to(dtype=UpperCamelCase		)		)  # (N, D)
  
              _lowercase					:				Optional[Any]           =			self.class_embedder(UpperCamelCase		)  # (N, D)
  
              _lowercase					:				List[Any]           =			timesteps_emb + class_labels  # (N, D)
  
              return conditioning
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int = 64		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				str           =			nn.LayerNorm(UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			AttentionPooling(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			nn.LayerNorm(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			self.norma(UpperCamelCase		)
              _lowercase					:				Tuple           =			self.pool(UpperCamelCase		)
              _lowercase					:				Tuple           =			self.proj(UpperCamelCase		)
              _lowercase					:				int           =			self.norma(UpperCamelCase		)
              return hidden_states
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[Any]	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 15_36		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				str           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			nn.LayerNorm(UpperCamelCase		)
              _lowercase					:				List[str]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       torch.FloatTensor	,      UpperCamelCase       :       torch.FloatTensor		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			self.text_proj(UpperCamelCase		)
              _lowercase					:				Any           =			self.text_norm(UpperCamelCase		)
  
              # image
              _lowercase					:				int           =			self.image_proj(UpperCamelCase		)
  
              return time_image_embeds + time_text_embeds
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       int	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 15_36		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				List[Any]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				int           =			nn.LayerNorm(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       torch.FloatTensor		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			self.image_proj(UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			self.image_norm(UpperCamelCase		)
              return time_image_embeds
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[int]	,      UpperCamelCase       :       int = 7_68	,      UpperCamelCase       :       int = 15_36		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				Dict           =			nn.Linear(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			nn.LayerNorm(UpperCamelCase		)
              _lowercase					:				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	,      2_56	,      3	,      padding=1	,      stride=2		)	,      nn.SiLU()	,      nn.Convad(2_56	,      4	,      3	,      padding=1		)	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       torch.FloatTensor	,      UpperCamelCase       :       torch.FloatTensor		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			self.image_proj(UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			self.image_norm(UpperCamelCase		)
              _lowercase					:				Optional[int]           =			self.input_hint_block(UpperCamelCase		)
              return time_image_embeds, hint
  
  
  
  
  class 						UpperCAmelCase__   (      nn.Module		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       int	,      UpperCamelCase       :       Tuple	,      UpperCamelCase       :       List[Any]=None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              _lowercase					:				Any           =			dtype
              _lowercase					:				List[Any]           =			nn.Parameter(torch.randn(1	,      UpperCamelCase		) / embed_dim**0.5		)
              _lowercase					:				Optional[int]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      dtype=self.dtype		)
              _lowercase					:				Optional[Any]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      dtype=self.dtype		)
              _lowercase					:				Union[str, Any]           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      dtype=self.dtype		)
              _lowercase					:				Union[str, Any]           =			num_heads
              _lowercase					:				Optional[Any]           =			embed_dim // self.num_heads
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			x.size()
  
              def shape(UpperCamelCase       :       Any		):
                     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                     _lowercase					:				int           =			x.view(UpperCamelCase	,      -1	,      self.num_heads	,      self.dim_per_head		)
                     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
                     _lowercase					:				Optional[Any]           =			x.transpose(1	,      2		)
                     # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                     _lowercase					:				Optional[int]           =			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)
                     _lowercase					:				Dict           =			x.transpose(1	,      2		)
                     return x
  
              _lowercase					:				List[str]           =			x.mean(dim=1	,      keepdim=UpperCamelCase		) + self.positional_embedding.to(x.dtype		)
              _lowercase					:				Any           =			torch.cat([class_token, x]	,      dim=1		)  # (bs, length+1, width)
  
              # (bs*n_heads, class_token_length, dim_per_head)
              _lowercase					:				List[str]           =			shape(self.q_proj(UpperCamelCase		)		)
              # (bs*n_heads, length+class_token_length, dim_per_head)
              _lowercase					:				List[str]           =			shape(self.k_proj(UpperCamelCase		)		)
              _lowercase					:				int           =			shape(self.v_proj(UpperCamelCase		)		)
  
              # (bs*n_heads, class_token_length, length+class_token_length):
              _lowercase					:				Dict           =			1 / math.sqrt(math.sqrt(self.dim_per_head		)		)
              _lowercase					:				Optional[Any]           =			torch.einsum('''bct,bcs->bts'''	,      q * scale	,      k * scale		)  # More stable with f16 than dividing afterwards
              _lowercase					:				Optional[Any]           =			torch.softmax(weight.float()	,      dim=-1		).type(weight.dtype		)
  
              # (bs*n_heads, dim_per_head, class_token_length)
              _lowercase					:				int           =			torch.einsum('''bts,bcs->bct'''	,      UpperCamelCase	,      UpperCamelCase		)
  
              # (bs, length+1, width)
              _lowercase					:				Optional[Any]           =			a.reshape(UpperCamelCase	,      -1	,      1		).transpose(1	,      2		)
  
              return a[:, 0, :]  # cls_token
style_context: 
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (DDIMScheduler,)
       UpperCAmelCase_         =  (('''eta''', 0.0), ('''num_inference_steps''', 5_0))
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      **UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			{
                  '''num_train_timesteps''': 10_00,
                  '''beta_start''': 0.0001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }
  
              config.update(**UpperCamelCase		)
              return config
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str	,      **UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			self.scheduler_classes[0]
              _lowercase					:				Dict           =			self.get_scheduler_config(**UpperCamelCase		)
              _lowercase					:				List[str]           =			scheduler_class(**UpperCamelCase		)
  
              _lowercase       ,	_lowercase					:				int           =			10, 0.0
  
              _lowercase					:				List[str]           =			self.dummy_model()
              _lowercase					:				int           =			self.dummy_sample_deter
  
              scheduler.set_timesteps(UpperCamelCase		)
  
              for t in scheduler.timesteps:
                     _lowercase					:				Union[str, Any]           =			model(UpperCamelCase	,      UpperCamelCase		)
                     _lowercase					:				List[Any]           =			scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              return sample
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			self.scheduler_classes[0]
              _lowercase					:				Tuple           =			self.get_scheduler_config(steps_offset=1		)
              _lowercase					:				Union[str, Any]           =			scheduler_class(**UpperCamelCase		)
              scheduler.set_timesteps(5		)
              assert torch.equal(scheduler.timesteps	,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,      [0.002, 0.02, 0.2, 2]		):
                     self.check_over_configs(beta_start=UpperCamelCase	,      beta_end=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              self.check_over_configs(thresholding=UpperCamelCase		)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=UpperCamelCase	,      prediction_type=UpperCamelCase	,      sample_max_value=UpperCamelCase	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, num_inference_steps in zip([1, 10, 50]	,      [10, 50, 5_00]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      num_inference_steps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, eta in zip([1, 10, 49]	,      [0.0, 0.5, 1.0]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      eta=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.scheduler_classes[0]
              _lowercase					:				str           =			self.get_scheduler_config()
              _lowercase					:				int           =			scheduler_class(**UpperCamelCase		)
  
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20	,      4_00		) - 0.14771		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80	,      9_60		) - 0.32460		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87	,      4_86		) - 0.00979		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99	,      9_98		) - 0.02		)		) < 1E-5
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			self.full_loop()
  
              _lowercase					:				Optional[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 172.0067		) < 1E-2
              assert abs(result_mean.item() - 0.223967		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			self.full_loop(prediction_type='''v_prediction'''		)
  
              _lowercase					:				int           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 52.5302		) < 1E-2
              assert abs(result_mean.item() - 0.0684		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				List[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Any           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.8295		) < 1E-2
              assert abs(result_mean.item() - 0.1951		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				Optional[int]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Dict           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.0784		) < 1E-2
              assert abs(result_mean.item() - 0.1941		) < 1E-3