
'''simple docstring'''

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 					A       ( UpperCamelCase_   :       torch.Tensor  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       bool = False  ,		UpperCamelCase_   :       float = 1  ,		UpperCamelCase_   :       float = 1  ,		UpperCamelCase_   :       int = 1_00_00  ,		)   ->    str:







			'''simple docstring'''



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

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

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

			# scale embeddings
			lowerCAmelCase__									=							scale * emb

			# concat sine and cosine embeddings
			lowerCAmelCase__									=							torch.cat([torch.sin(UpperCamelCase_ ), torch.cos(UpperCamelCase_ )]  ,		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(UpperCamelCase_  ,		(0, 1, 0, 0) )
			return emb







def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Dict=False  ,		UpperCamelCase_   :       Tuple=0 )   ->    Tuple:







			'''simple docstring'''



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

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







def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Union[str, Any] )   ->    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
			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 					A       ( UpperCamelCase_   :       Any  ,		UpperCamelCase_   :       Tuple )   ->    Union[str, Any]:







			'''simple docstring'''



			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_00_00**omega  # (D/2,)

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

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

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






class 				A       (		nn.Module      ):





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


				"""simple docstring"""

				super().__init__()

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

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

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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






class 				A       (		nn.Module      ):





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


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							nn.Linear(__magic_name__ ,							__magic_name__							)

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

				lowerCAmelCase__									=							get_activation(__magic_name__							)

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

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Tuple ,							__magic_name__     :    Optional[Any]=None							):


				"""simple docstring"""

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

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

				lowerCAmelCase__									=							self.linear_a(__magic_name__							)

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






class 				A       (		nn.Module      ):





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


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Dict							):


				"""simple docstring"""

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


				"""simple docstring"""

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

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

							lowerCAmelCase__									=							self.W







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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

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

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    str ,							__magic_name__     :    int ,							__magic_name__     :    int ,							__magic_name__     :    int ,							__magic_name__     :    int ,							):


				"""simple docstring"""

				super().__init__()

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

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.emb(__magic_name__							)

				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      ):





	def __init__(   self     :    int ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    str ,							__magic_name__     :    int=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(__magic_name__ ,							self.num_classes ,							__magic_name__							)
				return labels







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    torch.LongTensor ,							__magic_name__     :    Any=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(__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							self.embedding_table(__magic_name__							)
				return embeddings






class 				A       (		nn.Module      ):





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    int = 1024 ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 10 ,							):


				"""simple docstring"""

				super().__init__()

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__									=							text_embeds.shape[0]

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

				# text
				lowerCAmelCase__									=							self.text_proj(__magic_name__							)

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    int ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 32 ,							):


				"""simple docstring"""

				super().__init__()

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__									=							image_embeds.shape[0]

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Any ,							__magic_name__     :    Dict=0.1							):


				"""simple docstring"""

				super().__init__()

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Tuple ,							__magic_name__     :    List[str] ,							__magic_name__     :    List[Any]=None							):


				"""simple docstring"""

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

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

				lowerCAmelCase__									=							timesteps_emb + class_labels  # (N, D)

				return conditioning






class 				A       (		nn.Module      ):





	def __init__(   self     :    List[Any] ,							__magic_name__     :    int ,							__magic_name__     :    int ,							__magic_name__     :    int = 64							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    List[str]							):


				"""simple docstring"""

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    Dict ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 1536							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    torch.FloatTensor							):


				"""simple docstring"""

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

				# image
				lowerCAmelCase__									=							self.image_proj(__magic_name__							)

				return time_image_embeds + time_text_embeds






class 				A       (		nn.Module      ):





	def __init__(   self     :    Any ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 1536							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    torch.FloatTensor							):


				"""simple docstring"""

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    List[Any] ,							__magic_name__     :    int = 768 ,							__magic_name__     :    int = 1536							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__									=							nn.Linear(__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							nn.LayerNorm(__magic_name__							)
				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 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    torch.FloatTensor							):


				"""simple docstring"""

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






class 				A       (		nn.Module      ):





	def __init__(   self     :    int ,							__magic_name__     :    Dict ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    int=None							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							x.size()

				def shape(__magic_name__     :    Optional[Any]							):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							lowerCAmelCase__									=							x.view(__magic_name__ ,							-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=__magic_name__							) + 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(__magic_name__							)							)
				# (bs*n_heads, length+class_token_length, dim_per_head)
				lowerCAmelCase__									=							shape(self.k_proj(__magic_name__							)							)
				lowerCAmelCase__									=							shape(self.v_proj(__magic_name__							)							)

				# (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" ,							__magic_name__ ,							__magic_name__							)

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

				return a[:, 0, :]  # cls_token







'''simple docstring'''

import math







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





