


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def   lowerCamelCase(      a__		,a__		,a__ = False		,a__ = 1		,a__ = 1		,a__ = 1_0000		,):
				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   lowerCamelCase(      a__		,a__		,a__=False		,a__=0):
				_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   lowerCamelCase(      a__		,a__):
				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   lowerCamelCase(      a__		,a__):
				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   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()

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

										_SCREAMING_SNAKE_CASE	     =nn.Convad(
										    _a ,			_a ,			kernel_size=(patch_size, patch_size) ,			stride=_a ,			bias=_a     )
										if layer_norm:
														_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a ,			elementwise_affine=_a ,			eps=1E-6     )
										else:
														_SCREAMING_SNAKE_CASE	     =None

										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					List[str] ,			_a    :					str     )							->   List[str]:


										"""simple docstring"""





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

class   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )

										if cond_proj_dim is not None:
														_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a ,			bias=_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =None

										_SCREAMING_SNAKE_CASE	     =get_activation(_a     )

										if out_dim is not None:
														_SCREAMING_SNAKE_CASE	     =out_dim
										else:
														_SCREAMING_SNAKE_CASE	     =time_embed_dim
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )

										if post_act_fn is None:
														_SCREAMING_SNAKE_CASE	     =None
										else:
														_SCREAMING_SNAKE_CASE	     =get_activation(_a     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					str ,			_a    :					str=None     )							->   str:


										"""simple docstring"""





										if condition is not None:
														_SCREAMING_SNAKE_CASE	     =sample + self.cond_proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.linear_a(_a     )

										if self.act is not None:
														_SCREAMING_SNAKE_CASE	     =self.act(_a     )

										_SCREAMING_SNAKE_CASE	     =self.linear_a(_a     )

										if self.post_act is not None:
														_SCREAMING_SNAKE_CASE	     =self.post_act(_a     )
										return sample

class   A__							(						nn.Module     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					int ,			_a    :					bool ,			_a    :					float     )							->   Dict:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Optional[int]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE	     =nn.Parameter(torch.randn(_a     ) * scale ,			requires_grad=_a     )
										_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(_a     ) * scale ,			requires_grad=_a     )

														_SCREAMING_SNAKE_CASE	     =self.W




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int     )							->   Any:


										"""simple docstring"""





										if self.log:
														_SCREAMING_SNAKE_CASE	     =torch.log(_a     )

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

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

class   A__							(						nn.Module     ):




						def __init__(				self    :					Dict ,			_a    :					int ,			_a    :					int ,			_a    :					int ,			_a    :					int ,			)							->   Any:


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE	     =height
										_SCREAMING_SNAKE_CASE	     =width
										_SCREAMING_SNAKE_CASE	     =num_embed
										_SCREAMING_SNAKE_CASE	     =embed_dim

										_SCREAMING_SNAKE_CASE	     =nn.Embedding(self.num_embed ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.Embedding(self.height ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.Embedding(self.width ,			_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Dict     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.emb(_a     )

										_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   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE	     =dropout_prob > 0
										_SCREAMING_SNAKE_CASE	     =nn.Embedding(num_classes + use_cfg_embedding ,			_a     )
										_SCREAMING_SNAKE_CASE	     =num_classes
										_SCREAMING_SNAKE_CASE	     =dropout_prob




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


										"""simple docstring"""





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




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


										"""simple docstring"""





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

class   A__							(						nn.Module     ):




						def __init__(				self    :					int ,			_a    :					int = 1024 ,			_a    :					int = 768 ,			_a    :					int = 768 ,			_a    :					int = 10 ,			)							->   Any:


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE	     =num_image_text_embeds
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			self.num_image_text_embeds * cross_attention_dim     )
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					torch.FloatTensor ,			_a    :					torch.FloatTensor     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =text_embeds.shape[0]

										# image
										_SCREAMING_SNAKE_CASE	     =self.image_embeds(_a     )
										_SCREAMING_SNAKE_CASE	     =image_text_embeds.reshape(_a ,			self.num_image_text_embeds ,			-1     )

										# text
										_SCREAMING_SNAKE_CASE	     =self.text_proj(_a     )

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

class   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE	     =num_image_text_embeds
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			self.num_image_text_embeds * cross_attention_dim     )
										_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					torch.FloatTensor     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =image_embeds.shape[0]

										# image
										_SCREAMING_SNAKE_CASE	     =self.image_embeds(_a     )
										_SCREAMING_SNAKE_CASE	     =image_embeds.reshape(_a ,			self.num_image_text_embeds ,			-1     )
										_SCREAMING_SNAKE_CASE	     =self.norm(_a     )
										return image_embeds

class   A__							(						nn.Module     ):




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


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE	     =Timesteps(num_channels=256 ,			flip_sin_to_cos=_a ,			downscale_freq_shift=1     )
										_SCREAMING_SNAKE_CASE	     =TimestepEmbedding(in_channels=256 ,			time_embed_dim=_a     )
										_SCREAMING_SNAKE_CASE	     =LabelEmbedding(_a ,			_a ,			_a     )




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					Any ,			_a    :					List[str] ,			_a    :					Optional[Any]=None     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.time_proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.timestep_embedder(timesteps_proj.to(dtype=_a     )     )  # (N, D)

										_SCREAMING_SNAKE_CASE	     =self.class_embedder(_a     )  # (N, D)

										_SCREAMING_SNAKE_CASE	     =timesteps_emb + class_labels  # (N, D)

										return conditioning

class   A__							(						nn.Module     ):




						def __init__(				self    :					str ,			_a    :					int ,			_a    :					int ,			_a    :					int = 64     )							->   List[str]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a     )
										_SCREAMING_SNAKE_CASE	     =AttentionPooling(_a ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a     )




						def 							__UpperCamelCase (				self    :					Tuple ,			_a    :					str     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.norma(_a     )
										_SCREAMING_SNAKE_CASE	     =self.pool(_a     )
										_SCREAMING_SNAKE_CASE	     =self.proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.norma(_a     )
										return hidden_states

class   A__							(						nn.Module     ):




						def __init__(				self    :					Any ,			_a    :					int = 768 ,			_a    :					int = 768 ,			_a    :					int = 1536     )							->   str:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a     )
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					torch.FloatTensor ,			_a    :					torch.FloatTensor     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.text_proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.text_norm(_a     )

										# image
										_SCREAMING_SNAKE_CASE	     =self.image_proj(_a     )

										return time_image_embeds + time_text_embeds

class   A__							(						nn.Module     ):




						def __init__(				self    :					str ,			_a    :					int = 768 ,			_a    :					int = 1536     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE	     =nn.Linear(_a ,			_a     )
										_SCREAMING_SNAKE_CASE	     =nn.LayerNorm(_a     )




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					torch.FloatTensor     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.image_proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.image_norm(_a     )
										return time_image_embeds

class   A__							(						nn.Module     ):




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


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					str ,			_a    :					torch.FloatTensor ,			_a    :					torch.FloatTensor     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.image_proj(_a     )
										_SCREAMING_SNAKE_CASE	     =self.image_norm(_a     )
										_SCREAMING_SNAKE_CASE	     =self.input_hint_block(_a     )
										return time_image_embeds, hint

class   A__							(						nn.Module     ):




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


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					int ,			_a    :					str     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =x.size()

										def shape(_a    :					Tuple     ):
														# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
														_SCREAMING_SNAKE_CASE	     =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)
														_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=_a     ) + 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(_a     )     )
										# (bs*n_heads, length+class_token_length, dim_per_head)
										_SCREAMING_SNAKE_CASE	     =shape(self.k_proj(_a     )     )
										_SCREAMING_SNAKE_CASE	     =shape(self.v_proj(_a     )     )

										# (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''' ,			_a ,			_a     )

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

										return a[:, 0, :]  # cls_token


from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


