


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def 							UpperCamelCase_  (				__snake_case			:						torch.Tensor	,			__snake_case			:						int	,			__snake_case			:						bool = False	,			__snake_case			:						float = 1	,			__snake_case			:						float = 1	,			__snake_case			:						int = 1_00_00	,			)->      int:




      '''simple docstring'''
      assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

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

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

      # scale embeddings
      __magic_name__ 		=			scale * emb

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

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

      # zero pad
      if embedding_dim % 2 == 1:
            __magic_name__ 		=			torch.nn.functional.pad(__snake_case	,			(0, 1, 0, 0)     )
      return emb
def 							UpperCamelCase_  (				__snake_case			:						Dict	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						List[Any]=False	,			__snake_case			:						str=0     )->      Tuple:




      '''simple docstring'''
      __magic_name__ 		=			np.arange(__snake_case	,			dtype=np.floataa     )
      __magic_name__ 		=			np.arange(__snake_case	,			dtype=np.floataa     )
      __magic_name__ 		=			np.meshgrid(__snake_case	,			__snake_case     )  # here w goes first
      __magic_name__ 		=			np.stack(__snake_case	,			axis=0     )

      __magic_name__ 		=			grid.reshape([2, 1, grid_size, grid_size]     )
      __magic_name__ 		=			get_ad_sincos_pos_embed_from_grid(__snake_case	,			__snake_case     )
      if cls_token and extra_tokens > 0:
            __magic_name__ 		=			np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]	,			axis=0     )
      return pos_embed
def 							UpperCamelCase_  (				__snake_case			:						Optional[int]	,			__snake_case			:						Tuple     )->      Dict:




      '''simple docstring'''



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

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

      __magic_name__ 		=			np.concatenate([emb_h, emb_w]	,			axis=1     )  # (H*W, D)
      return emb
def 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						Optional[Any]     )->      int:




      '''simple docstring'''



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

      __magic_name__ 		=			np.arange(embed_dim // 2	,			dtype=np.floataa     )
      omega /= embed_dim / 2.0
      __magic_name__ 		=			1.0 / 1_00_00**omega  # (D/2,)

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

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

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

class     __snake_case							(      nn.Module):
     def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	int=2_2_4  ,			_lowerCAmelCase					:	List[Any]=2_2_4  ,			_lowerCAmelCase					:	Union[str, Any]=1_6  ,			_lowerCAmelCase					:	Optional[int]=3  ,			_lowerCAmelCase					:	Dict=7_6_8  ,			_lowerCAmelCase					:	List[str]=False  ,			_lowerCAmelCase					:	Optional[int]=True  ,			_lowerCAmelCase					:	List[str]=True  ,			)       ->				Optional[Any]:



           '''simple docstring'''





           super().__init__()

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

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

           __magic_name__ 		=			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					:	List[str]  ,			_lowerCAmelCase					:	List[str]     )       ->				Dict:



           '''simple docstring'''





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







class     __snake_case							(      nn.Module):
     def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	int = "silu"  ,			_lowerCAmelCase					:	int = None  ,			_lowerCAmelCase					:	Optional[Any] = None  ,			_lowerCAmelCase					:	Dict=None  ,			)       ->				List[str]:



           '''simple docstring'''





           super().__init__()

           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )

           if cond_proj_dim is not None:
                 __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__  ,			bias=lowerCAmelCase__     )
           else:
                 __magic_name__ 		=			None

           __magic_name__ 		=			get_activation(lowerCAmelCase__     )

           if out_dim is not None:
                 __magic_name__ 		=			out_dim
           else:
                 __magic_name__ 		=			time_embed_dim
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )

           if post_act_fn is None:
                 __magic_name__ 		=			None
           else:
                 __magic_name__ 		=			get_activation(lowerCAmelCase__     )







     def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	List[str]=None     )       ->				int:



           '''simple docstring'''





           if condition is not None:
                 __magic_name__ 		=			sample + self.cond_proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.linear_a(lowerCAmelCase__     )

           if self.act is not None:
                 __magic_name__ 		=			self.act(lowerCAmelCase__     )

           __magic_name__ 		=			self.linear_a(lowerCAmelCase__     )

           if self.post_act is not None:
                 __magic_name__ 		=			self.post_act(lowerCAmelCase__     )
           return sample







class     __snake_case							(      nn.Module):
     def __init__(	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[Any]     )       ->				Union[str, Any]:



           '''simple docstring'''





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







     def   a (	self					:	str  ,			_lowerCAmelCase					:	int     )       ->				Tuple:



           '''simple docstring'''





           __magic_name__ 		=			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     __snake_case							(      nn.Module):
     def __init__(	self					:	Optional[Any]  ,			_lowerCAmelCase					:	Optional[int] = 2_5_6  ,			_lowerCAmelCase					:	int = 1.0  ,			_lowerCAmelCase					:	Any=True  ,			_lowerCAmelCase					:	List[str]=True  ,			_lowerCAmelCase					:	Optional[Any]=False     )       ->				Tuple:



           '''simple docstring'''





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

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

                 __magic_name__ 		=			self.W







     def   a (	self					:	Any  ,			_lowerCAmelCase					:	Optional[Any]     )       ->				Union[str, Any]:



           '''simple docstring'''





           if self.log:
                 __magic_name__ 		=			torch.log(lowerCAmelCase__     )

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

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







class     __snake_case							(      nn.Module):
     def __init__(	self					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Any  ,			)       ->				List[str]:



           '''simple docstring'''





           super().__init__()

           __magic_name__ 		=			height
           __magic_name__ 		=			width
           __magic_name__ 		=			num_embed
           __magic_name__ 		=			embed_dim

           __magic_name__ 		=			nn.Embedding(self.num_embed  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.Embedding(self.height  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.Embedding(self.width  ,			lowerCAmelCase__     )







     def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	str     )       ->				List[Any]:



           '''simple docstring'''





           __magic_name__ 		=			self.emb(lowerCAmelCase__     )

           __magic_name__ 		=			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
           __magic_name__ 		=			height_emb.unsqueeze(2     )

           __magic_name__ 		=			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
           __magic_name__ 		=			width_emb.unsqueeze(1     )

           __magic_name__ 		=			height_emb + width_emb

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

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

           return emb







class     __snake_case							(      nn.Module):
     def __init__(	self					:	str  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Dict     )       ->				Dict:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			dropout_prob > 0
           __magic_name__ 		=			nn.Embedding(num_classes + use_cfg_embedding  ,			lowerCAmelCase__     )
           __magic_name__ 		=			num_classes
           __magic_name__ 		=			dropout_prob
     def   a (	self					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Optional[Any]=None     )       ->				List[str]:



           '''simple docstring'''





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







     def   a (	self					:	Any  ,			_lowerCAmelCase					:	Union[str, Any]  ,			_lowerCAmelCase					:	int=None     )       ->				str:



           '''simple docstring'''





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







class     __snake_case							(      nn.Module):
     def __init__(	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	Any = 1_0_2_4  ,			_lowerCAmelCase					:	Tuple = 7_6_8  ,			_lowerCAmelCase					:	Any = 7_6_8  ,			_lowerCAmelCase					:	List[str] = 1_0  ,			)       ->				Union[str, Any]:



           '''simple docstring'''





           super().__init__()

           __magic_name__ 		=			num_image_text_embeds
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			self.num_image_text_embeds * cross_attention_dim     )
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )







     def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[str]     )       ->				List[str]:



           '''simple docstring'''





           __magic_name__ 		=			text_embeds.shape[0]

           # image
           __magic_name__ 		=			self.image_embeds(lowerCAmelCase__     )
           __magic_name__ 		=			image_text_embeds.reshape(lowerCAmelCase__  ,			self.num_image_text_embeds  ,			-1     )

           # text
           __magic_name__ 		=			self.text_proj(lowerCAmelCase__     )

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







class     __snake_case							(      nn.Module):
     def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	List[Any] = 7_6_8  ,			_lowerCAmelCase					:	int = 7_6_8  ,			_lowerCAmelCase					:	Union[str, Any] = 3_2  ,			)       ->				Optional[int]:



           '''simple docstring'''





           super().__init__()

           __magic_name__ 		=			num_image_text_embeds
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			self.num_image_text_embeds * cross_attention_dim     )
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )







     def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	int     )       ->				Optional[int]:



           '''simple docstring'''





           __magic_name__ 		=			image_embeds.shape[0]

           # image
           __magic_name__ 		=			self.image_embeds(lowerCAmelCase__     )
           __magic_name__ 		=			image_embeds.reshape(lowerCAmelCase__  ,			self.num_image_text_embeds  ,			-1     )
           __magic_name__ 		=			self.norm(lowerCAmelCase__     )
           return image_embeds







class     __snake_case							(      nn.Module):
     def __init__(	self					:	Optional[Any]  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Dict=0.1     )       ->				Any:



           '''simple docstring'''





           super().__init__()

           __magic_name__ 		=			Timesteps(num_channels=2_5_6  ,			flip_sin_to_cos=lowerCAmelCase__  ,			downscale_freq_shift=1     )
           __magic_name__ 		=			TimestepEmbedding(in_channels=2_5_6  ,			time_embed_dim=lowerCAmelCase__     )
           __magic_name__ 		=			LabelEmbedding(lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     )







     def   a (	self					:	Tuple  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	List[str]=None     )       ->				Any:



           '''simple docstring'''





           __magic_name__ 		=			self.time_proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__     )     )  # (N, D)

           __magic_name__ 		=			self.class_embedder(lowerCAmelCase__     )  # (N, D)

           __magic_name__ 		=			timesteps_emb + class_labels  # (N, D)

           return conditioning







class     __snake_case							(      nn.Module):
     def __init__(	self					:	Optional[int]  ,			_lowerCAmelCase					:	Union[str, Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Dict = 6_4     )       ->				Dict:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )
           __magic_name__ 		=			AttentionPooling(lowerCAmelCase__  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )







     def   a (	self					:	Dict  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				Optional[int]:



           '''simple docstring'''





           __magic_name__ 		=			self.norma(lowerCAmelCase__     )
           __magic_name__ 		=			self.pool(lowerCAmelCase__     )
           __magic_name__ 		=			self.proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.norma(lowerCAmelCase__     )
           return hidden_states







class     __snake_case							(      nn.Module):
     def __init__(	self					:	Dict  ,			_lowerCAmelCase					:	int = 7_6_8  ,			_lowerCAmelCase					:	Any = 7_6_8  ,			_lowerCAmelCase					:	Tuple = 1_5_3_6     )       ->				Any:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )







     def   a (	self					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]     )       ->				Optional[Any]:



           '''simple docstring'''





           __magic_name__ 		=			self.text_proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.text_norm(lowerCAmelCase__     )

           # image
           __magic_name__ 		=			self.image_proj(lowerCAmelCase__     )

           return time_image_embeds + time_text_embeds







class     __snake_case							(      nn.Module):
     def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	str = 7_6_8  ,			_lowerCAmelCase					:	int = 1_5_3_6     )       ->				int:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )







     def   a (	self					:	List[Any]  ,			_lowerCAmelCase					:	Optional[int]     )       ->				Any:



           '''simple docstring'''





           __magic_name__ 		=			self.image_proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.image_norm(lowerCAmelCase__     )
           return time_image_embeds







class     __snake_case							(      nn.Module):
     def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	List[Any] = 7_6_8  ,			_lowerCAmelCase					:	List[str] = 1_5_3_6     )       ->				List[str]:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			nn.Linear(lowerCAmelCase__  ,			lowerCAmelCase__     )
           __magic_name__ 		=			nn.LayerNorm(lowerCAmelCase__     )
           __magic_name__ 		=			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					:	List[str]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	List[str]     )       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			self.image_proj(lowerCAmelCase__     )
           __magic_name__ 		=			self.image_norm(lowerCAmelCase__     )
           __magic_name__ 		=			self.input_hint_block(lowerCAmelCase__     )
           return time_image_embeds, hint







class     __snake_case							(      nn.Module):
     def __init__(	self					:	str  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Tuple=None     )       ->				List[str]:



           '''simple docstring'''





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







     def   a (	self					:	str  ,			_lowerCAmelCase					:	Optional[Any]     )       ->				Tuple:



           '''simple docstring'''





           __magic_name__ 		=			x.size()

           def shape(_lowerCAmelCase					:	Optional[int]     ):
                 # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                 __magic_name__ 		=			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)
                 __magic_name__ 		=			x.transpose(1  ,			2     )
                 # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                 __magic_name__ 		=			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)
                 __magic_name__ 		=			x.transpose(1  ,			2     )
                 return x

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

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

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

           # (bs*n_heads, dim_per_head, class_token_length)
           __magic_name__ 		=			torch.einsum('bts,bcs->bct'  ,			lowerCAmelCase__  ,			lowerCAmelCase__     )

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

           return a[:, 0, :]  # cls_token





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase :				str						    =       {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[int]						    =       ['''BartTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Any						    =       [
					    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''BartForCausalLM''',
					    '''BartForConditionalGeneration''',
					    '''BartForQuestionAnswering''',
					    '''BartForSequenceClassification''',
					    '''BartModel''',
					    '''BartPretrainedModel''',
					    '''PretrainedBartModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Tuple						    =       [
					    '''TFBartForConditionalGeneration''',
					    '''TFBartForSequenceClassification''',
					    '''TFBartModel''',
					    '''TFBartPretrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				List[Any]						    =       [
					    '''FlaxBartDecoderPreTrainedModel''',
					    '''FlaxBartForCausalLM''',
					    '''FlaxBartForConditionalGeneration''',
					    '''FlaxBartForQuestionAnswering''',
					    '''FlaxBartForSequenceClassification''',
					    '''FlaxBartModel''',
					    '''FlaxBartPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
					from .tokenization_bart import BartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bart_fast import BartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bart import (
										    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BartForCausalLM,
										    BartForConditionalGeneration,
										    BartForQuestionAnswering,
										    BartForSequenceClassification,
										    BartModel,
										    BartPretrainedModel,
										    PretrainedBartModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bart import (
										    TFBartForConditionalGeneration,
										    TFBartForSequenceClassification,
										    TFBartModel,
										    TFBartPretrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bart import (
										    FlaxBartDecoderPreTrainedModel,
										    FlaxBartForCausalLM,
										    FlaxBartForConditionalGeneration,
										    FlaxBartForQuestionAnswering,
										    FlaxBartForSequenceClassification,
										    FlaxBartModel,
										    FlaxBartPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase :				Optional[int]						    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

