





import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = False					,		_SCREAMING_SNAKE_CASE = 1					,		_SCREAMING_SNAKE_CASE = 1					,		_SCREAMING_SNAKE_CASE = 1_00_00					,		)					->  List[str]:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE						=		embedding_dim // 2
      SCREAMING_SNAKE_CASE						=		-math.log(_SCREAMING_SNAKE_CASE      ) * torch.arange(
          start=0					,		end=_SCREAMING_SNAKE_CASE					,		dtype=torch.floataa					,		device=timesteps.device      )
      SCREAMING_SNAKE_CASE						=		exponent / (half_dim - downscale_freq_shift)

      SCREAMING_SNAKE_CASE						=		torch.exp(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		timesteps[:, None].float() * emb[None, :]

      # scale embeddings
      SCREAMING_SNAKE_CASE						=		scale * emb

      # concat sine and cosine embeddings
      SCREAMING_SNAKE_CASE						=		torch.cat([torch.sin(_SCREAMING_SNAKE_CASE      ), torch.cos(_SCREAMING_SNAKE_CASE      )]					,		dim=-1      )

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

      # zero pad
      if embedding_dim % 2 == 1:
            SCREAMING_SNAKE_CASE						=		torch.nn.functional.pad(_SCREAMING_SNAKE_CASE					,		(0, 1, 0, 0)      )
      return emb



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=0      )					->  Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		np.arange(_SCREAMING_SNAKE_CASE					,		dtype=np.floataa      )
      SCREAMING_SNAKE_CASE						=		np.arange(_SCREAMING_SNAKE_CASE					,		dtype=np.floataa      )
      SCREAMING_SNAKE_CASE						=		np.meshgrid(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )  # here w goes first
      SCREAMING_SNAKE_CASE						=		np.stack(_SCREAMING_SNAKE_CASE					,		axis=0      )

      SCREAMING_SNAKE_CASE						=		grid.reshape([2, 1, grid_size, grid_size]      )
      SCREAMING_SNAKE_CASE						=		get_ad_sincos_pos_embed_from_grid(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )
      if cls_token and extra_tokens > 0:
            SCREAMING_SNAKE_CASE						=		np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]					,		axis=0      )
      return pos_embed



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  Union[str, Any]:


      '''simple docstring'''


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

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

      SCREAMING_SNAKE_CASE						=		np.concatenate([emb_h, emb_w]					,		axis=1      )  # (H*W, D)
      return emb



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE						=		np.arange(embed_dim // 2					,		dtype=np.floataa      )
      omega /= embed_dim / 2.0
      SCREAMING_SNAKE_CASE						=		1.0 / 1_00_00**omega  # (D/2,)

      SCREAMING_SNAKE_CASE						=		pos.reshape(-1      )  # (M,)
      SCREAMING_SNAKE_CASE						=		np.einsum("""m,d->md"""					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )  # (M, D/2), outer product

      SCREAMING_SNAKE_CASE						=		np.sin(_SCREAMING_SNAKE_CASE      )  # (M, D/2)
      SCREAMING_SNAKE_CASE						=		np.cos(_SCREAMING_SNAKE_CASE      )  # (M, D/2)

      SCREAMING_SNAKE_CASE						=		np.concatenate([emb_sin, emb_cos]					,		axis=1      )  # (M, D)
      return emb

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		(height // patch_size) * (width // patch_size)
           SCREAMING_SNAKE_CASE						=		flatten
           SCREAMING_SNAKE_CASE						=		layer_norm

           SCREAMING_SNAKE_CASE						=		nn.Convad(
               lowerCamelCase__		,lowerCamelCase__		,kernel_size=(patch_size, patch_size)		,stride=lowerCamelCase__		,bias=lowerCamelCase__			)
           if layer_norm:
                 SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__		,elementwise_affine=lowerCamelCase__		,eps=1e-6			)
           else:
                 SCREAMING_SNAKE_CASE						=		None

           SCREAMING_SNAKE_CASE						=		get_ad_sincos_pos_embed(lowerCamelCase__		,int(num_patches**0.5			)			)
           self.register_buffer("""pos_embed"""		,torch.from_numpy(lowerCamelCase__			).float().unsqueeze(0			)		,persistent=lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       List[Any]			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.proj(lowerCamelCase__			)
           if self.flatten:
                 SCREAMING_SNAKE_CASE						=		latent.flatten(2			).transpose(1		,2			)  # BCHW -> BNC
           if self.layer_norm:
                 SCREAMING_SNAKE_CASE						=		self.norm(lowerCamelCase__			)
           return latent + self.pos_embed

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)

           if cond_proj_dim is not None:
                 SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__		,bias=lowerCamelCase__			)
           else:
                 SCREAMING_SNAKE_CASE						=		None

           SCREAMING_SNAKE_CASE						=		get_activation(lowerCamelCase__			)

           if out_dim is not None:
                 SCREAMING_SNAKE_CASE						=		out_dim
           else:
                 SCREAMING_SNAKE_CASE						=		time_embed_dim
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)

           if post_act_fn is None:
                 SCREAMING_SNAKE_CASE						=		None
           else:
                 SCREAMING_SNAKE_CASE						=		get_activation(lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       int=None			)	->						str:




           '''simple docstring'''


           if condition is not None:
                 SCREAMING_SNAKE_CASE						=		sample + self.cond_proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.linear_a(lowerCamelCase__			)

           if self.act is not None:
                 SCREAMING_SNAKE_CASE						=		self.act(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		self.linear_a(lowerCamelCase__			)

           if self.post_act is not None:
                 SCREAMING_SNAKE_CASE						=		self.post_act(lowerCamelCase__			)
           return sample

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		flip_sin_to_cos
           SCREAMING_SNAKE_CASE						=		downscale_freq_shift






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]		,lowerCamelCase__		:       Optional[Any]			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		get_timestep_embedding(
               lowerCamelCase__		,self.num_channels		,flip_sin_to_cos=self.flip_sin_to_cos		,downscale_freq_shift=self.downscale_freq_shift		,)
           return t_emb

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		nn.Parameter(torch.randn(lowerCamelCase__			) * scale		,requires_grad=lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		log
           SCREAMING_SNAKE_CASE						=		flip_sin_to_cos

           if set_W_to_weight:
                 # to delete later
                 SCREAMING_SNAKE_CASE						=		nn.Parameter(torch.randn(lowerCamelCase__			) * scale		,requires_grad=lowerCamelCase__			)

                 SCREAMING_SNAKE_CASE						=		self.W






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       Tuple			)	->						Optional[int]:




           '''simple docstring'''


           if self.log:
                 SCREAMING_SNAKE_CASE						=		torch.log(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		x[:, None] * self.weight[None, :] * 2 * np.pi

           if self.flip_sin_to_cos:
                 SCREAMING_SNAKE_CASE						=		torch.cat([torch.cos(lowerCamelCase__			), torch.sin(lowerCamelCase__			)]		,dim=-1			)
           else:
                 SCREAMING_SNAKE_CASE						=		torch.cat([torch.sin(lowerCamelCase__			), torch.cos(lowerCamelCase__			)]		,dim=-1			)
           return out

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       List[str]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       int		,lowerCamelCase__		:       int		,lowerCamelCase__		:       int		,)	->						List[Any]:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		height
           SCREAMING_SNAKE_CASE						=		width
           SCREAMING_SNAKE_CASE						=		num_embed
           SCREAMING_SNAKE_CASE						=		embed_dim

           SCREAMING_SNAKE_CASE						=		nn.Embedding(self.num_embed		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.Embedding(self.height		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.Embedding(self.width		,lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int		,lowerCamelCase__		:       str			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.emb(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		self.height_emb(torch.arange(self.height		,device=index.device			).view(1		,self.height			)			)

           # 1 x H x D -> 1 x H x 1 x D
           SCREAMING_SNAKE_CASE						=		height_emb.unsqueeze(2			)

           SCREAMING_SNAKE_CASE						=		self.width_emb(torch.arange(self.width		,device=index.device			).view(1		,self.width			)			)

           # 1 x W x D -> 1 x 1 x W x D
           SCREAMING_SNAKE_CASE						=		width_emb.unsqueeze(1			)

           SCREAMING_SNAKE_CASE						=		height_emb + width_emb

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

           SCREAMING_SNAKE_CASE						=		emb + pos_emb[:, : emb.shape[1], :]

           return emb

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       str		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Any			)	->						Tuple:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		dropout_prob > 0
           SCREAMING_SNAKE_CASE						=		nn.Embedding(num_classes + use_cfg_embedding		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		num_classes
           SCREAMING_SNAKE_CASE						=		dropout_prob






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       int=None			)	->						List[Any]:




           '''simple docstring'''


           if force_drop_ids is None:
                 SCREAMING_SNAKE_CASE						=		torch.rand(labels.shape[0]		,device=labels.device			) < self.dropout_prob
           else:
                 SCREAMING_SNAKE_CASE						=		torch.tensor(force_drop_ids == 1			)
           SCREAMING_SNAKE_CASE						=		torch.where(lowerCamelCase__		,self.num_classes		,lowerCamelCase__			)
           return labels






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int		,lowerCamelCase__		:       torch.LongTensor		,lowerCamelCase__		:       List[Any]=None			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.dropout_prob > 0
           if (self.training and use_dropout) or (force_drop_ids is not None):
                 SCREAMING_SNAKE_CASE						=		self.token_drop(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.embedding_table(lowerCamelCase__			)
           return embeddings

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       str		,lowerCamelCase__		:       int = 1024		,lowerCamelCase__		:       int = 768		,lowerCamelCase__		:       int = 768		,lowerCamelCase__		:       int = 10		,)	->						str:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		num_image_text_embeds
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,self.num_image_text_embeds * cross_attention_dim			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       torch.FloatTensor		,lowerCamelCase__		:       torch.FloatTensor			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		text_embeds.shape[0]

           # image
           SCREAMING_SNAKE_CASE						=		self.image_embeds(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		image_text_embeds.reshape(lowerCamelCase__		,self.num_image_text_embeds		,-1			)

           # text
           SCREAMING_SNAKE_CASE						=		self.text_proj(lowerCamelCase__			)

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

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       str		,lowerCamelCase__		:       int = 768		,lowerCamelCase__		:       int = 768		,lowerCamelCase__		:       int = 32		,)	->						Any:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		num_image_text_embeds
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,self.num_image_text_embeds * cross_attention_dim			)
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       torch.FloatTensor			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		image_embeds.shape[0]

           # image
           SCREAMING_SNAKE_CASE						=		self.image_embeds(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		image_embeds.reshape(lowerCamelCase__		,self.num_image_text_embeds		,-1			)
           SCREAMING_SNAKE_CASE						=		self.norm(lowerCamelCase__			)
           return image_embeds

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       List[str]=0.1			)	->						int:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		Timesteps(num_channels=256		,flip_sin_to_cos=lowerCamelCase__		,downscale_freq_shift=1			)
           SCREAMING_SNAKE_CASE						=		TimestepEmbedding(in_channels=256		,time_embed_dim=lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		LabelEmbedding(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       int=None			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.time_proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase__			)			)  # (N, D)

           SCREAMING_SNAKE_CASE						=		self.class_embedder(lowerCamelCase__			)  # (N, D)

           SCREAMING_SNAKE_CASE						=		timesteps_emb + class_labels  # (N, D)

           return conditioning

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       List[Any]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       int		,lowerCamelCase__		:       int = 64			)	->						int:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		AttentionPooling(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]		,lowerCamelCase__		:       Optional[Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.norma(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.pool(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.norma(lowerCamelCase__			)
           return hidden_states

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       torch.FloatTensor		,lowerCamelCase__		:       torch.FloatTensor			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.text_proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.text_norm(lowerCamelCase__			)

           # image
           SCREAMING_SNAKE_CASE						=		self.image_proj(lowerCamelCase__			)

           return time_image_embeds + time_text_embeds

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]		,lowerCamelCase__		:       torch.FloatTensor			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.image_norm(lowerCamelCase__			)
           return time_image_embeds

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       Dict		,lowerCamelCase__		:       int = 768		,lowerCamelCase__		:       int = 1536			)	->						List[str]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.LayerNorm(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		nn.Sequential(
               nn.Convad(3		,16		,3		,padding=1			)		,nn.SiLU()		,nn.Convad(16		,16		,3		,padding=1			)		,nn.SiLU()		,nn.Convad(16		,32		,3		,padding=1		,stride=2			)		,nn.SiLU()		,nn.Convad(32		,32		,3		,padding=1			)		,nn.SiLU()		,nn.Convad(32		,96		,3		,padding=1		,stride=2			)		,nn.SiLU()		,nn.Convad(96		,96		,3		,padding=1			)		,nn.SiLU()		,nn.Convad(96		,256		,3		,padding=1		,stride=2			)		,nn.SiLU()		,nn.Convad(256		,4		,3		,padding=1			)		,)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]		,lowerCamelCase__		:       torch.FloatTensor		,lowerCamelCase__		:       torch.FloatTensor			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_proj(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.image_norm(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.input_hint_block(lowerCamelCase__			)
           return time_image_embeds, hint

class 						UpperCamelCase__					( nn.Module  ):



     '''simple docstring'''

     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Union[str, Any]=None			)	->						Dict:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE						=		dtype
           SCREAMING_SNAKE_CASE						=		nn.Parameter(torch.randn(1		,lowerCamelCase__			) / embed_dim**0.5			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__		,dtype=self.dtype			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__		,dtype=self.dtype			)
           SCREAMING_SNAKE_CASE						=		nn.Linear(lowerCamelCase__		,lowerCamelCase__		,dtype=self.dtype			)
           SCREAMING_SNAKE_CASE						=		num_heads
           SCREAMING_SNAKE_CASE						=		embed_dim // self.num_heads






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       Union[str, Any]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		x.size()

           def shape(lowerCamelCase__		:       str			):
                 # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                 SCREAMING_SNAKE_CASE						=		x.view(lowerCamelCase__		,-1		,self.num_heads		,self.dim_per_head			)
                 # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
                 SCREAMING_SNAKE_CASE						=		x.transpose(1		,2			)
                 # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                 SCREAMING_SNAKE_CASE						=		x.reshape(bs * self.num_heads		,-1		,self.dim_per_head			)
                 # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
                 SCREAMING_SNAKE_CASE						=		x.transpose(1		,2			)
                 return x

           SCREAMING_SNAKE_CASE						=		x.mean(dim=1		,keepdim=lowerCamelCase__			) + self.positional_embedding.to(x.dtype			)
           SCREAMING_SNAKE_CASE						=		torch.cat([class_token, x]		,dim=1			)  # (bs, length+1, width)

           # (bs*n_heads, class_token_length, dim_per_head)
           SCREAMING_SNAKE_CASE						=		shape(self.q_proj(lowerCamelCase__			)			)
           # (bs*n_heads, length+class_token_length, dim_per_head)
           SCREAMING_SNAKE_CASE						=		shape(self.k_proj(lowerCamelCase__			)			)
           SCREAMING_SNAKE_CASE						=		shape(self.v_proj(lowerCamelCase__			)			)

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

           # (bs*n_heads, dim_per_head, class_token_length)
           SCREAMING_SNAKE_CASE						=		torch.einsum("""bts,bcs->bct"""		,lowerCamelCase__		,lowerCamelCase__			)

           # (bs, length+1, width)
           SCREAMING_SNAKE_CASE						=		a.reshape(lowerCamelCase__		,-1		,1			).transpose(1		,2			)

           return a[:, 0, :]  # cls_token









import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      float
     __snake_case       :      Optional[float]						      =     None
     __snake_case       :      Optional[float]						      =     None

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(x.a		,30			)
           self.assertIsNone(x.b			)
           self.assertIsNone(x.c			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.d






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""b"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""c"""]

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""c"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""b"""]






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(list(x.values()			)		,[30]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """b"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """c"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""c""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """c"""]			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.update({"""d""": 20}			)
           with self.assertRaises(lowerCamelCase__			):
                 del x["a"]
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.pop("""a"""			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.setdefault("""d"""		,32			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest({"""a""": 30, """b""": 10}			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), (10, 10)]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=(30, 30)			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(x.a		,(30, 30)			)


