

"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def       UpperCamelCase_					(     lowerCamelCase							:					torch.Tensor     , lowerCamelCase							:					int     , lowerCamelCase							:					bool = False     , lowerCamelCase							:					float = 1     , lowerCamelCase							:					float = 1     , lowerCamelCase							:					int = 1_0000     , )						->     List[str]:



       """simple docstring"""







       assert len(timesteps.shape   ) == 1, "Timesteps should be a 1d-array"

       __magic_name__			:		List[Any]        =     embedding_dim // 2
       __magic_name__			:		Any        =     -math.log(_lowercase   ) * torch.arange(
           start=0     , end=_lowercase     , dtype=torch.floataa     , device=timesteps.device   )
       __magic_name__			:		int        =     exponent / (half_dim - downscale_freq_shift)

       __magic_name__			:		Optional[int]        =     torch.exp(_lowercase   )
       __magic_name__			:		Union[str, Any]        =     timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       __magic_name__			:		List[str]        =     scale * emb

       # concat sine and cosine embeddings
       __magic_name__			:		str        =     torch.cat([torch.sin(_lowercase   ), torch.cos(_lowercase   )]     , dim=-1   )

       # flip sine and cosine embeddings
       if flip_sin_to_cos:
              __magic_name__			:		int        =     torch.cat([emb[:, half_dim:], emb[:, :half_dim]]     , dim=-1   )

       # zero pad
       if embedding_dim % 2 == 1:
              __magic_name__			:		Optional[Any]        =     torch.nn.functional.pad(_lowercase     , (0, 1, 0, 0)   )
       return emb







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Optional[Any]=False     , lowerCamelCase							:					Optional[Any]=0   )						->     List[Any]:



       """simple docstring"""







       __magic_name__			:		int        =     np.arange(_lowercase     , dtype=np.floataa   )
       __magic_name__			:		List[Any]        =     np.arange(_lowercase     , dtype=np.floataa   )
       __magic_name__			:		Optional[int]        =     np.meshgrid(_lowercase     , _lowercase   )  # here w goes first
       __magic_name__			:		int        =     np.stack(_lowercase     , axis=0   )

       __magic_name__			:		Union[str, Any]        =     grid.reshape([2, 1, grid_size, grid_size]   )
       __magic_name__			:		Union[str, Any]        =     get_ad_sincos_pos_embed_from_grid(_lowercase     , _lowercase   )
       if cls_token and extra_tokens > 0:
              __magic_name__			:		List[Any]        =     np.concatenate([np.zeros([extra_tokens, embed_dim]   ), pos_embed]     , axis=0   )
       return pos_embed







def       UpperCamelCase_					(     lowerCamelCase							:					Dict     , lowerCamelCase							:					Tuple   )						->     Dict:



       """simple docstring"""







       if embed_dim % 2 != 0:
              raise ValueError('''embed_dim must be divisible by 2'''   )

       # use half of dimensions to encode grid_h
       __magic_name__			:		List[Any]        =     get_ad_sincos_pos_embed_from_grid(embed_dim // 2     , grid[0]   )  # (H*W, D/2)
       __magic_name__			:		str        =     get_ad_sincos_pos_embed_from_grid(embed_dim // 2     , grid[1]   )  # (H*W, D/2)

       __magic_name__			:		Union[str, Any]        =     np.concatenate([emb_h, emb_w]     , axis=1   )  # (H*W, D)
       return emb







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					Tuple   )						->     List[Any]:



       """simple docstring"""







       if embed_dim % 2 != 0:
              raise ValueError('''embed_dim must be divisible by 2'''   )

       __magic_name__			:		Optional[int]        =     np.arange(embed_dim // 2     , dtype=np.floataa   )
       omega /= embed_dim / 2.0
       __magic_name__			:		Optional[int]        =     1.0 / 1_0000**omega  # (D/2,)

       __magic_name__			:		int        =     pos.reshape(-1   )  # (M,)
       __magic_name__			:		str        =     np.einsum('''m,d->md'''     , _lowercase     , _lowercase   )  # (M, D/2), outer product

       __magic_name__			:		Dict        =     np.sin(_lowercase   )  # (M, D/2)
       __magic_name__			:		List[str]        =     np.cos(_lowercase   )  # (M, D/2)

       __magic_name__			:		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]       ,       snake_case					: List[str]=224       ,       snake_case					: Optional[int]=224       ,       snake_case					: List[Any]=16       ,       snake_case					: Any=3       ,       snake_case					: List[Any]=768       ,       snake_case					: Any=False       ,       snake_case					: int=True       ,       snake_case					: str=True       ,       )	->			Optional[int]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Optional[int]        =     (height // patch_size) * (width // patch_size)
         __magic_name__			:		Dict        =     flatten
         __magic_name__			:		Optional[Any]        =     layer_norm

         __magic_name__			:		Tuple        =     nn.Convad(
             snake_case       ,       snake_case       ,       kernel_size=(patch_size, patch_size)       ,       stride=snake_case       ,       bias=snake_case      )
         if layer_norm:
                __magic_name__			:		Tuple        =     nn.LayerNorm(snake_case       ,       elementwise_affine=snake_case       ,       eps=1e-6      )
         else:
                __magic_name__			:		Optional[int]        =     None

         __magic_name__			:		Union[str, Any]        =     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 							_UpperCAmelCase      ( self					: List[str]       ,       snake_case					: List[Any]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     self.proj(snake_case      )
         if self.flatten:
                __magic_name__			:		int        =     latent.flatten(2      ).transpose(1       ,       2      )  # BCHW -> BNC
         if self.layer_norm:
                __magic_name__			:		List[Any]        =     self.norm(snake_case      )
         return latent + self.pos_embed

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: str = "silu"       ,       snake_case					: int = None       ,       snake_case					: Optional[str] = None       ,       snake_case					: Dict=None       ,       )	->			int:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Dict        =     nn.Linear(snake_case       ,       snake_case      )

         if cond_proj_dim is not None:
                __magic_name__			:		Optional[int]        =     nn.Linear(snake_case       ,       snake_case       ,       bias=snake_case      )
         else:
                __magic_name__			:		Optional[Any]        =     None

         __magic_name__			:		Any        =     get_activation(snake_case      )

         if out_dim is not None:
                __magic_name__			:		str        =     out_dim
         else:
                __magic_name__			:		List[Any]        =     time_embed_dim
         __magic_name__			:		int        =     nn.Linear(snake_case       ,       snake_case      )

         if post_act_fn is None:
                __magic_name__			:		Optional[int]        =     None
         else:
                __magic_name__			:		Tuple        =     get_activation(snake_case      )




  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: Dict       ,       snake_case					: Optional[int]=None      )	->			List[str]:


         '''simple docstring'''





         if condition is not None:
                __magic_name__			:		str        =     sample + self.cond_proj(snake_case      )
         __magic_name__			:		List[str]        =     self.linear_a(snake_case      )

         if self.act is not None:
                __magic_name__			:		Any        =     self.act(snake_case      )

         __magic_name__			:		Dict        =     self.linear_a(snake_case      )

         if self.post_act is not None:
                __magic_name__			:		Optional[int]        =     self.post_act(snake_case      )
         return sample

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: int       ,       snake_case					: int       ,       snake_case					: bool       ,       snake_case					: float      )	->			List[Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		int        =     num_channels
         __magic_name__			:		int        =     flip_sin_to_cos
         __magic_name__			:		Tuple        =     downscale_freq_shift




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Tuple      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     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        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Dict       ,       snake_case					: int = 256       ,       snake_case					: float = 1.0       ,       snake_case					: Optional[Any]=True       ,       snake_case					: Dict=True       ,       snake_case					: Any=False      )	->			List[str]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		List[str]        =     nn.Parameter(torch.randn(snake_case      ) * scale       ,       requires_grad=snake_case      )
         __magic_name__			:		Any        =     log
         __magic_name__			:		Optional[Any]        =     flip_sin_to_cos

         if set_W_to_weight:
                # to delete later
                __magic_name__			:		Optional[Any]        =     nn.Parameter(torch.randn(snake_case      ) * scale       ,       requires_grad=snake_case      )

                __magic_name__			:		Tuple        =     self.W




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: int      )	->			List[Any]:


         '''simple docstring'''





         if self.log:
                __magic_name__			:		Union[str, Any]        =     torch.log(snake_case      )

         __magic_name__			:		List[Any]        =     x[:, None] * self.weight[None, :] * 2 * np.pi

         if self.flip_sin_to_cos:
                __magic_name__			:		int        =     torch.cat([torch.cos(snake_case      ), torch.sin(snake_case      )]       ,       dim=-1      )
         else:
                __magic_name__			:		Any        =     torch.cat([torch.sin(snake_case      ), torch.cos(snake_case      )]       ,       dim=-1      )
         return out

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[int]       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int       ,       )	->			Optional[Any]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Dict        =     height
         __magic_name__			:		int        =     width
         __magic_name__			:		Dict        =     num_embed
         __magic_name__			:		str        =     embed_dim

         __magic_name__			:		Optional[Any]        =     nn.Embedding(self.num_embed       ,       snake_case      )
         __magic_name__			:		Tuple        =     nn.Embedding(self.height       ,       snake_case      )
         __magic_name__			:		Union[str, Any]        =     nn.Embedding(self.width       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[int]      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.emb(snake_case      )

         __magic_name__			:		Tuple        =     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
         __magic_name__			:		List[str]        =     height_emb.unsqueeze(2      )

         __magic_name__			:		Union[str, 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
         __magic_name__			:		Optional[Any]        =     width_emb.unsqueeze(1      )

         __magic_name__			:		List[Any]        =     height_emb + width_emb

         # 1 x H x W x D -> 1 x L xD
         __magic_name__			:		int        =     pos_emb.view(1       ,       self.height * self.width       ,       -1      )

         __magic_name__			:		Optional[Any]        =     emb + pos_emb[:, : emb.shape[1], :]

         return emb

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Union[str, Any]       ,       snake_case					: Any       ,       snake_case					: Tuple       ,       snake_case					: Dict      )	->			List[str]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Dict        =     dropout_prob > 0
         __magic_name__			:		List[str]        =     nn.Embedding(num_classes + use_cfg_embedding       ,       snake_case      )
         __magic_name__			:		Optional[int]        =     num_classes
         __magic_name__			:		Tuple        =     dropout_prob



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: Union[str, Any]       ,       snake_case					: Optional[int]=None      )	->			str:


         '''simple docstring'''





         if force_drop_ids is None:
                __magic_name__			:		str        =     torch.rand(labels.shape[0]       ,       device=labels.device      ) < self.dropout_prob
         else:
                __magic_name__			:		str        =     torch.tensor(force_drop_ids == 1      )
         __magic_name__			:		Union[str, Any]        =     torch.where(snake_case       ,       self.num_classes       ,       snake_case      )
         return labels




  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: torch.LongTensor       ,       snake_case					: Optional[Any]=None      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.dropout_prob > 0
         if (self.training and use_dropout) or (force_drop_ids is not None):
                __magic_name__			:		Union[str, Any]        =     self.token_drop(snake_case       ,       snake_case      )
         __magic_name__			:		Tuple        =     self.embedding_table(snake_case      )
         return embeddings

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Union[str, Any]       ,       snake_case					: int = 1024       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 10       ,       )	->			List[Any]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		int        =     num_image_text_embeds
         __magic_name__			:		Optional[Any]        =     nn.Linear(snake_case       ,       self.num_image_text_embeds * cross_attention_dim      )
         __magic_name__			:		Optional[Any]        =     nn.Linear(snake_case       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     text_embeds.shape[0]

         # image
         __magic_name__			:		Tuple        =     self.image_embeds(snake_case      )
         __magic_name__			:		Union[str, Any]        =     image_text_embeds.reshape(snake_case       ,       self.num_image_text_embeds       ,       -1      )

         # text
         __magic_name__			:		Optional[Any]        =     self.text_proj(snake_case      )

         return torch.cat([image_text_embeds, text_embeds]       ,       dim=1      )

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[int]       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 32       ,       )	->			Optional[Any]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		List[str]        =     num_image_text_embeds
         __magic_name__			:		Any        =     nn.Linear(snake_case       ,       self.num_image_text_embeds * cross_attention_dim      )
         __magic_name__			:		int        =     nn.LayerNorm(snake_case      )




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: torch.FloatTensor      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     image_embeds.shape[0]

         # image
         __magic_name__			:		Any        =     self.image_embeds(snake_case      )
         __magic_name__			:		List[Any]        =     image_embeds.reshape(snake_case       ,       self.num_image_text_embeds       ,       -1      )
         __magic_name__			:		List[Any]        =     self.norm(snake_case      )
         return image_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[str]       ,       snake_case					: List[Any]       ,       snake_case					: List[str]       ,       snake_case					: str=0.1      )	->			str:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		List[str]        =     Timesteps(num_channels=256       ,       flip_sin_to_cos=snake_case       ,       downscale_freq_shift=1      )
         __magic_name__			:		List[Any]        =     TimestepEmbedding(in_channels=256       ,       time_embed_dim=snake_case      )
         __magic_name__			:		Optional[int]        =     LabelEmbedding(snake_case       ,       snake_case       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: Any       ,       snake_case					: str       ,       snake_case					: List[str]=None      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.time_proj(snake_case      )
         __magic_name__			:		Optional[int]        =     self.timestep_embedder(timesteps_proj.to(dtype=snake_case      )      )  # (N, D)

         __magic_name__			:		Union[str, Any]        =     self.class_embedder(snake_case      )  # (N, D)

         __magic_name__			:		List[str]        =     timesteps_emb + class_labels  # (N, D)

         return conditioning

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int = 64      )	->			Dict:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		str        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		Union[str, Any]        =     AttentionPooling(snake_case       ,       snake_case      )
         __magic_name__			:		Dict        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		Optional[int]        =     nn.LayerNorm(snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: List[str]      )	->			int:


         '''simple docstring'''





         __magic_name__			:		str        =     self.norma(snake_case      )
         __magic_name__			:		Dict        =     self.pool(snake_case      )
         __magic_name__			:		Optional[Any]        =     self.proj(snake_case      )
         __magic_name__			:		Dict        =     self.norma(snake_case      )
         return hidden_states

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[Any]       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			Any:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		Optional[int]        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.text_proj(snake_case      )
         __magic_name__			:		int        =     self.text_norm(snake_case      )

         # image
         __magic_name__			:		List[str]        =     self.image_proj(snake_case      )

         return time_image_embeds + time_text_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[Any]       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			Any:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Any        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		Optional[Any]        =     nn.LayerNorm(snake_case      )




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: torch.FloatTensor      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Dict        =     self.image_proj(snake_case      )
         __magic_name__			:		str        =     self.image_norm(snake_case      )
         return time_image_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Union[str, Any]       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			Union[str, Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Any        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		int        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		List[str]        =     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					: Optional[Any]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     self.image_proj(snake_case      )
         __magic_name__			:		Optional[Any]        =     self.image_norm(snake_case      )
         __magic_name__			:		int        =     self.input_hint_block(snake_case      )
         return time_image_embeds, hint

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: int       ,       snake_case					: str       ,       snake_case					: Union[str, Any]       ,       snake_case					: Optional[int]=None      )	->			List[Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		str        =     dtype
         __magic_name__			:		int        =     nn.Parameter(torch.randn(1       ,       snake_case      ) / embed_dim**0.5      )
         __magic_name__			:		List[str]        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[int]        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		Union[str, Any]        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		str        =     num_heads
         __magic_name__			:		List[str]        =     embed_dim // self.num_heads




  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: List[Any]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		str        =     x.size()

         def shape(snake_case					: Union[str, Any]      ):
                # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                __magic_name__			:		Dict        =     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)
                __magic_name__			:		Any        =     x.transpose(1       ,       2      )
                # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                __magic_name__			:		List[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)
                __magic_name__			:		Optional[int]        =     x.transpose(1       ,       2      )
                return x

         __magic_name__			:		List[str]        =     x.mean(dim=1       ,       keepdim=snake_case      ) + self.positional_embedding.to(x.dtype      )
         __magic_name__			:		Optional[int]        =     torch.cat([class_token, x]       ,       dim=1      )  # (bs, length+1, width)

         # (bs*n_heads, class_token_length, dim_per_head)
         __magic_name__			:		Union[str, Any]        =     shape(self.q_proj(snake_case      )      )
         # (bs*n_heads, length+class_token_length, dim_per_head)
         __magic_name__			:		Any        =     shape(self.k_proj(snake_case      )      )
         __magic_name__			:		int        =     shape(self.v_proj(snake_case      )      )

         # (bs*n_heads, class_token_length, length+class_token_length):
         __magic_name__			:		List[Any]        =     1 / math.sqrt(math.sqrt(self.dim_per_head      )      )
         __magic_name__			:		Dict        =     torch.einsum('''bct,bcs->bts'''       ,       q * scale       ,       k * scale      )  # More stable with f16 than dividing afterwards
         __magic_name__			:		Dict        =     torch.softmax(weight.float()       ,       dim=-1      ).type(weight.dtype      )

         # (bs*n_heads, dim_per_head, class_token_length)
         __magic_name__			:		int        =     torch.einsum('''bts,bcs->bct'''       ,       snake_case       ,       snake_case      )

         # (bs, length+1, width)
         __magic_name__			:		Union[str, Any]        =     a.reshape(snake_case       ,       -1       ,       1      ).transpose(1       ,       2      )

         return a[:, 0, :]  # cls_token




'''simple docstring'''



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      int     ,   _lowercase   :      bool = False     ,   _lowercase   :      float = 1     ,   _lowercase   :      float = 1     ,   _lowercase   :      int = 10000     ,   ):





     '''simple docstring'''






     assert len(timesteps.shape    ) == 1, "Timesteps should be a 1d-array"

     __UpperCAmelCase      :					List[Any]    				=     embedding_dim // 2
     __UpperCAmelCase      :					Any    				=     -math.log(_lowercase    ) * torch.arange(
         start=0     ,   end=_lowercase     ,   dtype=torch.floataa     ,   device=timesteps.device    )
     __UpperCAmelCase      :					int    				=     exponent / (half_dim - downscale_freq_shift)

     __UpperCAmelCase      :					Optional[int]    				=     torch.exp(_lowercase    )
     __UpperCAmelCase      :					Union[str, Any]    				=     timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     __UpperCAmelCase      :					List[str]    				=     scale * emb

     # concat sine and cosine embeddings
     __UpperCAmelCase      :					str    				=     torch.cat([torch.sin(_lowercase    ), torch.cos(_lowercase    )]     ,   dim=-1    )

     # flip sine and cosine embeddings
     if flip_sin_to_cos:
          __UpperCAmelCase      :					int    				=     torch.cat([emb[:, half_dim:], emb[:, :half_dim]]     ,   dim=-1    )

     # zero pad
     if embedding_dim % 2 == 1:
          __UpperCAmelCase      :					Optional[Any]    				=     torch.nn.functional.pad(_lowercase     ,   (0, 1, 0, 0)    )
     return emb




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Union[str, Any]     ,   _lowercase   :      Optional[Any]=False     ,   _lowercase   :      Optional[Any]=0    ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					List[Any]    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					Optional[int]    				=     np.meshgrid(_lowercase     ,   _lowercase    )  # here w goes first
     __UpperCAmelCase      :					int    				=     np.stack(_lowercase     ,   axis=0    )

     __UpperCAmelCase      :					Union[str, Any]    				=     grid.reshape([2, 1, grid_size, grid_size]    )
     __UpperCAmelCase      :					Union[str, Any]    				=     get_ad_sincos_pos_embed_from_grid(_lowercase     ,   _lowercase    )
     if cls_token and extra_tokens > 0:
          __UpperCAmelCase      :					List[Any]    				=     np.concatenate([np.zeros([extra_tokens, embed_dim]    ), pos_embed]     ,   axis=0    )
     return pos_embed




def       _a      (		_lowercase   :      Dict     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






     if embed_dim % 2 != 0:
          raise ValueError('''embed_dim must be divisible by 2'''    )

     # use half of dimensions to encode grid_h
     __UpperCAmelCase      :					List[Any]    				=     get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,   grid[0]    )  # (H*W, D/2)
     __UpperCAmelCase      :					str    				=     get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,   grid[1]    )  # (H*W, D/2)

     __UpperCAmelCase      :					Union[str, Any]    				=     np.concatenate([emb_h, emb_w]     ,   axis=1    )  # (H*W, D)
     return emb




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






     if embed_dim % 2 != 0:
          raise ValueError('''embed_dim must be divisible by 2'''    )

     __UpperCAmelCase      :					Optional[int]    				=     np.arange(embed_dim // 2     ,   dtype=np.floataa    )
     omega /= embed_dim / 2.0
     __UpperCAmelCase      :					Optional[int]    				=     1.0 / 10000**omega  # (D/2,)

     __UpperCAmelCase      :					int    				=     pos.reshape(-1    )  # (M,)
     __UpperCAmelCase      :					str    				=     np.einsum('''m,d->md'''     ,   _lowercase     ,   _lowercase    )  # (M, D/2), outer product

     __UpperCAmelCase      :					Dict    				=     np.sin(_lowercase    )  # (M, D/2)
     __UpperCAmelCase      :					List[str]    				=     np.cos(_lowercase    )  # (M, D/2)

     __UpperCAmelCase      :					List[str]    				=     np.concatenate([emb_sin, emb_cos]     ,   axis=1    )  # (M, D)
     return emb



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[str]				,					snake_case	:						List[str]=224				,					snake_case	:						Optional[int]=224				,					snake_case	:						List[Any]=16				,					snake_case	:						Any=3				,					snake_case	:						List[Any]=768				,					snake_case	:						Any=False				,					snake_case	:						int=True				,					snake_case	:						str=True				,					)		->				Optional[int]:
          super().__init__()

          __UpperCAmelCase      :					Optional[int]    				=     (height // patch_size) * (width // patch_size)
          __UpperCAmelCase      :					Dict    				=     flatten
          __UpperCAmelCase      :					Optional[Any]    				=     layer_norm

          __UpperCAmelCase      :					Tuple    				=     nn.Convad(
              snake_case				,					snake_case				,					kernel_size=(patch_size, patch_size)				,					stride=snake_case				,					bias=snake_case     )
          if layer_norm:
               __UpperCAmelCase      :					Tuple    				=     nn.LayerNorm(snake_case				,					elementwise_affine=snake_case				,					eps=1E-6     )
          else:
               __UpperCAmelCase      :					Optional[int]    				=     None

          __UpperCAmelCase      :					Union[str, Any]    				=     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 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						List[Any]     )		->				List[Any]:
          __UpperCAmelCase      :					List[Any]    				=     self.proj(snake_case     )
          if self.flatten:
               __UpperCAmelCase      :					int    				=     latent.flatten(2     ).transpose(1				,					2     )  # BCHW -> BNC
          if self.layer_norm:
               __UpperCAmelCase      :					List[Any]    				=     self.norm(snake_case     )
          return latent + self.pos_embed



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[Any]				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						str = "silu"				,					snake_case	:						int = None				,					snake_case	:						Optional[str] = None				,					snake_case	:						Dict=None				,					)		->				int:
          super().__init__()

          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )

          if cond_proj_dim is not None:
               __UpperCAmelCase      :					Optional[int]    				=     nn.Linear(snake_case				,					snake_case				,					bias=snake_case     )
          else:
               __UpperCAmelCase      :					Optional[Any]    				=     None

          __UpperCAmelCase      :					Any    				=     get_activation(snake_case     )

          if out_dim is not None:
               __UpperCAmelCase      :					str    				=     out_dim
          else:
               __UpperCAmelCase      :					List[Any]    				=     time_embed_dim
          __UpperCAmelCase      :					int    				=     nn.Linear(snake_case				,					snake_case     )

          if post_act_fn is None:
               __UpperCAmelCase      :					Optional[int]    				=     None
          else:
               __UpperCAmelCase      :					Tuple    				=     get_activation(snake_case     )






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						Dict				,					snake_case	:						Optional[int]=None     )		->				List[str]:
          if condition is not None:
               __UpperCAmelCase      :					str    				=     sample + self.cond_proj(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     self.linear_a(snake_case     )

          if self.act is not None:
               __UpperCAmelCase      :					Any    				=     self.act(snake_case     )

          __UpperCAmelCase      :					Dict    				=     self.linear_a(snake_case     )

          if self.post_act is not None:
               __UpperCAmelCase      :					Optional[int]    				=     self.post_act(snake_case     )
          return sample



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						int				,					snake_case	:						int				,					snake_case	:						bool				,					snake_case	:						float     )		->				List[Any]:
          super().__init__()
          __UpperCAmelCase      :					int    				=     num_channels
          __UpperCAmelCase      :					int    				=     flip_sin_to_cos
          __UpperCAmelCase      :					Tuple    				=     downscale_freq_shift






     def 					lowerCamelCase__					(							self	:						str				,					snake_case	:						Tuple     )		->				List[Any]:
          __UpperCAmelCase      :					Optional[int]    				=     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	:						Dict				,					snake_case	:						int = 256				,					snake_case	:						float = 1.0				,					snake_case	:						Optional[Any]=True				,					snake_case	:						Dict=True				,					snake_case	:						Any=False     )		->				List[str]:
          super().__init__()
          __UpperCAmelCase      :					List[str]    				=     nn.Parameter(torch.randn(snake_case     ) * scale				,					requires_grad=snake_case     )
          __UpperCAmelCase      :					Any    				=     log
          __UpperCAmelCase      :					Optional[Any]    				=     flip_sin_to_cos

          if set_W_to_weight:
               # to delete later
               __UpperCAmelCase      :					Optional[Any]    				=     nn.Parameter(torch.randn(snake_case     ) * scale				,					requires_grad=snake_case     )

               __UpperCAmelCase      :					Tuple    				=     self.W






     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int     )		->				List[Any]:
          if self.log:
               __UpperCAmelCase      :					Union[str, Any]    				=     torch.log(snake_case     )

          __UpperCAmelCase      :					List[Any]    				=     x[:, None] * self.weight[None, :] * 2 * np.pi

          if self.flip_sin_to_cos:
               __UpperCAmelCase      :					int    				=     torch.cat([torch.cos(snake_case     ), torch.sin(snake_case     )]				,					dim=-1     )
          else:
               __UpperCAmelCase      :					Any    				=     torch.cat([torch.sin(snake_case     ), torch.cos(snake_case     )]				,					dim=-1     )
          return out



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[int]				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					)		->				Optional[Any]:
          super().__init__()

          __UpperCAmelCase      :					Dict    				=     height
          __UpperCAmelCase      :					int    				=     width
          __UpperCAmelCase      :					Dict    				=     num_embed
          __UpperCAmelCase      :					str    				=     embed_dim

          __UpperCAmelCase      :					Optional[Any]    				=     nn.Embedding(self.num_embed				,					snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Embedding(self.height				,					snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     nn.Embedding(self.width				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						Optional[int]     )		->				Dict:
          __UpperCAmelCase      :					Tuple    				=     self.emb(snake_case     )

          __UpperCAmelCase      :					Tuple    				=     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
          __UpperCAmelCase      :					List[str]    				=     height_emb.unsqueeze(2     )

          __UpperCAmelCase      :					Union[str, 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
          __UpperCAmelCase      :					Optional[Any]    				=     width_emb.unsqueeze(1     )

          __UpperCAmelCase      :					List[Any]    				=     height_emb + width_emb

          # 1 x H x W x D -> 1 x L xD
          __UpperCAmelCase      :					int    				=     pos_emb.view(1				,					self.height * self.width				,					-1     )

          __UpperCAmelCase      :					Optional[Any]    				=     emb + pos_emb[:, : emb.shape[1], :]

          return emb



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						Any				,					snake_case	:						Tuple				,					snake_case	:						Dict     )		->				List[str]:
          super().__init__()
          __UpperCAmelCase      :					Dict    				=     dropout_prob > 0
          __UpperCAmelCase      :					List[str]    				=     nn.Embedding(num_classes + use_cfg_embedding				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     num_classes
          __UpperCAmelCase      :					Tuple    				=     dropout_prob

     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						Union[str, Any]				,					snake_case	:						Optional[int]=None     )		->				str:
          if force_drop_ids is None:
               __UpperCAmelCase      :					str    				=     torch.rand(labels.shape[0]				,					device=labels.device     ) < self.dropout_prob
          else:
               __UpperCAmelCase      :					str    				=     torch.tensor(force_drop_ids == 1     )
          __UpperCAmelCase      :					Union[str, Any]    				=     torch.where(snake_case				,					self.num_classes				,					snake_case     )
          return labels






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						torch.LongTensor				,					snake_case	:						Optional[Any]=None     )		->				int:
          __UpperCAmelCase      :					Tuple    				=     self.dropout_prob > 0
          if (self.training and use_dropout) or (force_drop_ids is not None):
               __UpperCAmelCase      :					Union[str, Any]    				=     self.token_drop(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Tuple    				=     self.embedding_table(snake_case     )
          return embeddings



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						int = 1024				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 10				,					)		->				List[Any]:
          super().__init__()

          __UpperCAmelCase      :					int    				=     num_image_text_embeds
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				int:
          __UpperCAmelCase      :					Tuple    				=     text_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Tuple    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     image_text_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )

          # text
          __UpperCAmelCase      :					Optional[Any]    				=     self.text_proj(snake_case     )

          return torch.cat([image_text_embeds, text_embeds]				,					dim=1     )



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[int]				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 32				,					)		->				Optional[Any]:
          super().__init__()

          __UpperCAmelCase      :					List[str]    				=     num_image_text_embeds
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor     )		->				Union[str, Any]:
          __UpperCAmelCase      :					Tuple    				=     image_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Any    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					List[Any]    				=     image_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )
          __UpperCAmelCase      :					List[Any]    				=     self.norm(snake_case     )
          return image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[str]				,					snake_case	:						List[Any]				,					snake_case	:						List[str]				,					snake_case	:						str=0.1     )		->				str:
          super().__init__()

          __UpperCAmelCase      :					List[str]    				=     Timesteps(num_channels=256				,					flip_sin_to_cos=snake_case				,					downscale_freq_shift=1     )
          __UpperCAmelCase      :					List[Any]    				=     TimestepEmbedding(in_channels=256				,					time_embed_dim=snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     LabelEmbedding(snake_case				,					snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						Any				,					snake_case	:						str				,					snake_case	:						List[str]=None     )		->				Tuple:
          __UpperCAmelCase      :					Tuple    				=     self.time_proj(snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     self.timestep_embedder(timesteps_proj.to(dtype=snake_case     )     )  # (N, D)

          __UpperCAmelCase      :					Union[str, Any]    				=     self.class_embedder(snake_case     )  # (N, D)

          __UpperCAmelCase      :					List[str]    				=     timesteps_emb + class_labels  # (N, D)

          return conditioning



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						str				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int = 64     )		->				Dict:
          super().__init__()
          __UpperCAmelCase      :					str    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     AttentionPooling(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						str				,					snake_case	:						List[str]     )		->				int:
          __UpperCAmelCase      :					str    				=     self.norma(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.pool(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.proj(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.norma(snake_case     )
          return hidden_states



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Any:
          super().__init__()
          __UpperCAmelCase      :					Tuple    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Linear(snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Optional[int]:
          # text
          __UpperCAmelCase      :					Tuple    				=     self.text_proj(snake_case     )
          __UpperCAmelCase      :					int    				=     self.text_norm(snake_case     )

          # image
          __UpperCAmelCase      :					List[str]    				=     self.image_proj(snake_case     )

          return time_image_embeds + time_text_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Any:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						torch.FloatTensor     )		->				Any:
          # image
          __UpperCAmelCase      :					Dict    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					str    				=     self.image_norm(snake_case     )
          return time_image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Union[str, Any]:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     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 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Dict:
          # image
          __UpperCAmelCase      :					Union[str, Any]    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.image_norm(snake_case     )
          __UpperCAmelCase      :					int    				=     self.input_hint_block(snake_case     )
          return time_image_embeds, hint



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						int				,					snake_case	:						str				,					snake_case	:						Union[str, Any]				,					snake_case	:						Optional[int]=None     )		->				List[Any]:
          super().__init__()
          __UpperCAmelCase      :					str    				=     dtype
          __UpperCAmelCase      :					int    				=     nn.Parameter(torch.randn(1				,					snake_case     ) / embed_dim**0.5     )
          __UpperCAmelCase      :					List[str]    				=     nn.Linear(snake_case				,					snake_case				,					dtype=self.dtype     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.Linear(snake_case				,					snake_case				,					dtype=self.dtype     )
          __UpperCAmelCase      :					Union[str, Any]    				=     nn.Linear(snake_case				,					snake_case				,					dtype=self.dtype     )
          __UpperCAmelCase      :					str    				=     num_heads
          __UpperCAmelCase      :					List[str]    				=     embed_dim // self.num_heads






     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						List[Any]     )		->				Optional[int]:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					str    				=     x.size()

          def shape(snake_case	:						Union[str, Any]     ):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               __UpperCAmelCase      :					Dict    				=     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)
               __UpperCAmelCase      :					Any    				=     x.transpose(1				,					2     )
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               __UpperCAmelCase      :					List[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)
               __UpperCAmelCase      :					Optional[int]    				=     x.transpose(1				,					2     )
               return x

          __UpperCAmelCase      :					List[str]    				=     x.mean(dim=1				,					keepdim=snake_case     ) + self.positional_embedding.to(x.dtype     )
          __UpperCAmelCase      :					Optional[int]    				=     torch.cat([class_token, x]				,					dim=1     )  # (bs, length+1, width)

          # (bs*n_heads, class_token_length, dim_per_head)
          __UpperCAmelCase      :					Union[str, Any]    				=     shape(self.q_proj(snake_case     )     )
          # (bs*n_heads, length+class_token_length, dim_per_head)
          __UpperCAmelCase      :					Any    				=     shape(self.k_proj(snake_case     )     )
          __UpperCAmelCase      :					int    				=     shape(self.v_proj(snake_case     )     )

          # (bs*n_heads, class_token_length, length+class_token_length):
          __UpperCAmelCase      :					List[Any]    				=     1 / math.sqrt(math.sqrt(self.dim_per_head     )     )
          __UpperCAmelCase      :					Dict    				=     torch.einsum('''bct,bcs->bts'''				,					q * scale				,					k * scale     )  # More stable with f16 than dividing afterwards
          __UpperCAmelCase      :					Dict    				=     torch.softmax(weight.float()				,					dim=-1     ).type(weight.dtype     )

          # (bs*n_heads, dim_per_head, class_token_length)
          __UpperCAmelCase      :					int    				=     torch.einsum('''bts,bcs->bct'''				,					snake_case				,					snake_case     )

          # (bs, length+1, width)
          __UpperCAmelCase      :					Union[str, Any]    				=     a.reshape(snake_case				,					-1				,					1     ).transpose(1				,					2     )

          return a[:, 0, :]  # cls_token