






"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase = False     ,  _lowerCamelCase = 1     ,  _lowerCamelCase = 1     ,  _lowerCamelCase = 1_0000     ,  ):
 assert len(timesteps.shape							) == 1, "Timesteps should be a 1d-array"

 lowerCamelCase__	:     Tuple								=		embedding_dim // 2
 lowerCamelCase__	:     Union[str, Any]								=		-math.log(_lowerCamelCase							) * torch.arange(
     start=0     ,  end=_lowerCamelCase     ,  dtype=torch.floataa     ,  device=timesteps.device							)
 lowerCamelCase__	:     Optional[Any]								=		exponent / (half_dim - downscale_freq_shift)

 lowerCamelCase__	:     List[str]								=		torch.exp(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 lowerCamelCase__	:     Dict								=		scale * emb

 # concat sine and cosine embeddings
 lowerCamelCase__	:     int								=		torch.cat([torch.sin(_lowerCamelCase							), torch.cos(_lowerCamelCase							)]     ,  dim=-1							)

 # flip sine and cosine embeddings
 if flip_sin_to_cos:
  lowerCamelCase__	:     Any								=		torch.cat([emb[:, half_dim:], emb[:, :half_dim]]     ,  dim=-1							)

 # zero pad
 if embedding_dim % 2 == 1:
  lowerCamelCase__	:     List[str]								=		torch.nn.functional.pad(_lowerCamelCase     ,  (0, 1, 0, 0)							)
 return emb






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase=False     ,  _lowerCamelCase=0							):
 lowerCamelCase__	:     Optional[int]								=		np.arange(_lowerCamelCase     ,  dtype=np.floataa							)
 lowerCamelCase__	:     Optional[int]								=		np.arange(_lowerCamelCase     ,  dtype=np.floataa							)
 lowerCamelCase__	:     Optional[int]								=		np.meshgrid(_lowerCamelCase     ,  _lowerCamelCase							)  # here w goes first
 lowerCamelCase__	:     Any								=		np.stack(_lowerCamelCase     ,  axis=0							)

 lowerCamelCase__	:     List[str]								=		grid.reshape([2, 1, grid_size, grid_size]							)
 lowerCamelCase__	:     int								=		get_ad_sincos_pos_embed_from_grid(_lowerCamelCase     ,  _lowerCamelCase							)
 if cls_token and extra_tokens > 0:
  lowerCamelCase__	:     List[str]								=		np.concatenate([np.zeros([extra_tokens, embed_dim]							), pos_embed]     ,  axis=0							)
 return pos_embed






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if embed_dim % 2 != 0:
  raise ValueError('embed_dim must be divisible by 2'							)

 # use half of dimensions to encode grid_h
 lowerCamelCase__	:     Any								=		get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,  grid[0]							)  # (H*W, D/2)
 lowerCamelCase__	:     Union[str, Any]								=		get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,  grid[1]							)  # (H*W, D/2)

 lowerCamelCase__	:     List[Any]								=		np.concatenate([emb_h, emb_w]     ,  axis=1							)  # (H*W, D)
 return emb






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if embed_dim % 2 != 0:
  raise ValueError('embed_dim must be divisible by 2'							)

 lowerCamelCase__	:     Union[str, Any]								=		np.arange(embed_dim // 2     ,  dtype=np.floataa							)
 omega /= embed_dim / 2.0
 lowerCamelCase__	:     List[Any]								=		1.0 / 1_0000**omega  # (D/2,)

 lowerCamelCase__	:     Dict								=		pos.reshape(-1							)  # (M,)
 lowerCamelCase__	:     str								=		np.einsum('m,d->md'     ,  _lowerCamelCase     ,  _lowerCamelCase							)  # (M, D/2), outer product

 lowerCamelCase__	:     List[Any]								=		np.sin(_lowerCamelCase							)  # (M, D/2)
 lowerCamelCase__	:     Dict								=		np.cos(_lowerCamelCase							)  # (M, D/2)

 lowerCamelCase__	:     Optional[Any]								=		np.concatenate([emb_sin, emb_cos]     ,  axis=1							)  # (M, D)
 return emb



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_=2_2_4,	lowerCamelCase_=2_2_4,	lowerCamelCase_=1_6,	lowerCamelCase_=3,	lowerCamelCase_=7_6_8,	lowerCamelCase_=False,	lowerCamelCase_=True,	lowerCamelCase_=True,	):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     Any								=		(height // patch_size) * (width // patch_size)
       lowerCamelCase__	:     Tuple								=		flatten
       lowerCamelCase__	:     List[Any]								=		layer_norm

       lowerCamelCase__	:     Union[str, Any]								=		nn.Convad(
           lowerCamelCase_,	lowerCamelCase_,	kernel_size=(patch_size, patch_size),	stride=lowerCamelCase_,	bias=lowerCamelCase_   )
       if layer_norm:
        lowerCamelCase__	:     int								=		nn.LayerNorm(lowerCamelCase_,	elementwise_affine=lowerCamelCase_,	eps=1e-6   )
       else:
        lowerCamelCase__	:     Tuple								=		None

       lowerCamelCase__	:     List[Any]								=		get_ad_sincos_pos_embed(lowerCamelCase_,	int(num_patches**0.5   )   )
       self.register_buffer('pos_embed',	torch.from_numpy(lowerCamelCase_   ).float().unsqueeze(0   ),	persistent=lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.proj(lowerCamelCase_   )
       if self.flatten:
        lowerCamelCase__	:     Optional[Any]								=		latent.flatten(2   ).transpose(1,	2   )  # BCHW -> BNC
       if self.layer_norm:
        lowerCamelCase__	:     Optional[Any]								=		self.norm(lowerCamelCase_   )
       return latent + self.pos_embed



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = "silu",	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_=None,	):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     int								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )

       if cond_proj_dim is not None:
        lowerCamelCase__	:     List[str]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_,	bias=lowerCamelCase_   )
       else:
        lowerCamelCase__	:     List[str]								=		None

       lowerCamelCase__	:     int								=		get_activation(lowerCamelCase_   )

       if out_dim is not None:
        lowerCamelCase__	:     int								=		out_dim
       else:
        lowerCamelCase__	:     Union[str, Any]								=		time_embed_dim
       lowerCamelCase__	:     Optional[Any]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )

       if post_act_fn is None:
        lowerCamelCase__	:     Any								=		None
       else:
        lowerCamelCase__	:     Optional[Any]								=		get_activation(lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       if condition is not None:
        lowerCamelCase__	:     str								=		sample + self.cond_proj(lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		self.linear_a(lowerCamelCase_   )

       if self.act is not None:
        lowerCamelCase__	:     List[Any]								=		self.act(lowerCamelCase_   )

       lowerCamelCase__	:     List[Any]								=		self.linear_a(lowerCamelCase_   )

       if self.post_act is not None:
        lowerCamelCase__	:     Union[str, Any]								=		self.post_act(lowerCamelCase_   )
       return sample



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     List[str]								=		num_channels
       lowerCamelCase__	:     List[Any]								=		flip_sin_to_cos
       lowerCamelCase__	:     Tuple								=		downscale_freq_shift





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		get_timestep_embedding(
           lowerCamelCase_,	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					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 2_5_6,	lowerCamelCase_ = 1.0,	lowerCamelCase_=True,	lowerCamelCase_=True,	lowerCamelCase_=False   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     str								=		nn.Parameter(torch.randn(lowerCamelCase_   ) * scale,	requires_grad=lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		log
       lowerCamelCase__	:     List[str]								=		flip_sin_to_cos

       if set_W_to_weight:
        # to delete later
        lowerCamelCase__	:     int								=		nn.Parameter(torch.randn(lowerCamelCase_   ) * scale,	requires_grad=lowerCamelCase_   )

        lowerCamelCase__	:     Optional[Any]								=		self.W





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       if self.log:
        lowerCamelCase__	:     Dict								=		torch.log(lowerCamelCase_   )

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

       if self.flip_sin_to_cos:
        lowerCamelCase__	:     List[Any]								=		torch.cat([torch.cos(lowerCamelCase_   ), torch.sin(lowerCamelCase_   )],	dim=-1   )
       else:
        lowerCamelCase__	:     Optional[Any]								=		torch.cat([torch.sin(lowerCamelCase_   ), torch.cos(lowerCamelCase_   )],	dim=-1   )
       return out



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     str								=		height
       lowerCamelCase__	:     str								=		width
       lowerCamelCase__	:     List[Any]								=		num_embed
       lowerCamelCase__	:     Dict								=		embed_dim

       lowerCamelCase__	:     str								=		nn.Embedding(self.num_embed,	lowerCamelCase_   )
       lowerCamelCase__	:     Tuple								=		nn.Embedding(self.height,	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[Any]								=		nn.Embedding(self.width,	lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.emb(lowerCamelCase_   )

       lowerCamelCase__	:     int								=		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__	:     List[Any]								=		height_emb.unsqueeze(2   )

       lowerCamelCase__	:     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
       lowerCamelCase__	:     Dict								=		width_emb.unsqueeze(1   )

       lowerCamelCase__	:     List[Any]								=		height_emb + width_emb

       # 1 x H x W x D -> 1 x L xD
       lowerCamelCase__	:     List[str]								=		pos_emb.view(1,	self.height * self.width,	-1   )

       lowerCamelCase__	:     Dict								=		emb + pos_emb[:, : emb.shape[1], :]

       return emb



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     Any								=		dropout_prob > 0
       lowerCamelCase__	:     Optional[int]								=		nn.Embedding(num_classes + use_cfg_embedding,	lowerCamelCase_   )
       lowerCamelCase__	:     int								=		num_classes
       lowerCamelCase__	:     Dict								=		dropout_prob




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       if force_drop_ids is None:
        lowerCamelCase__	:     List[str]								=		torch.rand(labels.shape[0],	device=labels.device   ) < self.dropout_prob
       else:
        lowerCamelCase__	:     Union[str, Any]								=		torch.tensor(force_drop_ids == 1   )
       lowerCamelCase__	:     Dict								=		torch.where(lowerCamelCase_,	self.num_classes,	lowerCamelCase_   )
       return labels





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		self.dropout_prob > 0
       if (self.training and use_dropout) or (force_drop_ids is not None):
        lowerCamelCase__	:     List[str]								=		self.token_drop(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     List[str]								=		self.embedding_table(lowerCamelCase_   )
       return embeddings



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 1_0_2_4,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 1_0,	):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     Optional[int]								=		num_image_text_embeds
       lowerCamelCase__	:     Any								=		nn.Linear(lowerCamelCase_,	self.num_image_text_embeds * cross_attention_dim   )
       lowerCamelCase__	:     List[Any]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		text_embeds.shape[0]

       # image
       lowerCamelCase__	:     Optional[Any]								=		self.image_embeds(lowerCamelCase_   )
       lowerCamelCase__	:     Any								=		image_text_embeds.reshape(lowerCamelCase_,	self.num_image_text_embeds,	-1   )

       # text
       lowerCamelCase__	:     Optional[Any]								=		self.text_proj(lowerCamelCase_   )

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



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 3_2,	):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     List[Any]								=		num_image_text_embeds
       lowerCamelCase__	:     Union[str, Any]								=		nn.Linear(lowerCamelCase_,	self.num_image_text_embeds * cross_attention_dim   )
       lowerCamelCase__	:     int								=		nn.LayerNorm(lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		image_embeds.shape[0]

       # image
       lowerCamelCase__	:     Tuple								=		self.image_embeds(lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		image_embeds.reshape(lowerCamelCase_,	self.num_image_text_embeds,	-1   )
       lowerCamelCase__	:     Dict								=		self.norm(lowerCamelCase_   )
       return image_embeds



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=0.1   ):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     Tuple								=		Timesteps(num_channels=2_5_6,	flip_sin_to_cos=lowerCamelCase_,	downscale_freq_shift=1   )
       lowerCamelCase__	:     Union[str, Any]								=		TimestepEmbedding(in_channels=2_5_6,	time_embed_dim=lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		LabelEmbedding(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.time_proj(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase_   )   )  # (N, D)

       lowerCamelCase__	:     int								=		self.class_embedder(lowerCamelCase_   )  # (N, D)

       lowerCamelCase__	:     int								=		timesteps_emb + class_labels  # (N, D)

       return conditioning



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = 6_4   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     int								=		nn.LayerNorm(lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		AttentionPooling(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Any								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		nn.LayerNorm(lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.norma(lowerCamelCase_   )
       lowerCamelCase__	:     Any								=		self.pool(lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		self.proj(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		self.norma(lowerCamelCase_   )
       return hidden_states



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 1_5_3_6   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     List[Any]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		nn.LayerNorm(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.text_proj(lowerCamelCase_   )
       lowerCamelCase__	:     Optional[Any]								=		self.text_norm(lowerCamelCase_   )

       # image
       lowerCamelCase__	:     Any								=		self.image_proj(lowerCamelCase_   )

       return time_image_embeds + time_text_embeds



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 1_5_3_6   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     int								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		nn.LayerNorm(lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.image_proj(lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		self.image_norm(lowerCamelCase_   )
       return time_image_embeds



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_ = 7_6_8,	lowerCamelCase_ = 1_5_3_6   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     Dict								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		nn.LayerNorm(lowerCamelCase_   )
       lowerCamelCase__	:     List[str]								=		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      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.image_proj(lowerCamelCase_   )
       lowerCamelCase__	:     Any								=		self.image_norm(lowerCamelCase_   )
       lowerCamelCase__	:     str								=		self.input_hint_block(lowerCamelCase_   )
       return time_image_embeds, hint



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     Any								=		dtype
       lowerCamelCase__	:     Optional[Any]								=		nn.Parameter(torch.randn(1,	lowerCamelCase_   ) / embed_dim**0.5   )
       lowerCamelCase__	:     Optional[int]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_,	dtype=self.dtype   )
       lowerCamelCase__	:     Dict								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_,	dtype=self.dtype   )
       lowerCamelCase__	:     List[Any]								=		nn.Linear(lowerCamelCase_,	lowerCamelCase_,	dtype=self.dtype   )
       lowerCamelCase__	:     Optional[Any]								=		num_heads
       lowerCamelCase__	:     List[str]								=		embed_dim // self.num_heads





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	:     List[str]								=		x.size()

       def shape(lowerCamelCase_   ):
        # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
        lowerCamelCase__	:     Tuple								=		x.view(lowerCamelCase_,	-1,	self.num_heads,	self.dim_per_head   )
        # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
        lowerCamelCase__	:     str								=		x.transpose(1,	2   )
        # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
        lowerCamelCase__	:     Optional[int]								=		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__	:     Optional[int]								=		x.transpose(1,	2   )
        return x

       lowerCamelCase__	:     Tuple								=		x.mean(dim=1,	keepdim=lowerCamelCase_   ) + self.positional_embedding.to(x.dtype   )
       lowerCamelCase__	:     str								=		torch.cat([class_token, x],	dim=1   )  # (bs, length+1, width)

       # (bs*n_heads, class_token_length, dim_per_head)
       lowerCamelCase__	:     List[Any]								=		shape(self.q_proj(lowerCamelCase_   )   )
       # (bs*n_heads, length+class_token_length, dim_per_head)
       lowerCamelCase__	:     Union[str, Any]								=		shape(self.k_proj(lowerCamelCase_   )   )
       lowerCamelCase__	:     List[Any]								=		shape(self.v_proj(lowerCamelCase_   )   )

       # (bs*n_heads, class_token_length, length+class_token_length):
       lowerCamelCase__	:     List[str]								=		1 / math.sqrt(math.sqrt(self.dim_per_head   )   )
       lowerCamelCase__	:     List[Any]								=		torch.einsum('bct,bcs->bts',	q * scale,	k * scale   )  # More stable with f16 than dividing afterwards
       lowerCamelCase__	:     Dict								=		torch.softmax(weight.float(),	dim=-1   ).type(weight.dtype   )

       # (bs*n_heads, dim_per_head, class_token_length)
       lowerCamelCase__	:     int								=		torch.einsum('bts,bcs->bct',	lowerCamelCase_,	lowerCamelCase_   )

       # (bs, length+1, width)
       lowerCamelCase__	:     List[Any]								=		a.reshape(lowerCamelCase_,	-1,	1   ).transpose(1,	2   )

       return a[:, 0, :]  # cls_token









"""simple docstring"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		HfArgumentParser(_lowerCamelCase							)
 try:
  lowerCamelCase__	:     Dict								=		parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  lowerCamelCase__	:     Optional[int]								=		'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  lowerCamelCase__	:     Optional[Any]								=		' '.join(str(_lowerCamelCase							).split(' '							)[:-1]							)
  lowerCamelCase__	:     int								=		''
  lowerCamelCase__	:     Optional[int]								=		eval(str(_lowerCamelCase							).split(' '							)[-1]							)
  lowerCamelCase__	:     Tuple								=		[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]							)
   else:
    wrong_args.append(_lowerCamelCase							)
  if len(_lowerCamelCase							) > 0:
   lowerCamelCase__	:     Optional[int]								=		full_error_msg + begin_error_msg + str(_lowerCamelCase							)
  raise ValueError(_lowerCamelCase							)

 lowerCamelCase__	:     Any								=		PyTorchBenchmark(args=_lowerCamelCase							)
 benchmark.run()


if __name__ == "__main__":
      main()

