






'''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_  (     lowercase__					,  lowercase__					,  lowercase__ = False					,  lowercase__ = 1					,  lowercase__ = 1					,  lowercase__ = 10000					,  ):
 assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

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

 lowerCamelCase__	=  torch.exp(SCREAMING_SNAKE_CASE_)
 lowerCamelCase__	=  timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 lowerCamelCase__	=  scale * emb

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






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__=False					,  lowercase__=0):
 lowerCamelCase__	=  np.arange(SCREAMING_SNAKE_CASE_					,  dtype=np.floataa)
 lowerCamelCase__	=  np.arange(SCREAMING_SNAKE_CASE_					,  dtype=np.floataa)
 lowerCamelCase__	=  np.meshgrid(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)  # here w goes first
 lowerCamelCase__	=  np.stack(SCREAMING_SNAKE_CASE_					,  axis=0)

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






def  lowerCamelCase_  (     lowercase__					,  lowercase__):





 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  lowerCamelCase_  (     lowercase__					,  lowercase__):





 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 / 10000**omega  # (D/2,)

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

 lowerCamelCase__	=  np.sin(SCREAMING_SNAKE_CASE_)  # (M, D/2)
 lowerCamelCase__	=  np.cos(SCREAMING_SNAKE_CASE_)  # (M, D/2)

 lowerCamelCase__	=  np.concatenate([emb_sin, emb_cos]					,  axis=1)  # (M, D)
 return emb
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()

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

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

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




    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  List[str]  )				->				Dict:

     '''simple docstring'''




     lowerCamelCase__	=  self.proj(__SCREAMING_SNAKE_CASE  )
     if self.flatten:
      lowerCamelCase__	=  latent.flatten(2  ).transpose(1					,  2  )  # BCHW -> BNC
     if self.layer_norm:
      lowerCamelCase__	=  self.norm(__SCREAMING_SNAKE_CASE  )
     return latent + self.pos_embed
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )

     if cond_proj_dim is not None:
      lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE					,  bias=__SCREAMING_SNAKE_CASE  )
     else:
      lowerCamelCase__	=  None

     lowerCamelCase__	=  get_activation(__SCREAMING_SNAKE_CASE  )

     if out_dim is not None:
      lowerCamelCase__	=  out_dim
     else:
      lowerCamelCase__	=  time_embed_dim
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )

     if post_act_fn is None:
      lowerCamelCase__	=  None
     else:
      lowerCamelCase__	=  get_activation(__SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  Tuple					,  __lowerCamelCase  :  Union[str, Any]=None  )				->				Tuple:

     '''simple docstring'''




     if condition is not None:
      lowerCamelCase__	=  sample + self.cond_proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.linear_a(__SCREAMING_SNAKE_CASE  )

     if self.act is not None:
      lowerCamelCase__	=  self.act(__SCREAMING_SNAKE_CASE  )

     lowerCamelCase__	=  self.linear_a(__SCREAMING_SNAKE_CASE  )

     if self.post_act is not None:
      lowerCamelCase__	=  self.post_act(__SCREAMING_SNAKE_CASE  )
     return sample
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  str  )				->				Any:

     '''simple docstring'''




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




    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  int  )				->				Optional[int]:

     '''simple docstring'''




     lowerCamelCase__	=  get_timestep_embedding(
         __SCREAMING_SNAKE_CASE					,  self.num_channels					,  flip_sin_to_cos=self.flip_sin_to_cos					,  downscale_freq_shift=self.downscale_freq_shift					,  )
     return t_emb
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Optional[int]					,  __lowerCamelCase  :  Optional[Any] = 256					,  __lowerCamelCase  :  Union[str, Any] = 1.0					,  __lowerCamelCase  :  int=True					,  __lowerCamelCase  :  List[str]=True					,  __lowerCamelCase  :  Tuple=False  )				->				Dict:

     '''simple docstring'''




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

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

      lowerCamelCase__	=  self.W




    def  a__					(       self  :  List[Any]					,  __lowerCamelCase  :  Any  )				->				int:

     '''simple docstring'''




     if self.log:
      lowerCamelCase__	=  torch.log(__SCREAMING_SNAKE_CASE  )

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

     if self.flip_sin_to_cos:
      lowerCamelCase__	=  torch.cat([torch.cos(__SCREAMING_SNAKE_CASE  ), torch.sin(__SCREAMING_SNAKE_CASE  )]					,  dim=-1  )
     else:
      lowerCamelCase__	=  torch.cat([torch.sin(__SCREAMING_SNAKE_CASE  ), torch.cos(__SCREAMING_SNAKE_CASE  )]					,  dim=-1  )
     return out
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Optional[Any]					,  __lowerCamelCase  :  Optional[Any]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  Any					,  )				->				str:

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__	=  height
     lowerCamelCase__	=  width
     lowerCamelCase__	=  num_embed
     lowerCamelCase__	=  embed_dim

     lowerCamelCase__	=  nn.Embedding(self.num_embed					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.Embedding(self.height					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.Embedding(self.width					,  __SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Tuple					,  __lowerCamelCase  :  int  )				->				Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__	=  self.emb(__SCREAMING_SNAKE_CASE  )

     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 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Dict					,  __lowerCamelCase  :  Any					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  Tuple  )				->				Optional[int]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  dropout_prob > 0
     lowerCamelCase__	=  nn.Embedding(num_classes + use_cfg_embedding					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  num_classes
     lowerCamelCase__	=  dropout_prob

    def  a__					(       self  :  int					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Any=None  )				->				List[str]:

     '''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(__SCREAMING_SNAKE_CASE					,  self.num_classes					,  __SCREAMING_SNAKE_CASE  )
     return labels




    def  a__					(       self  :  Any					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Dict=None  )				->				Any:

     '''simple docstring'''




     lowerCamelCase__	=  self.dropout_prob > 0
     if (self.training and use_dropout) or (force_drop_ids is not None):
      lowerCamelCase__	=  self.token_drop(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.embedding_table(__SCREAMING_SNAKE_CASE  )
     return embeddings
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Optional[int]					,  __lowerCamelCase  :  Tuple = 1024					,  __lowerCamelCase  :  List[Any] = 768					,  __lowerCamelCase  :  str = 768					,  __lowerCamelCase  :  Optional[int] = 10					,  )				->				Union[str, Any]:

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__	=  num_image_text_embeds
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  self.num_image_text_embeds * cross_attention_dim  )
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Any					,  __lowerCamelCase  :  List[Any]  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  text_embeds.shape[0]

     # image
     lowerCamelCase__	=  self.image_embeds(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  image_text_embeds.reshape(__SCREAMING_SNAKE_CASE					,  self.num_image_text_embeds					,  -1  )

     # text
     lowerCamelCase__	=  self.text_proj(__SCREAMING_SNAKE_CASE  )

     return torch.cat([image_text_embeds, text_embeds]					,  dim=1  )
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Tuple					,  __lowerCamelCase  :  Any = 768					,  __lowerCamelCase  :  Optional[Any] = 768					,  __lowerCamelCase  :  Any = 32					,  )				->				Tuple:

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__	=  num_image_text_embeds
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  self.num_image_text_embeds * cross_attention_dim  )
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  Dict  )				->				Optional[int]:

     '''simple docstring'''




     lowerCamelCase__	=  image_embeds.shape[0]

     # image
     lowerCamelCase__	=  self.image_embeds(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  image_embeds.reshape(__SCREAMING_SNAKE_CASE					,  self.num_image_text_embeds					,  -1  )
     lowerCamelCase__	=  self.norm(__SCREAMING_SNAKE_CASE  )
     return image_embeds
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  str					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  str=0.1  )				->				int:

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__	=  Timesteps(num_channels=256					,  flip_sin_to_cos=__SCREAMING_SNAKE_CASE					,  downscale_freq_shift=1  )
     lowerCamelCase__	=  TimestepEmbedding(in_channels=256					,  time_embed_dim=__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  LabelEmbedding(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Optional[int]					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  List[str]					,  __lowerCamelCase  :  Tuple=None  )				->				Dict:

     '''simple docstring'''




     lowerCamelCase__	=  self.time_proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE  )  )  # (N, D)

     lowerCamelCase__	=  self.class_embedder(__SCREAMING_SNAKE_CASE  )  # (N, D)

     lowerCamelCase__	=  timesteps_emb + class_labels  # (N, D)

     return conditioning
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Any					,  __lowerCamelCase  :  Tuple					,  __lowerCamelCase  :  Any = 64  )				->				List[str]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  AttentionPooling(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Tuple					,  __lowerCamelCase  :  Any  )				->				Dict:

     '''simple docstring'''




     lowerCamelCase__	=  self.norma(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.pool(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.norma(__SCREAMING_SNAKE_CASE  )
     return hidden_states
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  str					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  Any  )				->				Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  self.text_proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.text_norm(__SCREAMING_SNAKE_CASE  )

     # image
     lowerCamelCase__	=  self.image_proj(__SCREAMING_SNAKE_CASE  )

     return time_image_embeds + time_text_embeds
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Dict = 768					,  __lowerCamelCase  :  Any = 1536  )				->				int:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )




    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  Union[str, Any]  )				->				Dict:

     '''simple docstring'''




     lowerCamelCase__	=  self.image_proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.image_norm(__SCREAMING_SNAKE_CASE  )
     return time_image_embeds
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  nn.Linear(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )
     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  a__					(       self  :  List[str]					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  Dict  )				->				Any:

     '''simple docstring'''




     lowerCamelCase__	=  self.image_proj(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.image_norm(__SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  self.input_hint_block(__SCREAMING_SNAKE_CASE  )
     return time_image_embeds, hint
class 					lowercase							(				nn.Module   ):


    '''simple docstring'''

    def __init__(       self  :  int					,  __lowerCamelCase  :  List[Any]					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Any=None  )				->				Dict:

     '''simple docstring'''




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




    def  a__					(       self  :  str					,  __lowerCamelCase  :  Tuple  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  x.size()

     def shape(__lowerCamelCase  :  Any  ):
      # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
      lowerCamelCase__	=  x.view(__SCREAMING_SNAKE_CASE					,  -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=__SCREAMING_SNAKE_CASE  ) + 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(__SCREAMING_SNAKE_CASE  )  )
     # (bs*n_heads, length+class_token_length, dim_per_head)
     lowerCamelCase__	=  shape(self.k_proj(__SCREAMING_SNAKE_CASE  )  )
     lowerCamelCase__	=  shape(self.v_proj(__SCREAMING_SNAKE_CASE  )  )

     # (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"					,  __SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )

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

     return a[:, 0, :]  # cls_token





def   a						(				SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		[True for i in range(n + 1							)]
       UpperCamelCase     :						Tuple 						=		[i - 1 for i in range(n + 1							)]
       UpperCamelCase     :						Optional[Any] 						=		[]
       for i in range(2				,      n + 1							):
              if is_prime[i]:
                     primes.append(SCREAMING_SNAKE_CASE_							)
              for j in range(0				,      len(SCREAMING_SNAKE_CASE_							)							):
                     if i * primes[j] >= n:
                            break
                     UpperCamelCase     :						Any 						=		False

                     if i % primes[j] == 0:
                            UpperCamelCase     :						int 						=		totients[i] * primes[j]
                            break

                     UpperCamelCase     :						Dict 						=		totients[i] * (primes[j] - 1)

       return totients


if __name__ == "__main__":
  import doctest

  doctest.testmod()

