



'''simple docstring'''






import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def 			snake_case_					( _lowerCAmelCase  :						torch.Tensor       ,				_lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						bool = False       ,				_lowerCAmelCase  :						float = 1       ,				_lowerCAmelCase  :						float = 1       ,				_lowerCAmelCase  :						int = 10000       ,				)				->       List[str]:
							assert len(timesteps.shape  ) == 1, "Timesteps should be a 1d-array"

							UpperCAmelCase			:	Any										=		embedding_dim // 2
							UpperCAmelCase			:	int										=		-math.log(_lowerCAmelCase  ) * torch.arange(
							    start=0       ,				end=_lowerCAmelCase       ,				dtype=torch.floataa       ,				device=timesteps.device  )
							UpperCAmelCase			:	str										=		exponent / (half_dim - downscale_freq_shift)

							UpperCAmelCase			:	int										=		torch.exp(_lowerCAmelCase  )
							UpperCAmelCase			:	List[str]										=		timesteps[:, None].float() * emb[None, :]

							# scale embeddings
							UpperCAmelCase			:	Any										=		scale * emb

							# concat sine and cosine embeddings
							UpperCAmelCase			:	str										=		torch.cat([torch.sin(_lowerCAmelCase  ), torch.cos(_lowerCAmelCase  )]       ,				dim=-1  )

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

							# zero pad
							if embedding_dim % 2 == 1:
														UpperCAmelCase			:	int										=		torch.nn.functional.pad(_lowerCAmelCase       ,				(0, 1, 0, 0)  )
							return emb


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Optional[int]       ,				_lowerCAmelCase  :						Tuple=False       ,				_lowerCAmelCase  :						str=0  )				->       str:
							UpperCAmelCase			:	Optional[Any]										=		np.arange(_lowerCAmelCase       ,				dtype=np.floataa  )
							UpperCAmelCase			:	Optional[int]										=		np.arange(_lowerCAmelCase       ,				dtype=np.floataa  )
							UpperCAmelCase			:	Tuple										=		np.meshgrid(_lowerCAmelCase       ,				_lowerCAmelCase  )  # here w goes first
							UpperCAmelCase			:	List[Any]										=		np.stack(_lowerCAmelCase       ,				axis=0  )

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


def 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						Optional[int]  )				->       Any:
							if embed_dim % 2 != 0:
														raise ValueError('''embed_dim must be divisible by 2'''  )

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

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


def 			snake_case_					( _lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						Optional[Any]  )				->       List[str]:
							if embed_dim % 2 != 0:
														raise ValueError('''embed_dim must be divisible by 2'''  )

							UpperCAmelCase			:	Optional[int]										=		np.arange(embed_dim // 2       ,				dtype=np.floataa  )
							omega /= embed_dim / 2.0
							UpperCAmelCase			:	Optional[int]										=		1.0 / 10000**omega  # (D/2,)

							UpperCAmelCase			:	Tuple										=		pos.reshape(-1  )  # (M,)
							UpperCAmelCase			:	Optional[Any]										=		np.einsum('''m,d->md'''       ,				_lowerCAmelCase       ,				_lowerCAmelCase  )  # (M, D/2), outer product

							UpperCAmelCase			:	Tuple										=		np.sin(_lowerCAmelCase  )  # (M, D/2)
							UpperCAmelCase			:	Optional[Any]										=		np.cos(_lowerCAmelCase  )  # (M, D/2)

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

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Optional[Any]		,      __snake_case      :					Dict=224		,      __snake_case      :					Union[str, Any]=224		,      __snake_case      :					Optional[Any]=16		,      __snake_case      :					Union[str, Any]=3		,      __snake_case      :					Union[str, Any]=768		,      __snake_case      :					str=False		,      __snake_case      :					str=True		,      __snake_case      :					Optional[Any]=True		,      )      ->		Tuple:
								super().__init__()

								UpperCAmelCase			:	List[str]										=		(height // patch_size) * (width // patch_size)
								UpperCAmelCase			:	Any										=		flatten
								UpperCAmelCase			:	List[Any]										=		layer_norm

								UpperCAmelCase			:	Dict										=		nn.Convad(
								    __snake_case		,      __snake_case		,      kernel_size=(patch_size, patch_size)		,      stride=__snake_case		,      bias=__snake_case		)
								if layer_norm:
															UpperCAmelCase			:	List[str]										=		nn.LayerNorm(__snake_case		,      elementwise_affine=__snake_case		,      eps=1E-6		)
								else:
															UpperCAmelCase			:	Optional[Any]										=		None

								UpperCAmelCase			:	Optional[int]										=		get_ad_sincos_pos_embed(__snake_case		,      int(num_patches**0.5		)		)
								self.register_buffer('''pos_embed'''		,      torch.from_numpy(__snake_case		).float().unsqueeze(0		)		,      persistent=__snake_case		)







	def 					A     (						self      :					Optional[int]		,      __snake_case      :					int		)      ->		List[str]:
								UpperCAmelCase			:	Dict										=		self.proj(__snake_case		)
								if self.flatten:
															UpperCAmelCase			:	List[str]										=		latent.flatten(2		).transpose(1		,      2		)  # BCHW -> BNC
								if self.layer_norm:
															UpperCAmelCase			:	Any										=		self.norm(__snake_case		)
								return latent + self.pos_embed

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Any		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					str = "silu"		,      __snake_case      :					int = None		,      __snake_case      :					Optional[str] = None		,      __snake_case      :					Any=None		,      )      ->		Any:
								super().__init__()

								UpperCAmelCase			:	Tuple										=		nn.Linear(__snake_case		,      __snake_case		)

								if cond_proj_dim is not None:
															UpperCAmelCase			:	int										=		nn.Linear(__snake_case		,      __snake_case		,      bias=__snake_case		)
								else:
															UpperCAmelCase			:	Any										=		None

								UpperCAmelCase			:	int										=		get_activation(__snake_case		)

								if out_dim is not None:
															UpperCAmelCase			:	List[Any]										=		out_dim
								else:
															UpperCAmelCase			:	Tuple										=		time_embed_dim
								UpperCAmelCase			:	Any										=		nn.Linear(__snake_case		,      __snake_case		)

								if post_act_fn is None:
															UpperCAmelCase			:	str										=		None
								else:
															UpperCAmelCase			:	str										=		get_activation(__snake_case		)







	def 					A     (						self      :					str		,      __snake_case      :					Optional[int]		,      __snake_case      :					Tuple=None		)      ->		Union[str, Any]:
								if condition is not None:
															UpperCAmelCase			:	int										=		sample + self.cond_proj(__snake_case		)
								UpperCAmelCase			:	Optional[int]										=		self.linear_a(__snake_case		)

								if self.act is not None:
															UpperCAmelCase			:	Any										=		self.act(__snake_case		)

								UpperCAmelCase			:	Tuple										=		self.linear_a(__snake_case		)

								if self.post_act is not None:
															UpperCAmelCase			:	List[str]										=		self.post_act(__snake_case		)
								return sample

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					int		,      __snake_case      :					int		,      __snake_case      :					bool		,      __snake_case      :					float		)      ->		Union[str, Any]:
								super().__init__()
								UpperCAmelCase			:	Union[str, Any]										=		num_channels
								UpperCAmelCase			:	List[Any]										=		flip_sin_to_cos
								UpperCAmelCase			:	int										=		downscale_freq_shift







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					Tuple		)      ->		Optional[Any]:
								UpperCAmelCase			:	Optional[Any]										=		get_timestep_embedding(
								    __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     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					int = 256		,      __snake_case      :					float = 1.0		,      __snake_case      :					List[Any]=True		,      __snake_case      :					Any=True		,      __snake_case      :					Dict=False		)      ->		Optional[Any]:
								super().__init__()
								UpperCAmelCase			:	Dict										=		nn.Parameter(torch.randn(__snake_case		) * scale		,      requires_grad=__snake_case		)
								UpperCAmelCase			:	Dict										=		log
								UpperCAmelCase			:	Tuple										=		flip_sin_to_cos

								if set_W_to_weight:
															# to delete later
															UpperCAmelCase			:	Dict										=		nn.Parameter(torch.randn(__snake_case		) * scale		,      requires_grad=__snake_case		)

															UpperCAmelCase			:	str										=		self.W







	def 					A     (						self      :					List[str]		,      __snake_case      :					Optional[int]		)      ->		Tuple:
								if self.log:
															UpperCAmelCase			:	Any										=		torch.log(__snake_case		)

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

								if self.flip_sin_to_cos:
															UpperCAmelCase			:	Dict										=		torch.cat([torch.cos(__snake_case		), torch.sin(__snake_case		)]		,      dim=-1		)
								else:
															UpperCAmelCase			:	Optional[Any]										=		torch.cat([torch.sin(__snake_case		), torch.cos(__snake_case		)]		,      dim=-1		)
								return out

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Any		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int		,      )      ->		Union[str, Any]:
								super().__init__()

								UpperCAmelCase			:	Optional[Any]										=		height
								UpperCAmelCase			:	Any										=		width
								UpperCAmelCase			:	Optional[Any]										=		num_embed
								UpperCAmelCase			:	Tuple										=		embed_dim

								UpperCAmelCase			:	List[Any]										=		nn.Embedding(self.num_embed		,      __snake_case		)
								UpperCAmelCase			:	List[Any]										=		nn.Embedding(self.height		,      __snake_case		)
								UpperCAmelCase			:	Dict										=		nn.Embedding(self.width		,      __snake_case		)







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					Union[str, Any]		)      ->		Tuple:
								UpperCAmelCase			:	Optional[Any]										=		self.emb(__snake_case		)

								UpperCAmelCase			:	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
								UpperCAmelCase			:	str										=		height_emb.unsqueeze(2		)

								UpperCAmelCase			:	List[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
								UpperCAmelCase			:	Optional[int]										=		width_emb.unsqueeze(1		)

								UpperCAmelCase			:	Union[str, Any]										=		height_emb + width_emb

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

								UpperCAmelCase			:	int										=		emb + pos_emb[:, : emb.shape[1], :]

								return emb

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					List[Any]		,      __snake_case      :					Optional[int]		,      __snake_case      :					Optional[int]		,      __snake_case      :					Dict		)      ->		List[Any]:
								super().__init__()
								UpperCAmelCase			:	List[str]										=		dropout_prob > 0
								UpperCAmelCase			:	List[Any]										=		nn.Embedding(num_classes + use_cfg_embedding		,      __snake_case		)
								UpperCAmelCase			:	int										=		num_classes
								UpperCAmelCase			:	Optional[int]										=		dropout_prob







	def 					A     (						self      :					List[str]		,      __snake_case      :					Optional[Any]		,      __snake_case      :					Dict=None		)      ->		List[Any]:
								if force_drop_ids is None:
															UpperCAmelCase			:	str										=		torch.rand(labels.shape[0]		,      device=labels.device		) < self.dropout_prob
								else:
															UpperCAmelCase			:	Optional[int]										=		torch.tensor(force_drop_ids == 1		)
								UpperCAmelCase			:	Tuple										=		torch.where(__snake_case		,      self.num_classes		,      __snake_case		)
								return labels







	def 					A     (						self      :					Any		,      __snake_case      :					torch.LongTensor		,      __snake_case      :					str=None		)      ->		Tuple:
								UpperCAmelCase			:	str										=		self.dropout_prob > 0
								if (self.training and use_dropout) or (force_drop_ids is not None):
															UpperCAmelCase			:	Any										=		self.token_drop(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	List[str]										=		self.embedding_table(__snake_case		)
								return embeddings

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Dict		,      __snake_case      :					int = 1024		,      __snake_case      :					int = 768		,      __snake_case      :					int = 768		,      __snake_case      :					int = 10		,      )      ->		List[str]:
								super().__init__()

								UpperCAmelCase			:	Any										=		num_image_text_embeds
								UpperCAmelCase			:	Tuple										=		nn.Linear(__snake_case		,      self.num_image_text_embeds * cross_attention_dim		)
								UpperCAmelCase			:	Any										=		nn.Linear(__snake_case		,      __snake_case		)







	def 					A     (						self      :					List[Any]		,      __snake_case      :					torch.FloatTensor		,      __snake_case      :					torch.FloatTensor		)      ->		Optional[int]:
								UpperCAmelCase			:	int										=		text_embeds.shape[0]

								# image
								UpperCAmelCase			:	List[Any]										=		self.image_embeds(__snake_case		)
								UpperCAmelCase			:	int										=		image_text_embeds.reshape(__snake_case		,      self.num_image_text_embeds		,      -1		)

								# text
								UpperCAmelCase			:	List[str]										=		self.text_proj(__snake_case		)

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

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					int = 768		,      __snake_case      :					int = 768		,      __snake_case      :					int = 32		,      )      ->		Dict:
								super().__init__()

								UpperCAmelCase			:	Any										=		num_image_text_embeds
								UpperCAmelCase			:	Union[str, Any]										=		nn.Linear(__snake_case		,      self.num_image_text_embeds * cross_attention_dim		)
								UpperCAmelCase			:	str										=		nn.LayerNorm(__snake_case		)







	def 					A     (						self      :					int		,      __snake_case      :					torch.FloatTensor		)      ->		Union[str, Any]:
								UpperCAmelCase			:	Tuple										=		image_embeds.shape[0]

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

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Dict		,      __snake_case      :					List[str]		,      __snake_case      :					Optional[Any]		,      __snake_case      :					int=0.1		)      ->		Any:
								super().__init__()

								UpperCAmelCase			:	Union[str, Any]										=		Timesteps(num_channels=256		,      flip_sin_to_cos=__snake_case		,      downscale_freq_shift=1		)
								UpperCAmelCase			:	Optional[int]										=		TimestepEmbedding(in_channels=256		,      time_embed_dim=__snake_case		)
								UpperCAmelCase			:	Tuple										=		LabelEmbedding(__snake_case		,      __snake_case		,      __snake_case		)







	def 					A     (						self      :					Dict		,      __snake_case      :					Tuple		,      __snake_case      :					Tuple		,      __snake_case      :					Any=None		)      ->		Tuple:
								UpperCAmelCase			:	Optional[Any]										=		self.time_proj(__snake_case		)
								UpperCAmelCase			:	Any										=		self.timestep_embedder(timesteps_proj.to(dtype=__snake_case		)		)  # (N, D)

								UpperCAmelCase			:	Tuple										=		self.class_embedder(__snake_case		)  # (N, D)

								UpperCAmelCase			:	str										=		timesteps_emb + class_labels  # (N, D)

								return conditioning

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int = 64		)      ->		Tuple:
								super().__init__()
								UpperCAmelCase			:	Tuple										=		nn.LayerNorm(__snake_case		)
								UpperCAmelCase			:	Optional[int]										=		AttentionPooling(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	Tuple										=		nn.Linear(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	Tuple										=		nn.LayerNorm(__snake_case		)







	def 					A     (						self      :					Dict		,      __snake_case      :					int		)      ->		List[Any]:
								UpperCAmelCase			:	Optional[int]										=		self.norma(__snake_case		)
								UpperCAmelCase			:	List[Any]										=		self.pool(__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		self.proj(__snake_case		)
								UpperCAmelCase			:	List[str]										=		self.norma(__snake_case		)
								return hidden_states

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					int		,      __snake_case      :					int = 768		,      __snake_case      :					int = 768		,      __snake_case      :					int = 1536		)      ->		Union[str, Any]:
								super().__init__()
								UpperCAmelCase			:	int										=		nn.Linear(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	str										=		nn.LayerNorm(__snake_case		)
								UpperCAmelCase			:	str										=		nn.Linear(__snake_case		,      __snake_case		)







	def 					A     (						self      :					List[str]		,      __snake_case      :					torch.FloatTensor		,      __snake_case      :					torch.FloatTensor		)      ->		Union[str, Any]:
								# text
								UpperCAmelCase			:	Optional[int]										=		self.text_proj(__snake_case		)
								UpperCAmelCase			:	Dict										=		self.text_norm(__snake_case		)

								# image
								UpperCAmelCase			:	List[Any]										=		self.image_proj(__snake_case		)

								return time_image_embeds + time_text_embeds

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					int		,      __snake_case      :					int = 768		,      __snake_case      :					int = 1536		)      ->		Optional[int]:
								super().__init__()
								UpperCAmelCase			:	Any										=		nn.Linear(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		nn.LayerNorm(__snake_case		)







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					torch.FloatTensor		)      ->		Union[str, Any]:
								# image
								UpperCAmelCase			:	List[str]										=		self.image_proj(__snake_case		)
								UpperCAmelCase			:	List[Any]										=		self.image_norm(__snake_case		)
								return time_image_embeds

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					str		,      __snake_case      :					int = 768		,      __snake_case      :					int = 1536		)      ->		Tuple:
								super().__init__()
								UpperCAmelCase			:	Dict										=		nn.Linear(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		nn.LayerNorm(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		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      :					Optional[Any]		,      __snake_case      :					torch.FloatTensor		,      __snake_case      :					torch.FloatTensor		)      ->		int:
								# image
								UpperCAmelCase			:	Optional[Any]										=		self.image_proj(__snake_case		)
								UpperCAmelCase			:	str										=		self.image_norm(__snake_case		)
								UpperCAmelCase			:	List[Any]										=		self.input_hint_block(__snake_case		)
								return time_image_embeds, hint

class     SCREAMING_SNAKE_CASE(							nn.Module      ):







	"""simple docstring"""






	def __init__(						self      :					str		,      __snake_case      :					Any		,      __snake_case      :					Optional[int]		,      __snake_case      :					List[Any]=None		)      ->		Any:
								super().__init__()
								UpperCAmelCase			:	Tuple										=		dtype
								UpperCAmelCase			:	Tuple										=		nn.Parameter(torch.randn(1		,      __snake_case		) / embed_dim**0.5		)
								UpperCAmelCase			:	List[str]										=		nn.Linear(__snake_case		,      __snake_case		,      dtype=self.dtype		)
								UpperCAmelCase			:	int										=		nn.Linear(__snake_case		,      __snake_case		,      dtype=self.dtype		)
								UpperCAmelCase			:	str										=		nn.Linear(__snake_case		,      __snake_case		,      dtype=self.dtype		)
								UpperCAmelCase			:	str										=		num_heads
								UpperCAmelCase			:	List[Any]										=		embed_dim // self.num_heads







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					List[Any]		)      ->		Optional[int]:
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		x.size()

								def shape(__snake_case      :					Optional[Any]		):
															# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
															UpperCAmelCase			:	str										=		x.view(__snake_case		,      -1		,      self.num_heads		,      self.dim_per_head		)
															# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
															UpperCAmelCase			:	Union[str, Any]										=		x.transpose(1		,      2		)
															# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
															UpperCAmelCase			:	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)
															UpperCAmelCase			:	int										=		x.transpose(1		,      2		)
															return x

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

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

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

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

								# (bs, length+1, width)
								UpperCAmelCase			:	List[str]										=		a.reshape(__snake_case		,      -1		,      1		).transpose(1		,      2		)

								return a[:, 0, :]  # cls_token










'''simple docstring'''






import re


def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       Dict:
							def docstring_decorator(_lowerCAmelCase  :						List[str]  ):
														UpperCAmelCase			:	str										=		fn.__doc__
														UpperCAmelCase			:	List[str]										=		func_doc.split('''\n'''  )
														UpperCAmelCase			:	Optional[Any]										=		0
														while i < len(_lowerCAmelCase  ) and re.search(R'''^\s*Examples?:\s*$'''       ,				lines[i]  ) is None:
																					i += 1
														if i < len(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Union[str, Any]										=		example_docstring
																					UpperCAmelCase			:	Tuple										=		'''\n'''.join(_lowerCAmelCase  )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""  )
														UpperCAmelCase			:	Any										=		func_doc
														return fn

							return docstring_decorator





