



'''simple docstring'''







import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE = False ,		SCREAMING_SNAKE_CASE = 1 ,		SCREAMING_SNAKE_CASE = 1 ,		SCREAMING_SNAKE_CASE = 1_0000 ,		):
		assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

		SCREAMING_SNAKE_CASE_						:int										=      embedding_dim // 2
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      -math.log(lowerCAmelCase__     ) * torch.arange(
		    start=0 ,		end=lowerCAmelCase__ ,		dtype=torch.floataa ,		device=timesteps.device     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      exponent / (half_dim - downscale_freq_shift)

		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.exp(lowerCAmelCase__     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      timesteps[:, None].float() * emb[None, :]

		# scale embeddings
		SCREAMING_SNAKE_CASE_						:Any										=      scale * emb

		# concat sine and cosine embeddings
		SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.cat([torch.sin(lowerCAmelCase__     ), torch.cos(lowerCAmelCase__     )] ,		dim=-1     )

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

		# zero pad
		if embedding_dim % 2 == 1:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.nn.functional.pad(lowerCAmelCase__ ,		(0, 1, 0, 0)     )
		return emb







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE=False ,		SCREAMING_SNAKE_CASE=0     ):
		SCREAMING_SNAKE_CASE_						:Any										=      np.arange(lowerCAmelCase__ ,		dtype=np.floataa     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.arange(lowerCAmelCase__ ,		dtype=np.floataa     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.meshgrid(lowerCAmelCase__ ,		lowerCAmelCase__     )  # here w goes first
		SCREAMING_SNAKE_CASE_						:List[str]										=      np.stack(lowerCAmelCase__ ,		axis=0     )

		SCREAMING_SNAKE_CASE_						:Tuple										=      grid.reshape([2, 1, grid_size, grid_size]     )
		SCREAMING_SNAKE_CASE_						:List[Any]										=      get_ad_sincos_pos_embed_from_grid(lowerCAmelCase__ ,		lowerCAmelCase__     )
		if cls_token and extra_tokens > 0:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed] ,		axis=0     )
		return pos_embed







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		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_						:Optional[int]										=      get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,		grid[0]     )  # (H*W, D/2)
		SCREAMING_SNAKE_CASE_						:List[Any]										=      get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,		grid[1]     )  # (H*W, D/2)

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







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

		SCREAMING_SNAKE_CASE_						:Tuple										=      np.arange(embed_dim // 2 ,		dtype=np.floataa     )
		omega /= embed_dim / 2.0
		SCREAMING_SNAKE_CASE_						:Tuple										=      1.0 / 1_0000**omega  # (D/2,)

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

		SCREAMING_SNAKE_CASE_						:int										=      np.sin(lowerCAmelCase__     )  # (M, D/2)
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.cos(lowerCAmelCase__     )  # (M, D/2)

		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.concatenate([emb_sin, emb_cos] ,		axis=1     )  # (M, D)
		return emb




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Any=224      ,       SCREAMING_SNAKE_CASE  :							Any=224      ,       SCREAMING_SNAKE_CASE  :							int=16      ,       SCREAMING_SNAKE_CASE  :							List[Any]=3      ,       SCREAMING_SNAKE_CASE  :							Dict=768      ,       SCREAMING_SNAKE_CASE  :							Tuple=False      ,       SCREAMING_SNAKE_CASE  :							Any=True      ,       SCREAMING_SNAKE_CASE  :							List[str]=True      ,       ):




					"""simple docstring"""







					super().__init__()

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

					SCREAMING_SNAKE_CASE_						:Tuple										=      nn.Convad(
					    lowercase__      ,       lowercase__      ,       kernel_size=(patch_size, patch_size)      ,       stride=lowercase__      ,       bias=lowercase__	)
					if layer_norm:
							SCREAMING_SNAKE_CASE_						:Dict										=      nn.LayerNorm(lowercase__      ,       elementwise_affine=lowercase__      ,       eps=1E-6	)
					else:
							SCREAMING_SNAKE_CASE_						:Dict										=      None

					SCREAMING_SNAKE_CASE_						:str										=      get_ad_sincos_pos_embed(lowercase__      ,       int(num_patches**0.5	)	)
					self.register_buffer('pos_embed'      ,       torch.from_numpy(lowercase__	).float().unsqueeze(0	)      ,       persistent=lowercase__	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.proj(lowercase__	)
					if self.flatten:
							SCREAMING_SNAKE_CASE_						:Tuple										=      latent.flatten(2	).transpose(1      ,       2	)  # BCHW -> BNC
					if self.layer_norm:
							SCREAMING_SNAKE_CASE_						:Tuple										=      self.norm(lowercase__	)
					return latent + self.pos_embed




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str = "silu"      ,       SCREAMING_SNAKE_CASE  :							int = None      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None      ,       SCREAMING_SNAKE_CASE  :							List[str]=None      ,       ):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:Any										=      nn.Linear(lowercase__      ,       lowercase__	)

					if cond_proj_dim is not None:
							SCREAMING_SNAKE_CASE_						:List[str]										=      nn.Linear(lowercase__      ,       lowercase__      ,       bias=lowercase__	)
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      None

					SCREAMING_SNAKE_CASE_						:Dict										=      get_activation(lowercase__	)

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

					if post_act_fn is None:
							SCREAMING_SNAKE_CASE_						:Tuple										=      None
					else:
							SCREAMING_SNAKE_CASE_						:str										=      get_activation(lowercase__	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]=None	):




					"""simple docstring"""







					if condition is not None:
							SCREAMING_SNAKE_CASE_						:Dict										=      sample + self.cond_proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:int										=      self.linear_a(lowercase__	)

					if self.act is not None:
							SCREAMING_SNAKE_CASE_						:Dict										=      self.act(lowercase__	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.linear_a(lowercase__	)

					if self.post_act is not None:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.post_act(lowercase__	)
					return sample




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							bool      ,       SCREAMING_SNAKE_CASE  :							float	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      num_channels
					SCREAMING_SNAKE_CASE_						:List[str]										=      flip_sin_to_cos
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      downscale_freq_shift






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_timestep_embedding(
					    lowercase__      ,       self.num_channels      ,       flip_sin_to_cos=self.flip_sin_to_cos      ,       downscale_freq_shift=self.downscale_freq_shift      ,       )
					return t_emb




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int = 256      ,       SCREAMING_SNAKE_CASE  :							float = 1.0      ,       SCREAMING_SNAKE_CASE  :							str=True      ,       SCREAMING_SNAKE_CASE  :							List[str]=True      ,       SCREAMING_SNAKE_CASE  :							Any=False	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      nn.Parameter(torch.randn(lowercase__	) * scale      ,       requires_grad=lowercase__	)
					SCREAMING_SNAKE_CASE_						:Any										=      log
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      flip_sin_to_cos

					if set_W_to_weight:
							# to delete later
							SCREAMING_SNAKE_CASE_						:Dict										=      nn.Parameter(torch.randn(lowercase__	) * scale      ,       requires_grad=lowercase__	)

							SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.W






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					if self.log:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.log(lowercase__	)

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

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




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       ):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:str										=      height
					SCREAMING_SNAKE_CASE_						:List[str]										=      width
					SCREAMING_SNAKE_CASE_						:str										=      num_embed
					SCREAMING_SNAKE_CASE_						:List[str]										=      embed_dim

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      nn.Embedding(self.num_embed      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      nn.Embedding(self.height      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      nn.Embedding(self.width      ,       lowercase__	)






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.emb(lowercase__	)

					SCREAMING_SNAKE_CASE_						:str										=      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_						:int										=      height_emb.unsqueeze(2	)

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

					SCREAMING_SNAKE_CASE_						:Any										=      height_emb + width_emb

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

					SCREAMING_SNAKE_CASE_						:Tuple										=      emb + pos_emb[:, : emb.shape[1], :]

					return emb




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:str										=      dropout_prob > 0
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.Embedding(num_classes + use_cfg_embedding      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      num_classes
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      dropout_prob






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]=None	):




					"""simple docstring"""







					if force_drop_ids is None:
							SCREAMING_SNAKE_CASE_						:str										=      torch.rand(labels.shape[0]      ,       device=labels.device	) < self.dropout_prob
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      torch.tensor(force_drop_ids == 1	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.where(lowercase__      ,       self.num_classes      ,       lowercase__	)
					return labels






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							torch.LongTensor      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.token_drop(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Dict										=      self.embedding_table(lowercase__	)
					return embeddings




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int = 1_024      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 10      ,       ):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:Any										=      num_image_text_embeds
					SCREAMING_SNAKE_CASE_						:str										=      nn.Linear(lowercase__      ,       self.num_image_text_embeds * cross_attention_dim	)
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.Linear(lowercase__      ,       lowercase__	)






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      text_embeds.shape[0]

					# image
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.image_embeds(lowercase__	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      image_text_embeds.reshape(lowercase__      ,       self.num_image_text_embeds      ,       -1	)

					# text
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.text_proj(lowercase__	)

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




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 32      ,       ):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:str										=      num_image_text_embeds
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.Linear(lowercase__      ,       self.num_image_text_embeds * cross_attention_dim	)
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.LayerNorm(lowercase__	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      image_embeds.shape[0]

					# image
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.image_embeds(lowercase__	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      image_embeds.reshape(lowercase__      ,       self.num_image_text_embeds      ,       -1	)
					SCREAMING_SNAKE_CASE_						:Any										=      self.norm(lowercase__	)
					return image_embeds




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple=0.1	):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:int										=      Timesteps(num_channels=256      ,       flip_sin_to_cos=lowercase__      ,       downscale_freq_shift=1	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      TimestepEmbedding(in_channels=256      ,       time_embed_dim=lowercase__	)
					SCREAMING_SNAKE_CASE_						:int										=      LabelEmbedding(lowercase__      ,       lowercase__      ,       lowercase__	)






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Dict=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.time_proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:int										=      self.timestep_embedder(timesteps_proj.to(dtype=lowercase__	)	)  # (N, D)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.class_embedder(lowercase__	)  # (N, D)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      timesteps_emb + class_labels  # (N, D)

					return conditioning




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int = 64	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:int										=      nn.LayerNorm(lowercase__	)
					SCREAMING_SNAKE_CASE_						:Dict										=      AttentionPooling(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.Linear(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:int										=      nn.LayerNorm(lowercase__	)






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.norma(lowercase__	)
					SCREAMING_SNAKE_CASE_						:int										=      self.pool(lowercase__	)
					SCREAMING_SNAKE_CASE_						:str										=      self.proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:Any										=      self.norma(lowercase__	)
					return hidden_states




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 1_536	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:str										=      nn.Linear(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      nn.LayerNorm(lowercase__	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.Linear(lowercase__      ,       lowercase__	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.text_proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.text_norm(lowercase__	)

					# image
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.image_proj(lowercase__	)

					return time_image_embeds + time_text_embeds




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 1_536	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.Linear(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.LayerNorm(lowercase__	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.image_proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:Any										=      self.image_norm(lowercase__	)
					return time_image_embeds




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							int = 768      ,       SCREAMING_SNAKE_CASE  :							int = 1_536	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:str										=      nn.Linear(lowercase__      ,       lowercase__	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      nn.LayerNorm(lowercase__	)
					SCREAMING_SNAKE_CASE_						: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 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.image_proj(lowercase__	)
					SCREAMING_SNAKE_CASE_						:str										=      self.image_norm(lowercase__	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      self.input_hint_block(lowercase__	)
					return time_image_embeds, hint




class 				__lowerCAmelCase(							nn.Module      ):






			def __init__(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=None	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      x.size()

					def shape(SCREAMING_SNAKE_CASE  :							Dict	):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							SCREAMING_SNAKE_CASE_						:List[Any]										=      x.view(lowercase__      ,       -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_						:List[Any]										=      x.transpose(1      ,       2	)
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							SCREAMING_SNAKE_CASE_						:str										=      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_						:Tuple										=      x.transpose(1      ,       2	)
							return x

					SCREAMING_SNAKE_CASE_						:str										=      x.mean(dim=1      ,       keepdim=lowercase__	) + self.positional_embedding.to(x.dtype	)
					SCREAMING_SNAKE_CASE_						:Any										=      torch.cat([class_token, x]      ,       dim=1	)  # (bs, length+1, width)

					# (bs*n_heads, class_token_length, dim_per_head)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      shape(self.q_proj(lowercase__	)	)
					# (bs*n_heads, length+class_token_length, dim_per_head)
					SCREAMING_SNAKE_CASE_						:Tuple										=      shape(self.k_proj(lowercase__	)	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      shape(self.v_proj(lowercase__	)	)

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

					# (bs*n_heads, dim_per_head, class_token_length)
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.einsum('bts,bcs->bct'      ,       lowercase__      ,       lowercase__	)

					# (bs, length+1, width)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      a.reshape(lowercase__      ,       -1      ,       1	).transpose(1      ,       2	)

					return a[:, 0, :]  # cls_token

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = False      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1_0_0_0_0      ,    ):
       assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

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

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

       # scale embeddings
       lowerCAmelCase__	      =   scale * emb

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

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

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






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__=False      ,    lowerCAmelCase__=0      ):
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.meshgrid(lowerCAmelCase__      ,    lowerCAmelCase__      )  # here w goes first
       lowerCAmelCase__	      =   np.stack(lowerCAmelCase__      ,    axis=0      )

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






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

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

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






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

       lowerCAmelCase__	      =   np.arange(embed_dim // 2      ,    dtype=np.floataa      )
       omega /= embed_dim / 2.0
       lowerCAmelCase__	      =   1.0 / 1_0_0_0_0**omega  # (D/2,)

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

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

       lowerCAmelCase__	      =   np.concatenate([emb_sin, emb_cos]      ,    axis=1      )  # (M, D)
       return emb
class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    Any=224     ,       lowercase__   :    Any=224     ,       lowercase__   :    int=16     ,       lowercase__   :    List[Any]=3     ,       lowercase__   :    Dict=768     ,       lowercase__   :    Tuple=False     ,       lowercase__   :    Any=True     ,       lowercase__   :    List[str]=True     ,       ):







        '''simple docstring'''



        super().__init__()

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

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

        lowerCAmelCase__	      =   get_ad_sincos_pos_embed(lowercase__     ,       int(num_patches**0.5))
        self.register_buffer('pos_embed'     ,       torch.from_numpy(lowercase__).float().unsqueeze(0)     ,       persistent=lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



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






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    str = "silu"     ,       lowercase__   :    int = None     ,       lowercase__   :    Optional[str] = None     ,       lowercase__   :    List[str]=None     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if cond_proj_dim is not None:
               lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       bias=lowercase__)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_activation(lowercase__)

        if out_dim is not None:
               lowerCAmelCase__	      =   out_dim
        else:
               lowerCAmelCase__	      =   time_embed_dim
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if post_act_fn is None:
               lowerCAmelCase__	      =   None
        else:
               lowerCAmelCase__	      =   get_activation(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if condition is not None:
               lowerCAmelCase__	      =   sample + self.cond_proj(lowercase__)
        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.act is not None:
               lowerCAmelCase__	      =   self.act(lowercase__)

        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.post_act is not None:
               lowerCAmelCase__	      =   self.post_act(lowercase__)
        return sample






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    bool     ,       lowercase__   :    float):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   get_timestep_embedding(
            lowercase__     ,       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   :    List[str]     ,       lowercase__   :    int = 256     ,       lowercase__   :    float = 1.0     ,       lowercase__   :    str=True     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any=False):







        '''simple docstring'''



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

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

               lowerCAmelCase__	      =   self.W




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        if self.log:
               lowerCAmelCase__	      =   torch.log(lowercase__)

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

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






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   height
        lowerCAmelCase__	      =   width
        lowerCAmelCase__	      =   num_embed
        lowerCAmelCase__	      =   embed_dim

        lowerCAmelCase__	      =   nn.Embedding(self.num_embed     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.height     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.width     ,       lowercase__)




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.emb(lowercase__)

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

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

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

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

        lowerCAmelCase__	      =   height_emb + width_emb

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

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

        return emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Any     ,       lowercase__   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dropout_prob > 0
        lowerCAmelCase__	      =   nn.Embedding(num_classes + use_cfg_embedding     ,       lowercase__)
        lowerCAmelCase__	      =   num_classes
        lowerCAmelCase__	      =   dropout_prob




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    List[Any]     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.LongTensor     ,       lowercase__   :    str=None):







        '''simple docstring'''



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






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int = 1_024     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 10     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   text_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_text_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)

        # text
        lowerCAmelCase__	      =   self.text_proj(lowercase__)

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






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 32     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   image_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)
        lowerCAmelCase__	      =   self.norm(lowercase__)
        return image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Dict     ,       lowercase__   :    Tuple=0.1):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   Timesteps(num_channels=256     ,       flip_sin_to_cos=lowercase__     ,       downscale_freq_shift=1)
        lowerCAmelCase__	      =   TimestepEmbedding(in_channels=256     ,       time_embed_dim=lowercase__)
        lowerCAmelCase__	      =   LabelEmbedding(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.time_proj(lowercase__)
        lowerCAmelCase__	      =   self.timestep_embedder(timesteps_proj.to(dtype=lowercase__))  # (N, D)

        lowerCAmelCase__	      =   self.class_embedder(lowercase__)  # (N, D)

        lowerCAmelCase__	      =   timesteps_emb + class_labels  # (N, D)

        return conditioning






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int = 64):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   AttentionPooling(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.norma(lowercase__)
        lowerCAmelCase__	      =   self.pool(lowercase__)
        lowerCAmelCase__	      =   self.proj(lowercase__)
        lowerCAmelCase__	      =   self.norma(lowercase__)
        return hidden_states






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.text_proj(lowercase__)
        lowerCAmelCase__	      =   self.text_norm(lowercase__)

        # image
        lowerCAmelCase__	      =   self.image_proj(lowercase__)

        return time_image_embeds + time_text_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        return time_image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        lowerCAmelCase__	      =   self.input_hint_block(lowercase__)
        return time_image_embeds, hint






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[int]=None):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   x.size()

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

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

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

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

        # (bs*n_heads, dim_per_head, class_token_length)
        lowerCAmelCase__	      =   torch.einsum('bts,bcs->bct'     ,       lowercase__     ,       lowercase__)

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

        return a[:, 0, :]  # cls_token
