





import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_ = False       ,		UpperCamelCase_ = 1       ,		UpperCamelCase_ = 1       ,		UpperCamelCase_ = 10000       ,		)  ->		Optional[Any]:
       assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

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

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

       # scale embeddings
       UpperCamelCase_ 							=	scale * emb

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

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

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=0	)  ->		List[Any]:
       UpperCamelCase_ 							=	np.arange(UpperCamelCase_       ,		dtype=np.floataa	)
       UpperCamelCase_ 							=	np.arange(UpperCamelCase_       ,		dtype=np.floataa	)
       UpperCamelCase_ 							=	np.meshgrid(UpperCamelCase_       ,		UpperCamelCase_	)  # here w goes first
       UpperCamelCase_ 							=	np.stack(UpperCamelCase_       ,		axis=0	)

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		str:
       if embed_dim % 2 != 0:
              raise ValueError("embed_dim must be divisible by 2"	)

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

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[int]:
       if embed_dim % 2 != 0:
              raise ValueError("embed_dim must be divisible by 2"	)

       UpperCamelCase_ 							=	np.arange(embed_dim // 2       ,		dtype=np.floataa	)
       omega /= embed_dim / 2.0
       UpperCamelCase_ 							=	1.0 / 10000**omega  # (D/2,)

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

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

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



class 				_UpperCamelCase       (						nn.Module  ):

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




              """simple docstring"""


              super().__init__()

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

              UpperCamelCase_ 							=	nn.Convad(
                  __UpperCamelCase       , __UpperCamelCase       , kernel_size=(patch_size, patch_size)       , stride=__UpperCamelCase       , bias=__UpperCamelCase		)
              if layer_norm:
                     UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase       , elementwise_affine=__UpperCamelCase       , eps=1e-6		)
              else:
                     UpperCamelCase_ 							=	None

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



       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			Dict		)							->			List[Any]:




              """simple docstring"""


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



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str = "silu"       , _SCREAMING_SNAKE_CASE:			int = None       , _SCREAMING_SNAKE_CASE:			Optional[str] = None       , _SCREAMING_SNAKE_CASE:			List[str]=None       , )							->			Tuple:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)

              if cond_proj_dim is not None:
                     UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase       , bias=__UpperCamelCase		)
              else:
                     UpperCamelCase_ 							=	None

              UpperCamelCase_ 							=	get_activation(__UpperCamelCase		)

              if out_dim is not None:
                     UpperCamelCase_ 							=	out_dim
              else:
                     UpperCamelCase_ 							=	time_embed_dim
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)

              if post_act_fn is None:
                     UpperCamelCase_ 							=	None
              else:
                     UpperCamelCase_ 							=	get_activation(__UpperCamelCase		)



       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			List[str]=None		)							->			int:




              """simple docstring"""


              if condition is not None:
                     UpperCamelCase_ 							=	sample + self.cond_proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.linear_a(__UpperCamelCase		)

              if self.act is not None:
                     UpperCamelCase_ 							=	self.act(__UpperCamelCase		)

              UpperCamelCase_ 							=	self.linear_a(__UpperCamelCase		)

              if self.post_act is not None:
                     UpperCamelCase_ 							=	self.post_act(__UpperCamelCase		)
              return sample



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			float		)							->			List[Any]:




              """simple docstring"""


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



       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	get_timestep_embedding(
                  __UpperCamelCase       , self.num_channels       , flip_sin_to_cos=self.flip_sin_to_cos       , downscale_freq_shift=self.downscale_freq_shift       , )
              return t_emb



class 				_UpperCamelCase       (						nn.Module  ):

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




              """simple docstring"""


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

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

                     UpperCamelCase_ 							=	self.W



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Any		)							->			Dict:




              """simple docstring"""


              if self.log:
                     UpperCamelCase_ 							=	torch.log(__UpperCamelCase		)

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

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



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int       , )							->			Optional[int]:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	height
              UpperCamelCase_ 							=	width
              UpperCamelCase_ 							=	num_embed
              UpperCamelCase_ 							=	embed_dim

              UpperCamelCase_ 							=	nn.Embedding(self.num_embed       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.Embedding(self.height       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.Embedding(self.width       , __UpperCamelCase		)



       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.emb(__UpperCamelCase		)

              UpperCamelCase_ 							=	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_ 							=	height_emb.unsqueeze(2		)

              UpperCamelCase_ 							=	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_ 							=	width_emb.unsqueeze(1		)

              UpperCamelCase_ 							=	height_emb + width_emb

              # 1 x H x W x D -> 1 x L xD
              UpperCamelCase_ 							=	pos_emb.view(1       , self.height * self.width       , -1		)

              UpperCamelCase_ 							=	emb + pos_emb[:, : emb.shape[1], :]

              return emb



class 				_UpperCamelCase       (						nn.Module  ):

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




              """simple docstring"""


              super().__init__()
              UpperCamelCase_ 							=	dropout_prob > 0
              UpperCamelCase_ 							=	nn.Embedding(num_classes + use_cfg_embedding       , __UpperCamelCase		)
              UpperCamelCase_ 							=	num_classes
              UpperCamelCase_ 							=	dropout_prob

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None		)							->			Any:




              """simple docstring"""


              if force_drop_ids is None:
                     UpperCamelCase_ 							=	torch.rand(labels.shape[0]       , device=labels.device		) < self.dropout_prob
              else:
                     UpperCamelCase_ 							=	torch.tensor(force_drop_ids == 1		)
              UpperCamelCase_ 							=	torch.where(__UpperCamelCase       , self.num_classes       , __UpperCamelCase		)
              return labels



       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			torch.LongTensor       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.dropout_prob > 0
              if (self.training and use_dropout) or (force_drop_ids is not None):
                     UpperCamelCase_ 							=	self.token_drop(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	self.embedding_table(__UpperCamelCase		)
              return embeddings



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int = 1024       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 10       , )							->			Optional[int]:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	num_image_text_embeds
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , self.num_image_text_embeds * cross_attention_dim		)
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	text_embeds.shape[0]

              # image
              UpperCamelCase_ 							=	self.image_embeds(__UpperCamelCase		)
              UpperCamelCase_ 							=	image_text_embeds.reshape(__UpperCamelCase       , self.num_image_text_embeds       , -1		)

              # text
              UpperCamelCase_ 							=	self.text_proj(__UpperCamelCase		)

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



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 32       , )							->			Optional[int]:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	num_image_text_embeds
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , self.num_image_text_embeds * cross_attention_dim		)
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)



       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	image_embeds.shape[0]

              # image
              UpperCamelCase_ 							=	self.image_embeds(__UpperCamelCase		)
              UpperCamelCase_ 							=	image_embeds.reshape(__UpperCamelCase       , self.num_image_text_embeds       , -1		)
              UpperCamelCase_ 							=	self.norm(__UpperCamelCase		)
              return image_embeds



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1		)							->			int:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	Timesteps(num_channels=256       , flip_sin_to_cos=__UpperCamelCase       , downscale_freq_shift=1		)
              UpperCamelCase_ 							=	TimestepEmbedding(in_channels=256       , time_embed_dim=__UpperCamelCase		)
              UpperCamelCase_ 							=	LabelEmbedding(__UpperCamelCase       , __UpperCamelCase       , __UpperCamelCase		)



       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			int=None		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.time_proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.timestep_embedder(timesteps_proj.to(dtype=__UpperCamelCase		)		)  # (N, D)

              UpperCamelCase_ 							=	self.class_embedder(__UpperCamelCase		)  # (N, D)

              UpperCamelCase_ 							=	timesteps_emb + class_labels  # (N, D)

              return conditioning



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int = 64		)							->			Tuple:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)
              UpperCamelCase_ 							=	AttentionPooling(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)



       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			int		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	self.norma(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.pool(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.norma(__UpperCamelCase		)
              return hidden_states



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 1536		)							->			Dict:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)



       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.text_proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.text_norm(__UpperCamelCase		)

              # image
              UpperCamelCase_ 							=	self.image_proj(__UpperCamelCase		)

              return time_image_embeds + time_text_embeds



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 1536		)							->			List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)



       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.image_norm(__UpperCamelCase		)
              return time_image_embeds



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int = 768       , _SCREAMING_SNAKE_CASE:			int = 1536		)							->			str:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_ 							=	nn.Linear(__UpperCamelCase       , __UpperCamelCase		)
              UpperCamelCase_ 							=	nn.LayerNorm(__UpperCamelCase		)
              UpperCamelCase_ 							=	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 lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_proj(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.image_norm(__UpperCamelCase		)
              UpperCamelCase_ 							=	self.input_hint_block(__UpperCamelCase		)
              return time_image_embeds, hint



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None		)							->			Tuple:




              """simple docstring"""


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



       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	x.size()

              def shape(_SCREAMING_SNAKE_CASE:			int		):
                     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                     UpperCamelCase_ 							=	x.view(__UpperCamelCase       , -1       , self.num_heads       , self.dim_per_head		)
                     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
                     UpperCamelCase_ 							=	x.transpose(1       , 2		)
                     # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                     UpperCamelCase_ 							=	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_ 							=	x.transpose(1       , 2		)
                     return x

              UpperCamelCase_ 							=	x.mean(dim=1       , keepdim=__UpperCamelCase		) + self.positional_embedding.to(x.dtype		)
              UpperCamelCase_ 							=	torch.cat([class_token, x]       , dim=1		)  # (bs, length+1, width)

              # (bs*n_heads, class_token_length, dim_per_head)
              UpperCamelCase_ 							=	shape(self.q_proj(__UpperCamelCase		)		)
              # (bs*n_heads, length+class_token_length, dim_per_head)
              UpperCamelCase_ 							=	shape(self.k_proj(__UpperCamelCase		)		)
              UpperCamelCase_ 							=	shape(self.v_proj(__UpperCamelCase		)		)

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

              # (bs*n_heads, dim_per_head, class_token_length)
              UpperCamelCase_ 							=	torch.einsum("bts,bcs->bct"       , __UpperCamelCase       , __UpperCamelCase		)

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

              return a[:, 0, :]  # cls_token








"""simple docstring"""
from functools import lru_cache






@lru_cache
def    A__							(     UpperCamelCase			):
							if not isinstance(UpperCamelCase   ,    UpperCamelCase			):
														raise TypeError("k must be an integer."			)
							if k < 0:
														raise ValueError("k integer must be greater or equal to zero."			)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1			) + fibonacci(k - 2			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    len(UpperCamelCase			)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							A						=    0
							while True:
														if fibonacci(UpperCamelCase			) >= len_list:
																					A						=    i
																					break
														i += 1
							A						=    0
							while fibb_k > 0:
														A						=    min(
														    offset + fibonacci(fibb_k - 1			)   ,    len_list - 1			)  # Prevent out of range
														A						=    arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1			)
																					fibb_k -= 2
							else:
														return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


