
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def       a			(   a		,	a=3  )    ->Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   int(mask / 2  )
			# copy image size
			SCREAMING_SNAKE_CASE    	=   zeros_like(a  )
			for i in range(a		,	gray_img.shape[0] - bd  ):
						for j in range(a		,	gray_img.shape[1] - bd  ):
									# get mask according with mask
									SCREAMING_SNAKE_CASE    	=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]  )
									# calculate mask median
									SCREAMING_SNAKE_CASE    	=   sort(a  )[inta(divide((multiply(a		,	a  ))		,	2  ) + 1  )]
									SCREAMING_SNAKE_CASE    	=   median
			return median_img


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase				      =					imread('../image_data/lena.jpg')
							# turn image in gray scale value
							__lowerCAmelCase				      =					cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__lowerCAmelCase				      =					median_filter(gray, 3)
							__lowerCAmelCase				      =					median_filter(gray, 5)

							# show result images
							imshow('median filter with 3x3 mask', medianaxa)
							imshow('median filter with 5x5 mask', medianaxa)
							waitKey(0)

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