code: 
  # A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
  # U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
  # from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
  # or u belongs to V and v to U. We can also say that there is no edge that connects
  # vertices of same set.
  from queue import Queue
  
  
  def       a			(   a  )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   Queue()
  			SCREAMING_SNAKE_CASE    	=   [False] * len(a  )
  			SCREAMING_SNAKE_CASE    	=   [-1] * len(a  )
  
  			def bfs():
  						while not queue.empty():
  									SCREAMING_SNAKE_CASE    	=   queue.get()
  									SCREAMING_SNAKE_CASE    	=   True
  
  									for neighbour in graph[u]:
  												if neighbour == u:
  															return False
  
  												if color[neighbour] == -1:
  															SCREAMING_SNAKE_CASE    	=   1 - color[u]
  															queue.put(a  )
  
  												elif color[neighbour] == color[u]:
  															return False
  
  						return True
  
  			for i in range(len(a  )  ):
  						if not visited[i]:
  									queue.put(a  )
  									SCREAMING_SNAKE_CASE    	=   0
  									if bfs() is False:
  												return False
  
  			return True
  
  
  if __name__ == "__main__":
  							# Adjacency List of graph
  							print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))
style_context: 
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  
  
  def       a			(   a		,	a		,	a = False		,	a = 1		,	a = 1		,	a = 1_0000		,	)    ->Dict:
  
  
  
  
  			'''simple docstring'''
  
  
  			assert len(timesteps.shape  ) == 1, "Timesteps should be a 1d-array"
  
  			SCREAMING_SNAKE_CASE    	=   embedding_dim // 2
  			SCREAMING_SNAKE_CASE    	=   -math.log(a  ) * torch.arange(
  			    start=0		,	end=a		,	dtype=torch.floataa		,	device=timesteps.device  )
  			SCREAMING_SNAKE_CASE    	=   exponent / (half_dim - downscale_freq_shift)
  
  			SCREAMING_SNAKE_CASE    	=   torch.exp(a  )
  			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(a  ), torch.cos(a  )]		,	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(a		,	(0, 1, 0, 0)  )
  			return emb
  
  
  def       a			(   a		,	a		,	a=False		,	a=0  )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   np.arange(a		,	dtype=np.floataa  )
  			SCREAMING_SNAKE_CASE    	=   np.arange(a		,	dtype=np.floataa  )
  			SCREAMING_SNAKE_CASE    	=   np.meshgrid(a		,	a  )  # here w goes first
  			SCREAMING_SNAKE_CASE    	=   np.stack(a		,	axis=0  )
  
  			SCREAMING_SNAKE_CASE    	=   grid.reshape([2, 1, grid_size, grid_size]  )
  			SCREAMING_SNAKE_CASE    	=   get_ad_sincos_pos_embed_from_grid(a		,	a  )
  			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       a			(   a		,	a  )    ->Optional[int]:
  
  
  
  
  			'''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       a			(   a		,	a  )    ->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_0000**omega  # (D/2,)
  
  			SCREAMING_SNAKE_CASE    	=   pos.reshape(-1  )  # (M,)
  			SCREAMING_SNAKE_CASE    	=   np.einsum('''m,d->md'''		,	a		,	a  )  # (M, D/2), outer product
  
  			SCREAMING_SNAKE_CASE    	=   np.sin(a  )  # (M, D/2)
  			SCREAMING_SNAKE_CASE    	=   np.cos(a  )  # (M, D/2)
  
  			SCREAMING_SNAKE_CASE    	=   np.concatenate([emb_sin, emb_cos]		,	axis=1  )  # (M, D)
  			return emb
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:int=2_2_4      ,    lowercase			:Dict=2_2_4      ,    lowercase			:List[Any]=1_6      ,    lowercase			:str=3      ,    lowercase			:List[str]=7_6_8      ,    lowercase			:Tuple=False      ,    lowercase			:Optional[int]=True      ,    lowercase			:str=True      ,    )		->			int:
  
  
  
  
  
  
  
  				"""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(
  				    lowercase      ,    lowercase      ,    kernel_size=(patch_size, patch_size)      ,    stride=lowercase      ,    bias=lowercase )
  				if layer_norm:
  							SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase      ,    elementwise_affine=lowercase      ,    eps=1e-6 )
  				else:
  							SCREAMING_SNAKE_CASE    	=   None
  
  				SCREAMING_SNAKE_CASE    	=   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			:Optional[int]      ,    lowercase			:str )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.proj(lowercase )
  				if self.flatten:
  							SCREAMING_SNAKE_CASE    	=   latent.flatten(2 ).transpose(1      ,    2 )  # BCHW -> BNC
  				if self.layer_norm:
  							SCREAMING_SNAKE_CASE    	=   self.norm(lowercase )
  				return latent + self.pos_embed
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:str = "silu"      ,    lowercase			:int = None      ,    lowercase			:Optional[str] = None      ,    lowercase			:int=None      ,    )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  
  				if cond_proj_dim is not None:
  							SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    bias=lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   None
  
  				SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )
  
  				if out_dim is not None:
  							SCREAMING_SNAKE_CASE    	=   out_dim
  				else:
  							SCREAMING_SNAKE_CASE    	=   time_embed_dim
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  
  				if post_act_fn is None:
  							SCREAMING_SNAKE_CASE    	=   None
  				else:
  							SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]=None )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if condition is not None:
  							SCREAMING_SNAKE_CASE    	=   sample + self.cond_proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )
  
  				if self.act is not None:
  							SCREAMING_SNAKE_CASE    	=   self.act(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )
  
  				if self.post_act is not None:
  							SCREAMING_SNAKE_CASE    	=   self.post_act(lowercase )
  				return sample
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:int      ,    lowercase			:int      ,    lowercase			:bool      ,    lowercase			:float )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   num_channels
  				SCREAMING_SNAKE_CASE    	=   flip_sin_to_cos
  				SCREAMING_SNAKE_CASE    	=   downscale_freq_shift
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:str )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   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]      ,    lowercase			:int = 2_5_6      ,    lowercase			:float = 1.0      ,    lowercase			:int=True      ,    lowercase			:Any=True      ,    lowercase			:List[Any]=False )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   nn.Parameter(torch.randn(lowercase ) * scale      ,    requires_grad=lowercase )
  				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(lowercase ) * scale      ,    requires_grad=lowercase )
  
  							SCREAMING_SNAKE_CASE    	=   self.W
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Optional[Any] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if self.log:
  							SCREAMING_SNAKE_CASE    	=   torch.log(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   x[:, None] * self.weight[None, :] * 2 * np.pi
  
  				if self.flip_sin_to_cos:
  							SCREAMING_SNAKE_CASE    	=   torch.cat([torch.cos(lowercase ), torch.sin(lowercase )]      ,    dim=-1 )
  				else:
  							SCREAMING_SNAKE_CASE    	=   torch.cat([torch.sin(lowercase ), torch.cos(lowercase )]      ,    dim=-1 )
  				return out
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    )		->			List[str]:
  
  
  
  
  
  
  
  				"""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      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.height      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.width      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Tuple )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.emb(lowercase )
  
  				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 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:int      ,    lowercase			:Union[str, Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   dropout_prob > 0
  				SCREAMING_SNAKE_CASE    	=   nn.Embedding(num_classes + use_cfg_embedding      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   num_classes
  				SCREAMING_SNAKE_CASE    	=   dropout_prob
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Tuple      ,    lowercase			:List[str]=None )		->			Dict:
  
  
  
  
  
  
  
  				"""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(lowercase      ,    self.num_classes      ,    lowercase )
  				return labels
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:torch.LongTensor      ,    lowercase			:Tuple=None )		->			Dict:
  
  
  
  
  
  
  
  				"""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(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.embedding_table(lowercase )
  				return embeddings
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:int = 1_0_2_4      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_0      ,    )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   text_embeds.shape[0]
  
  				# image
  				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
  				SCREAMING_SNAKE_CASE    	=   image_text_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )
  
  				# text
  				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )
  
  				return torch.cat([image_text_embeds, text_embeds]      ,    dim=1 )
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 3_2      ,    )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:torch.FloatTensor )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   image_embeds.shape[0]
  
  				# image
  				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
  				SCREAMING_SNAKE_CASE    	=   image_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )
  				SCREAMING_SNAKE_CASE    	=   self.norm(lowercase )
  				return image_embeds
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:int      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0.1 )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE    	=   Timesteps(num_channels=2_5_6      ,    flip_sin_to_cos=lowercase      ,    downscale_freq_shift=1 )
  				SCREAMING_SNAKE_CASE    	=   TimestepEmbedding(in_channels=2_5_6      ,    time_embed_dim=lowercase )
  				SCREAMING_SNAKE_CASE    	=   LabelEmbedding(lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int]      ,    lowercase			:int      ,    lowercase			:Optional[int]=None )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.time_proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.timestep_embedder(timesteps_proj.to(dtype=lowercase ) )  # (N, D)
  
  				SCREAMING_SNAKE_CASE    	=   self.class_embedder(lowercase )  # (N, D)
  
  				SCREAMING_SNAKE_CASE    	=   timesteps_emb + class_labels  # (N, D)
  
  				return conditioning
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int = 6_4 )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  				SCREAMING_SNAKE_CASE    	=   AttentionPooling(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.pool(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
  				return hidden_states
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:Tuple      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.text_norm(lowercase )
  
  				# image
  				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
  
  				return time_image_embeds + time_text_embeds
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:int      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
  				return time_image_embeds
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
  				SCREAMING_SNAKE_CASE    	=   nn.Sequential(
  				    nn.Convad(3      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    3_2      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    3_2      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    9_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    9_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    2_5_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(2_5_6      ,    4      ,    3      ,    padding=1 )      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.input_hint_block(lowercase )
  				return time_image_embeds, hint
  
  
  
  
  
  class 						lowerCamelCase			(					nn.Module				):
  
  
  
  
  
  
  
  	def __init__(    self			:List[Any]      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:int=None )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE    	=   dtype
  				SCREAMING_SNAKE_CASE    	=   nn.Parameter(torch.randn(1      ,    lowercase ) / embed_dim**0.5 )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    dtype=self.dtype )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    dtype=self.dtype )
  				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    dtype=self.dtype )
  				SCREAMING_SNAKE_CASE    	=   num_heads
  				SCREAMING_SNAKE_CASE    	=   embed_dim // self.num_heads
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   x.size()
  
  				def shape(lowercase			:List[Any] ):
  							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
  							SCREAMING_SNAKE_CASE    	=   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    	=   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=lowercase ) + 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(lowercase ) )
  				# (bs*n_heads, length+class_token_length, dim_per_head)
  				SCREAMING_SNAKE_CASE    	=   shape(self.k_proj(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   shape(self.v_proj(lowercase ) )
  
  				# (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'''      ,    lowercase      ,    lowercase )
  
  				# (bs, length+1, width)
  				SCREAMING_SNAKE_CASE    	=   a.reshape(lowercase      ,    -1      ,    1 ).transpose(1      ,    2 )
  
  				return a[:, 0, :]  # cls_token