code: 
  import math
  from typing import Optional
  
  import numpy as np
  import torch
  from torch import nn
  
  from .activations import get_activation
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						torch.Tensor   ,     _snake_case   :						int   ,     _snake_case   :						bool = False   ,     _snake_case   :						float = 1   ,     _snake_case   :						float = 1   ,     _snake_case   :						int = 10000   ,     )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"
  
  	__magic_name__	:	Union[str, Any]			     =  embedding_dim // 2
  	__magic_name__	:	Union[str, Any]			     =  -math.log(_snake_case     ) * torch.arange(
  	    start=0   ,     end=_snake_case   ,     dtype=torch.floataa   ,     device=timesteps.device     )
  	__magic_name__	:	Union[str, Any]			     =  exponent / (half_dim - downscale_freq_shift)
  
  	__magic_name__	:	List[Any]			     =  torch.exp(_snake_case     )
  	__magic_name__	:	Optional[int]			     =  timesteps[:, None].float() * emb[None, :]
  
  	# scale embeddings
  	__magic_name__	:	List[str]			     =  scale * emb
  
  	# concat sine and cosine embeddings
  	__magic_name__	:	Tuple			     =  torch.cat([torch.sin(_snake_case     ), torch.cos(_snake_case     )]   ,     dim=-1     )
  
  	# flip sine and cosine embeddings
  	if flip_sin_to_cos:
  		__magic_name__	:	str			     =  torch.cat([emb[:, half_dim:], emb[:, :half_dim]]   ,     dim=-1     )
  
  	# zero pad
  	if embedding_dim % 2 == 1:
  		__magic_name__	:	Union[str, Any]			     =  torch.nn.functional.pad(_snake_case   ,     (0, 1, 0, 0)     )
  	return emb
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						Optional[int]   ,     _snake_case   :						str=False   ,     _snake_case   :						str=0     )       ->	Union[str, Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Tuple			     =  np.arange(_snake_case   ,     dtype=np.floataa     )
  	__magic_name__	:	str			     =  np.arange(_snake_case   ,     dtype=np.floataa     )
  	__magic_name__	:	List[str]			     =  np.meshgrid(_snake_case   ,     _snake_case     )  # here w goes first
  	__magic_name__	:	Optional[int]			     =  np.stack(_snake_case   ,     axis=0     )
  
  	__magic_name__	:	Tuple			     =  grid.reshape([2, 1, grid_size, grid_size]     )
  	__magic_name__	:	Any			     =  get_ad_sincos_pos_embed_from_grid(_snake_case   ,     _snake_case     )
  	if cls_token and extra_tokens > 0:
  		__magic_name__	:	List[Any]			     =  np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]   ,     axis=0     )
  	return pos_embed
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						int     )       ->	Dict:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if embed_dim % 2 != 0:
  		raise ValueError("embed_dim must be divisible by 2"     )
  
  	# use half of dimensions to encode grid_h
  	__magic_name__	:	Dict			     =  get_ad_sincos_pos_embed_from_grid(embed_dim // 2   ,     grid[0]     )  # (H*W, D/2)
  	__magic_name__	:	Optional[int]			     =  get_ad_sincos_pos_embed_from_grid(embed_dim // 2   ,     grid[1]     )  # (H*W, D/2)
  
  	__magic_name__	:	int			     =  np.concatenate([emb_h, emb_w]   ,     axis=1     )  # (H*W, D)
  	return emb
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Dict   ,     _snake_case   :						Optional[Any]     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if embed_dim % 2 != 0:
  		raise ValueError("embed_dim must be divisible by 2"     )
  
  	__magic_name__	:	Optional[Any]			     =  np.arange(embed_dim // 2   ,     dtype=np.floataa     )
  	omega /= embed_dim / 2.0
  	__magic_name__	:	Dict			     =  1.0 / 10000**omega  # (D/2,)
  
  	__magic_name__	:	Optional[int]			     =  pos.reshape(-1     )  # (M,)
  	__magic_name__	:	int			     =  np.einsum("m,d->md"   ,     _snake_case   ,     _snake_case     )  # (M, D/2), outer product
  
  	__magic_name__	:	Any			     =  np.sin(_snake_case     )  # (M, D/2)
  	__magic_name__	:	Tuple			     =  np.cos(_snake_case     )  # (M, D/2)
  
  	__magic_name__	:	Optional[int]			     =  np.concatenate([emb_sin, emb_cos]   ,     axis=1     )  # (M, D)
  	return emb
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=224		,     _a=224		,     _a=16		,     _a=3		,     _a=768		,     _a=False		,     _a=True		,     _a=True		,     ):
  				super().__init__()
  
  				__magic_name__	:	Optional[Any]			     =  (height // patch_size) * (width // patch_size)
  				__magic_name__	:	List[str]			     =  flatten
  				__magic_name__	:	int			     =  layer_norm
  
  				__magic_name__	:	Optional[int]			     =  nn.Convad(
  				    _a		,     _a		,     kernel_size=(patch_size, patch_size)		,     stride=_a		,     bias=_a	)
  				if layer_norm:
  					__magic_name__	:	Tuple			     =  nn.LayerNorm(_a		,     elementwise_affine=_a		,     eps=1e-6	)
  				else:
  					__magic_name__	:	int			     =  None
  
  				__magic_name__	:	Any			     =  get_ad_sincos_pos_embed(_a		,     int(num_patches**0.5	)	)
  				self.register_buffer("pos_embed"		,     torch.from_numpy(_a	).float().unsqueeze(0	)		,     persistent=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	List[Any]			     =  self.proj(_a	)
  				if self.flatten:
  					__magic_name__	:	Tuple			     =  latent.flatten(2	).transpose(1		,     2	)  # BCHW -> BNC
  				if self.layer_norm:
  					__magic_name__	:	List[str]			     =  self.norm(_a	)
  				return latent + self.pos_embed
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a = "silu"		,     _a = None		,     _a = None		,     _a=None		,     ):
  				super().__init__()
  
  				__magic_name__	:	List[Any]			     =  nn.Linear(_a		,     _a	)
  
  				if cond_proj_dim is not None:
  					__magic_name__	:	Optional[Any]			     =  nn.Linear(_a		,     _a		,     bias=_a	)
  				else:
  					__magic_name__	:	Any			     =  None
  
  				__magic_name__	:	List[str]			     =  get_activation(_a	)
  
  				if out_dim is not None:
  					__magic_name__	:	Optional[int]			     =  out_dim
  				else:
  					__magic_name__	:	Dict			     =  time_embed_dim
  				__magic_name__	:	str			     =  nn.Linear(_a		,     _a	)
  
  				if post_act_fn is None:
  					__magic_name__	:	int			     =  None
  				else:
  					__magic_name__	:	Tuple			     =  get_activation(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=None	):
  				if condition is not None:
  					__magic_name__	:	int			     =  sample + self.cond_proj(_a	)
  				__magic_name__	:	Any			     =  self.linear_a(_a	)
  
  				if self.act is not None:
  					__magic_name__	:	List[str]			     =  self.act(_a	)
  
  				__magic_name__	:	str			     =  self.linear_a(_a	)
  
  				if self.post_act is not None:
  					__magic_name__	:	Union[str, Any]			     =  self.post_act(_a	)
  				return sample
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a	):
  				super().__init__()
  				__magic_name__	:	Dict			     =  num_channels
  				__magic_name__	:	Dict			     =  flip_sin_to_cos
  				__magic_name__	:	str			     =  downscale_freq_shift
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Dict			     =  get_timestep_embedding(
  				    _a		,     self.num_channels		,     flip_sin_to_cos=self.flip_sin_to_cos		,     downscale_freq_shift=self.downscale_freq_shift		,     )
  				return t_emb
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 256		,     _a = 1.0		,     _a=True		,     _a=True		,     _a=False	):
  				super().__init__()
  				__magic_name__	:	Dict			     =  nn.Parameter(torch.randn(_a	) * scale		,     requires_grad=_a	)
  				__magic_name__	:	Optional[int]			     =  log
  				__magic_name__	:	List[str]			     =  flip_sin_to_cos
  
  				if set_W_to_weight:
  					# to delete later
  					__magic_name__	:	List[Any]			     =  nn.Parameter(torch.randn(_a	) * scale		,     requires_grad=_a	)
  
  					__magic_name__	:	Tuple			     =  self.W
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				if self.log:
  					__magic_name__	:	Optional[Any]			     =  torch.log(_a	)
  
  				__magic_name__	:	List[Any]			     =  x[:, None] * self.weight[None, :] * 2 * np.pi
  
  				if self.flip_sin_to_cos:
  					__magic_name__	:	Any			     =  torch.cat([torch.cos(_a	), torch.sin(_a	)]		,     dim=-1	)
  				else:
  					__magic_name__	:	List[Any]			     =  torch.cat([torch.sin(_a	), torch.cos(_a	)]		,     dim=-1	)
  				return out
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     ):
  				super().__init__()
  
  				__magic_name__	:	Union[str, Any]			     =  height
  				__magic_name__	:	Tuple			     =  width
  				__magic_name__	:	Union[str, Any]			     =  num_embed
  				__magic_name__	:	str			     =  embed_dim
  
  				__magic_name__	:	List[Any]			     =  nn.Embedding(self.num_embed		,     _a	)
  				__magic_name__	:	int			     =  nn.Embedding(self.height		,     _a	)
  				__magic_name__	:	int			     =  nn.Embedding(self.width		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	str			     =  self.emb(_a	)
  
  				__magic_name__	:	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
  				__magic_name__	:	Any			     =  height_emb.unsqueeze(2	)
  
  				__magic_name__	:	List[str]			     =  self.width_emb(torch.arange(self.width		,     device=index.device	).view(1		,     self.width	)	)
  
  				# 1 x W x D -> 1 x 1 x W x D
  				__magic_name__	:	Dict			     =  width_emb.unsqueeze(1	)
  
  				__magic_name__	:	Union[str, Any]			     =  height_emb + width_emb
  
  				# 1 x H x W x D -> 1 x L xD
  				__magic_name__	:	Optional[Any]			     =  pos_emb.view(1		,     self.height * self.width		,     -1	)
  
  				__magic_name__	:	Union[str, Any]			     =  emb + pos_emb[:, : emb.shape[1], :]
  
  				return emb
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a	):
  				super().__init__()
  				__magic_name__	:	str			     =  dropout_prob > 0
  				__magic_name__	:	Tuple			     =  nn.Embedding(num_classes + use_cfg_embedding		,     _a	)
  				__magic_name__	:	Union[str, Any]			     =  num_classes
  				__magic_name__	:	int			     =  dropout_prob
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=None	):
  				if force_drop_ids is None:
  					__magic_name__	:	Dict			     =  torch.rand(labels.shape[0]		,     device=labels.device	) < self.dropout_prob
  				else:
  					__magic_name__	:	Optional[int]			     =  torch.tensor(force_drop_ids == 1	)
  				__magic_name__	:	List[str]			     =  torch.where(_a		,     self.num_classes		,     _a	)
  				return labels
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=None	):
  				__magic_name__	:	Optional[Any]			     =  self.dropout_prob > 0
  				if (self.training and use_dropout) or (force_drop_ids is not None):
  					__magic_name__	:	List[Any]			     =  self.token_drop(_a		,     _a	)
  				__magic_name__	:	List[Any]			     =  self.embedding_table(_a	)
  				return embeddings
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 1_024		,     _a = 768		,     _a = 768		,     _a = 10		,     ):
  				super().__init__()
  
  				__magic_name__	:	List[Any]			     =  num_image_text_embeds
  				__magic_name__	:	str			     =  nn.Linear(_a		,     self.num_image_text_embeds * cross_attention_dim	)
  				__magic_name__	:	Union[str, Any]			     =  nn.Linear(_a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				__magic_name__	:	Dict			     =  text_embeds.shape[0]
  
  				# image
  				__magic_name__	:	str			     =  self.image_embeds(_a	)
  				__magic_name__	:	Dict			     =  image_text_embeds.reshape(_a		,     self.num_image_text_embeds		,     -1	)
  
  				# text
  				__magic_name__	:	str			     =  self.text_proj(_a	)
  
  				return torch.cat([image_text_embeds, text_embeds]		,     dim=1	)
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 768		,     _a = 768		,     _a = 32		,     ):
  				super().__init__()
  
  				__magic_name__	:	Dict			     =  num_image_text_embeds
  				__magic_name__	:	str			     =  nn.Linear(_a		,     self.num_image_text_embeds * cross_attention_dim	)
  				__magic_name__	:	Any			     =  nn.LayerNorm(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Optional[Any]			     =  image_embeds.shape[0]
  
  				# image
  				__magic_name__	:	Dict			     =  self.image_embeds(_a	)
  				__magic_name__	:	Dict			     =  image_embeds.reshape(_a		,     self.num_image_text_embeds		,     -1	)
  				__magic_name__	:	str			     =  self.norm(_a	)
  				return image_embeds
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a=0.1	):
  				super().__init__()
  
  				__magic_name__	:	Tuple			     =  Timesteps(num_channels=256		,     flip_sin_to_cos=_a		,     downscale_freq_shift=1	)
  				__magic_name__	:	List[str]			     =  TimestepEmbedding(in_channels=256		,     time_embed_dim=_a	)
  				__magic_name__	:	Tuple			     =  LabelEmbedding(_a		,     _a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a=None	):
  				__magic_name__	:	Optional[Any]			     =  self.time_proj(_a	)
  				__magic_name__	:	Optional[Any]			     =  self.timestep_embedder(timesteps_proj.to(dtype=_a	)	)  # (N, D)
  
  				__magic_name__	:	int			     =  self.class_embedder(_a	)  # (N, D)
  
  				__magic_name__	:	str			     =  timesteps_emb + class_labels  # (N, D)
  
  				return conditioning
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a = 64	):
  				super().__init__()
  				__magic_name__	:	Any			     =  nn.LayerNorm(_a	)
  				__magic_name__	:	Optional[int]			     =  AttentionPooling(_a		,     _a	)
  				__magic_name__	:	Tuple			     =  nn.Linear(_a		,     _a	)
  				__magic_name__	:	Dict			     =  nn.LayerNorm(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Dict			     =  self.norma(_a	)
  				__magic_name__	:	Optional[int]			     =  self.pool(_a	)
  				__magic_name__	:	List[Any]			     =  self.proj(_a	)
  				__magic_name__	:	str			     =  self.norma(_a	)
  				return hidden_states
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 768		,     _a = 768		,     _a = 1_536	):
  				super().__init__()
  				__magic_name__	:	List[str]			     =  nn.Linear(_a		,     _a	)
  				__magic_name__	:	str			     =  nn.LayerNorm(_a	)
  				__magic_name__	:	Optional[Any]			     =  nn.Linear(_a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				# text
  				__magic_name__	:	Union[str, Any]			     =  self.text_proj(_a	)
  				__magic_name__	:	Optional[Any]			     =  self.text_norm(_a	)
  
  				# image
  				__magic_name__	:	List[Any]			     =  self.image_proj(_a	)
  
  				return time_image_embeds + time_text_embeds
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 768		,     _a = 1_536	):
  				super().__init__()
  				__magic_name__	:	List[str]			     =  nn.Linear(_a		,     _a	)
  				__magic_name__	:	List[Any]			     =  nn.LayerNorm(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				# image
  				__magic_name__	:	str			     =  self.image_proj(_a	)
  				__magic_name__	:	Any			     =  self.image_norm(_a	)
  				return time_image_embeds
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = 768		,     _a = 1_536	):
  				super().__init__()
  				__magic_name__	:	int			     =  nn.Linear(_a		,     _a	)
  				__magic_name__	:	int			     =  nn.LayerNorm(_a	)
  				__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				# image
  				__magic_name__	:	Any			     =  self.image_proj(_a	)
  				__magic_name__	:	str			     =  self.image_norm(_a	)
  				__magic_name__	:	Union[str, Any]			     =  self.input_hint_block(_a	)
  				return time_image_embeds, hint
  
  
  
  
  
  
  
  class    _snake_case      (  nn.Module       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a=None	):
  				super().__init__()
  				__magic_name__	:	int			     =  dtype
  				__magic_name__	:	int			     =  nn.Parameter(torch.randn(1		,     _a	) / embed_dim**0.5	)
  				__magic_name__	:	Optional[Any]			     =  nn.Linear(_a		,     _a		,     dtype=self.dtype	)
  				__magic_name__	:	Any			     =  nn.Linear(_a		,     _a		,     dtype=self.dtype	)
  				__magic_name__	:	Optional[int]			     =  nn.Linear(_a		,     _a		,     dtype=self.dtype	)
  				__magic_name__	:	List[Any]			     =  num_heads
  				__magic_name__	:	List[Any]			     =  embed_dim // self.num_heads
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Dict			     =  x.size()
  
  				def shape(_a	):
  					# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
  					__magic_name__	:	Any			     =  x.view(_a		,     -1		,     self.num_heads		,     self.dim_per_head	)
  					# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
  					__magic_name__	:	Dict			     =  x.transpose(1		,     2	)
  					# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
  					__magic_name__	:	Union[str, Any]			     =  x.reshape(bs * self.num_heads		,     -1		,     self.dim_per_head	)
  					# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
  					__magic_name__	:	Tuple			     =  x.transpose(1		,     2	)
  					return x
  
  				__magic_name__	:	List[Any]			     =  x.mean(dim=1		,     keepdim=_a	) + self.positional_embedding.to(x.dtype	)
  				__magic_name__	:	List[Any]			     =  torch.cat([class_token, x]		,     dim=1	)  # (bs, length+1, width)
  
  				# (bs*n_heads, class_token_length, dim_per_head)
  				__magic_name__	:	Optional[int]			     =  shape(self.q_proj(_a	)	)
  				# (bs*n_heads, length+class_token_length, dim_per_head)
  				__magic_name__	:	List[str]			     =  shape(self.k_proj(_a	)	)
  				__magic_name__	:	List[str]			     =  shape(self.v_proj(_a	)	)
  
  				# (bs*n_heads, class_token_length, length+class_token_length):
  				__magic_name__	:	Union[str, Any]			     =  1 / math.sqrt(math.sqrt(self.dim_per_head	)	)
  				__magic_name__	:	List[str]			     =  torch.einsum("bct,bcs->bts"		,     q * scale		,     k * scale	)  # More stable with f16 than dividing afterwards
  				__magic_name__	:	List[Any]			     =  torch.softmax(weight.float()		,     dim=-1	).type(weight.dtype	)
  
  				# (bs*n_heads, dim_per_head, class_token_length)
  				__magic_name__	:	Optional[Any]			     =  torch.einsum("bts,bcs->bct"		,     _a		,     _a	)
  
  				# (bs, length+1, width)
  				__magic_name__	:	List[str]			     =  a.reshape(_a		,     -1		,     1	).transpose(1		,     2	)
  
  				return a[:, 0, :]  # cls_token
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  