
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation





def 			_lowercase    (      __UpperCamelCase     :		torch.Tensor   ,       __UpperCamelCase     :		int   ,       __UpperCamelCase     :		bool = False   ,       __UpperCamelCase     :		float = 1   ,       __UpperCamelCase     :		float = 1   ,       __UpperCamelCase     :		int = 1_0000   ,       ):
      assert len(timesteps.shape ) == 1, "Timesteps should be a 1d-array"

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

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

      # scale embeddings
      snake_case__					=   scale * emb

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

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

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





def 			_lowercase    (      __UpperCamelCase     :		Dict   ,       __UpperCamelCase     :		List[str]   ,       __UpperCamelCase     :		Tuple=False   ,       __UpperCamelCase     :		Optional[Any]=0 ):
      snake_case__					=   np.arange(__UpperCamelCase   ,       dtype=np.floataa )
      snake_case__					=   np.arange(__UpperCamelCase   ,       dtype=np.floataa )
      snake_case__					=   np.meshgrid(__UpperCamelCase   ,       __UpperCamelCase )  # here w goes first
      snake_case__					=   np.stack(__UpperCamelCase   ,       axis=0 )

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





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		Union[str, Any] ):
      if embed_dim % 2 != 0:
            raise ValueError("""embed_dim must be divisible by 2""" )

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

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





def 			_lowercase    (      __UpperCamelCase     :		str   ,       __UpperCamelCase     :		Optional[Any] ):
      if embed_dim % 2 != 0:
            raise ValueError("""embed_dim must be divisible by 2""" )

      snake_case__					=   np.arange(embed_dim // 2   ,       dtype=np.floataa )
      omega /= embed_dim / 2.0
      snake_case__					=   1.0 / 1_0000**omega  # (D/2,)

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

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

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





       '''simple docstring'''




       def __init__(						self       :						List[Any]    ,		lowerCAmelCase__       :						Any=224    ,		lowerCAmelCase__       :						Union[str, Any]=224    ,		lowerCAmelCase__       :						str=16    ,		lowerCAmelCase__       :						str=3    ,		lowerCAmelCase__       :						int=768    ,		lowerCAmelCase__       :						Any=False    ,		lowerCAmelCase__       :						List[Any]=True    ,		lowerCAmelCase__       :						List[str]=True    ,		)			->    str:
             super().__init__()

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

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

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




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

             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)

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

             snake_case__					=   get_activation(lowerCAmelCase__							)

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

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

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						Optional[Any]=None							)			->    Dict:
             if condition is not None:
                   snake_case__					=   sample + self.cond_proj(lowerCAmelCase__							)
             snake_case__					=   self.linear_a(lowerCAmelCase__							)

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

             snake_case__					=   self.linear_a(lowerCAmelCase__							)

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						str    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						bool    ,		lowerCAmelCase__       :						float							)			->    Any:
             super().__init__()
             snake_case__					=   num_channels
             snake_case__					=   flip_sin_to_cos
             snake_case__					=   downscale_freq_shift

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						str							)			->    List[Any]:
             snake_case__					=   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     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




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

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

                   snake_case__					=   self.W

       def UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						Optional[int]							)			->    Any:
             if self.log:
                   snake_case__					=   torch.log(lowerCAmelCase__							)

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

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						Any    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int    ,		)			->    Dict:
             super().__init__()

             snake_case__					=   height
             snake_case__					=   width
             snake_case__					=   num_embed
             snake_case__					=   embed_dim

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

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		lowerCAmelCase__       :						Any							)			->    Union[str, Any]:
             snake_case__					=   self.emb(lowerCAmelCase__							)

             snake_case__					=   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
             snake_case__					=   height_emb.unsqueeze(2							)

             snake_case__					=   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
             snake_case__					=   width_emb.unsqueeze(1							)

             snake_case__					=   height_emb + width_emb

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

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

             return emb



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




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

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						Union[str, Any]    ,		lowerCAmelCase__       :						Optional[int]=None							)			->    List[str]:
             if force_drop_ids is None:
                   snake_case__					=   torch.rand(labels.shape[0]    ,		device=labels.device							) < self.dropout_prob
             else:
                   snake_case__					=   torch.tensor(force_drop_ids == 1							)
             snake_case__					=   torch.where(lowerCAmelCase__    ,		self.num_classes    ,		lowerCAmelCase__							)
             return labels

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						Optional[int]    ,		lowerCAmelCase__       :						int = 1024    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 10    ,		)			->    Optional[Any]:
             super().__init__()

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

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						torch.FloatTensor    ,		lowerCAmelCase__       :						torch.FloatTensor							)			->    Tuple:
             snake_case__					=   text_embeds.shape[0]

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

             # text
             snake_case__					=   self.text_proj(lowerCAmelCase__							)

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						Dict    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 32    ,		)			->    Tuple:
             super().__init__()

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

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						torch.FloatTensor							)			->    Optional[int]:
             snake_case__					=   image_embeds.shape[0]

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



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




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

             snake_case__					=   Timesteps(num_channels=256    ,		flip_sin_to_cos=lowerCAmelCase__    ,		downscale_freq_shift=1							)
             snake_case__					=   TimestepEmbedding(in_channels=256    ,		time_embed_dim=lowerCAmelCase__							)
             snake_case__					=   LabelEmbedding(lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						Dict    ,		lowerCAmelCase__       :						Optional[Any]    ,		lowerCAmelCase__       :						Any=None							)			->    Optional[Any]:
             snake_case__					=   self.time_proj(lowerCAmelCase__							)
             snake_case__					=   self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__							)							)  # (N, D)

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

             snake_case__					=   timesteps_emb + class_labels  # (N, D)

             return conditioning



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						Tuple    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int = 64							)			->    Any:
             super().__init__()
             snake_case__					=   nn.LayerNorm(lowerCAmelCase__							)
             snake_case__					=   AttentionPooling(lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   nn.LayerNorm(lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						Dict							)			->    Any:
             snake_case__					=   self.norma(lowerCAmelCase__							)
             snake_case__					=   self.pool(lowerCAmelCase__							)
             snake_case__					=   self.proj(lowerCAmelCase__							)
             snake_case__					=   self.norma(lowerCAmelCase__							)
             return hidden_states



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						Any    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 1536							)			->    Any:
             super().__init__()
             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   nn.LayerNorm(lowerCAmelCase__							)
             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Dict    ,		lowerCAmelCase__       :						torch.FloatTensor    ,		lowerCAmelCase__       :						torch.FloatTensor							)			->    int:
             # text
             snake_case__					=   self.text_proj(lowerCAmelCase__							)
             snake_case__					=   self.text_norm(lowerCAmelCase__							)

             # image
             snake_case__					=   self.image_proj(lowerCAmelCase__							)

             return time_image_embeds + time_text_embeds



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						List[str]    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 1536							)			->    Any:
             super().__init__()
             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   nn.LayerNorm(lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Dict    ,		lowerCAmelCase__       :						torch.FloatTensor							)			->    Union[str, Any]:
             # image
             snake_case__					=   self.image_proj(lowerCAmelCase__							)
             snake_case__					=   self.image_norm(lowerCAmelCase__							)
             return time_image_embeds



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




       def __init__(						self       :						str    ,		lowerCAmelCase__       :						int = 768    ,		lowerCAmelCase__       :						int = 1536							)			->    Any:
             super().__init__()
             snake_case__					=   nn.Linear(lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   nn.LayerNorm(lowerCAmelCase__							)
             snake_case__					=   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 UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						torch.FloatTensor    ,		lowerCAmelCase__       :						torch.FloatTensor							)			->    Optional[int]:
             # image
             snake_case__					=   self.image_proj(lowerCAmelCase__							)
             snake_case__					=   self.image_norm(lowerCAmelCase__							)
             snake_case__					=   self.input_hint_block(lowerCAmelCase__							)
             return time_image_embeds, hint



class     SCREAMING_SNAKE_CASE__     (						nn.Module		):





       '''simple docstring'''




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

       def UpperCAmelCase_						(						self       :						Dict    ,		lowerCAmelCase__       :						Any							)			->    Any:
             snake_case__	,			snake_case__	,			snake_case__					=   x.size()

             def shape(lowerCAmelCase__       :						Union[str, Any]							):
                   # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                   snake_case__					=   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)
                   snake_case__					=   x.transpose(1    ,		2							)
                   # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                   snake_case__					=   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)
                   snake_case__					=   x.transpose(1    ,		2							)
                   return x

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

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

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

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

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

             return a[:, 0, :]  # cls_token









from doctest import testmod
from math import sqrt





def 			_lowercase    (      __UpperCamelCase     :		int ):
      snake_case__					=   []
      if num < 1:
            return facs
      facs.append(1 )
      if num == 1:
            return facs
      facs.append(__UpperCamelCase )
      for i in range(2   ,       int(sqrt(__UpperCamelCase ) ) + 1 ):
            if num % i == 0:  # If i is a factor of num
                  facs.append(__UpperCamelCase )
                  snake_case__					=   num // i  # num//i is the other factor of num
                  if d != i:  # If d and i are distinct
                        facs.append(__UpperCamelCase )  # we have found another factor
      facs.sort()
      return facs


if __name__ == "__main__":
       testmod(name='''factors_of_a_number''', verbose=True)






