'''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       :     Dict      ,  lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     Any = False      ,  lowerCAmelCase       :     List[str] = 1      ,  lowerCAmelCase       :     Tuple = 1      ,  lowerCAmelCase       :     Tuple = 1_0000      ,  ):
 assert len(timesteps.shape			) == 1, "Timesteps should be a 1d-array"

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

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

 # scale embeddings
 __UpperCAmelCase         =					scale * emb

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



def 						__snake_case (       lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     List[str]=False      ,  lowerCAmelCase       :     Optional[Any]=0			):
 __UpperCAmelCase         =					np.arange(lowerCAmelCase      ,  dtype=np.floataa			)
 __UpperCAmelCase         =					np.arange(lowerCAmelCase      ,  dtype=np.floataa			)
 __UpperCAmelCase         =					np.meshgrid(lowerCAmelCase      ,  lowerCAmelCase			)  # here w goes first
 __UpperCAmelCase         =					np.stack(lowerCAmelCase      ,  axis=0			)

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



def 						__snake_case (       lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     Any			):


 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 						__snake_case (       lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     Tuple			):


 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 / 1_0000**omega  # (D/2,)

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

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

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

class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    str			,a:    int=224			,a:    Dict=224			,a:    List[Any]=16			,a:    str=3			,a:    List[str]=768			,a:    Tuple=False			,a:    Optional[int]=True			,a:    str=True			,):
    super().__init__()

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

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

    __UpperCAmelCase         =					get_ad_sincos_pos_embed(a			,int(num_patches**0.5	)	)
    self.register_buffer('pos_embed'			,torch.from_numpy(a	).float().unsqueeze(0	)			,persistent=a	)





   def 			snake_case	(							self:    Optional[int]			,a:    str	):
    __UpperCAmelCase         =					self.proj(a	)
    if self.flatten:
     __UpperCAmelCase         =					latent.flatten(2	).transpose(1			,2	)  # BCHW -> BNC
    if self.layer_norm:
     __UpperCAmelCase         =					self.norm(a	)
    return latent + self.pos_embed




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Union[str, Any]			,a:    int			,a:    int			,a:    str = "silu"			,a:    int = None			,a:    Optional[str] = None			,a:    int=None			,):
    super().__init__()

    __UpperCAmelCase         =					nn.Linear(a			,a	)

    if cond_proj_dim is not None:
     __UpperCAmelCase         =					nn.Linear(a			,a			,bias=a	)
    else:
     __UpperCAmelCase         =					None

    __UpperCAmelCase         =					get_activation(a	)

    if out_dim is not None:
     __UpperCAmelCase         =					out_dim
    else:
     __UpperCAmelCase         =					time_embed_dim
    __UpperCAmelCase         =					nn.Linear(a			,a	)

    if post_act_fn is None:
     __UpperCAmelCase         =					None
    else:
     __UpperCAmelCase         =					get_activation(a	)





   def 			snake_case	(							self:    Optional[int]			,a:    Tuple			,a:    Optional[Any]=None	):






    if condition is not None:
     __UpperCAmelCase         =					sample + self.cond_proj(a	)
    __UpperCAmelCase         =					self.linear_a(a	)

    if self.act is not None:
     __UpperCAmelCase         =					self.act(a	)

    __UpperCAmelCase         =					self.linear_a(a	)

    if self.post_act is not None:
     __UpperCAmelCase         =					self.post_act(a	)
    return sample




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    int			,a:    int			,a:    bool			,a:    float	):
    super().__init__()
    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					flip_sin_to_cos
    __UpperCAmelCase         =					downscale_freq_shift





   def 			snake_case	(							self:    str			,a:    str	):
    __UpperCAmelCase         =					get_timestep_embedding(
        a			,self.num_channels			,flip_sin_to_cos=self.flip_sin_to_cos			,downscale_freq_shift=self.downscale_freq_shift			,)
    return t_emb




class   _lowercase(     nn.Module				):



   """simple docstring"""





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

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

     __UpperCAmelCase         =					self.W





   def 			snake_case	(							self:    Optional[Any]			,a:    Optional[Any]	):






    if self.log:
     __UpperCAmelCase         =					torch.log(a	)

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

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




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Optional[int]			,a:    int			,a:    int			,a:    int			,a:    int			,):
    super().__init__()

    __UpperCAmelCase         =					height
    __UpperCAmelCase         =					width
    __UpperCAmelCase         =					num_embed
    __UpperCAmelCase         =					embed_dim

    __UpperCAmelCase         =					nn.Embedding(self.num_embed			,a	)
    __UpperCAmelCase         =					nn.Embedding(self.height			,a	)
    __UpperCAmelCase         =					nn.Embedding(self.width			,a	)





   def 			snake_case	(							self:    int			,a:    Tuple	):
    __UpperCAmelCase         =					self.emb(a	)

    __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   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Optional[Any]			,a:    Tuple			,a:    int			,a:    Union[str, Any]	):
    super().__init__()
    __UpperCAmelCase         =					dropout_prob > 0
    __UpperCAmelCase         =					nn.Embedding(num_classes + use_cfg_embedding			,a	)
    __UpperCAmelCase         =					num_classes
    __UpperCAmelCase         =					dropout_prob





   def 			snake_case	(							self:    int			,a:    Tuple			,a:    List[str]=None	):






    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(a			,self.num_classes			,a	)
    return labels





   def 			snake_case	(							self:    List[Any]			,a:    torch.LongTensor			,a:    Tuple=None	):
    __UpperCAmelCase         =					self.dropout_prob > 0
    if (self.training and use_dropout) or (force_drop_ids is not None):
     __UpperCAmelCase         =					self.token_drop(a			,a	)
    __UpperCAmelCase         =					self.embedding_table(a	)
    return embeddings




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Any			,a:    int = 1024			,a:    int = 768			,a:    int = 768			,a:    int = 10			,):
    super().__init__()

    __UpperCAmelCase         =					num_image_text_embeds
    __UpperCAmelCase         =					nn.Linear(a			,self.num_image_text_embeds * cross_attention_dim	)
    __UpperCAmelCase         =					nn.Linear(a			,a	)





   def 			snake_case	(							self:    Tuple			,a:    torch.FloatTensor			,a:    torch.FloatTensor	):
    __UpperCAmelCase         =					text_embeds.shape[0]

    # image
    __UpperCAmelCase         =					self.image_embeds(a	)
    __UpperCAmelCase         =					image_text_embeds.reshape(a			,self.num_image_text_embeds			,-1	)

    # text
    __UpperCAmelCase         =					self.text_proj(a	)

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




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Optional[int]			,a:    int = 768			,a:    int = 768			,a:    int = 32			,):
    super().__init__()

    __UpperCAmelCase         =					num_image_text_embeds
    __UpperCAmelCase         =					nn.Linear(a			,self.num_image_text_embeds * cross_attention_dim	)
    __UpperCAmelCase         =					nn.LayerNorm(a	)





   def 			snake_case	(							self:    List[str]			,a:    torch.FloatTensor	):
    __UpperCAmelCase         =					image_embeds.shape[0]

    # image
    __UpperCAmelCase         =					self.image_embeds(a	)
    __UpperCAmelCase         =					image_embeds.reshape(a			,self.num_image_text_embeds			,-1	)
    __UpperCAmelCase         =					self.norm(a	)
    return image_embeds




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    int			,a:    Union[str, Any]			,a:    Optional[Any]=0.1	):
    super().__init__()

    __UpperCAmelCase         =					Timesteps(num_channels=256			,flip_sin_to_cos=a			,downscale_freq_shift=1	)
    __UpperCAmelCase         =					TimestepEmbedding(in_channels=256			,time_embed_dim=a	)
    __UpperCAmelCase         =					LabelEmbedding(a			,a			,a	)





   def 			snake_case	(							self:    int			,a:    Optional[int]			,a:    int			,a:    Optional[int]=None	):
    __UpperCAmelCase         =					self.time_proj(a	)
    __UpperCAmelCase         =					self.timestep_embedder(timesteps_proj.to(dtype=a	)	)  # (N, D)

    __UpperCAmelCase         =					self.class_embedder(a	)  # (N, D)

    __UpperCAmelCase         =					timesteps_emb + class_labels  # (N, D)

    return conditioning




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    str			,a:    int			,a:    int			,a:    int = 64	):
    super().__init__()
    __UpperCAmelCase         =					nn.LayerNorm(a	)
    __UpperCAmelCase         =					AttentionPooling(a			,a	)
    __UpperCAmelCase         =					nn.Linear(a			,a	)
    __UpperCAmelCase         =					nn.LayerNorm(a	)





   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					self.norma(a	)
    __UpperCAmelCase         =					self.pool(a	)
    __UpperCAmelCase         =					self.proj(a	)
    __UpperCAmelCase         =					self.norma(a	)
    return hidden_states




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    Tuple			,a:    int = 768			,a:    int = 768			,a:    int = 1536	):
    super().__init__()
    __UpperCAmelCase         =					nn.Linear(a			,a	)
    __UpperCAmelCase         =					nn.LayerNorm(a	)
    __UpperCAmelCase         =					nn.Linear(a			,a	)





   def 			snake_case	(							self:    str			,a:    torch.FloatTensor			,a:    torch.FloatTensor	):
    __UpperCAmelCase         =					self.text_proj(a	)
    __UpperCAmelCase         =					self.text_norm(a	)

    # image
    __UpperCAmelCase         =					self.image_proj(a	)

    return time_image_embeds + time_text_embeds




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    int			,a:    int = 768			,a:    int = 1536	):
    super().__init__()
    __UpperCAmelCase         =					nn.Linear(a			,a	)
    __UpperCAmelCase         =					nn.LayerNorm(a	)





   def 			snake_case	(							self:    Any			,a:    torch.FloatTensor	):
    __UpperCAmelCase         =					self.image_proj(a	)
    __UpperCAmelCase         =					self.image_norm(a	)
    return time_image_embeds




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    int = 768			,a:    int = 1536	):
    super().__init__()
    __UpperCAmelCase         =					nn.Linear(a			,a	)
    __UpperCAmelCase         =					nn.LayerNorm(a	)
    __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 			snake_case	(							self:    Any			,a:    torch.FloatTensor			,a:    torch.FloatTensor	):
    __UpperCAmelCase         =					self.image_proj(a	)
    __UpperCAmelCase         =					self.image_norm(a	)
    __UpperCAmelCase         =					self.input_hint_block(a	)
    return time_image_embeds, hint




class   _lowercase(     nn.Module				):



   """simple docstring"""





   def __init__(							self:    List[Any]			,a:    Any			,a:    Union[str, Any]			,a:    int=None	):
    super().__init__()
    __UpperCAmelCase         =					dtype
    __UpperCAmelCase         =					nn.Parameter(torch.randn(1			,a	) / embed_dim**0.5	)
    __UpperCAmelCase         =					nn.Linear(a			,a			,dtype=self.dtype	)
    __UpperCAmelCase         =					nn.Linear(a			,a			,dtype=self.dtype	)
    __UpperCAmelCase         =					nn.Linear(a			,a			,dtype=self.dtype	)
    __UpperCAmelCase         =					num_heads
    __UpperCAmelCase         =					embed_dim // self.num_heads





   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					x.size()

    def shape(a:    List[Any]	):
     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
     __UpperCAmelCase         =					x.view(a			,-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=a	) + 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(a	)	)
    # (bs*n_heads, length+class_token_length, dim_per_head)
    __UpperCAmelCase         =					shape(self.k_proj(a	)	)
    __UpperCAmelCase         =					shape(self.v_proj(a	)	)

    # (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'			,a			,a	)

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

    return a[:, 0, :]  # cls_token




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     list			):

 for insert_index, insert_value in enumerate(collection[1:]			):
  __UpperCAmelCase         =					insert_index
  while insert_index >= 0 and insert_value < collection[insert_index]:
   __UpperCAmelCase         =					collection[insert_index]
   insert_index -= 1
  if insert_index != temp_index:
   __UpperCAmelCase         =					insert_value
 return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _UpperCamelCase				:		List[Any]       			=		input('Enter numbers separated by a comma:\n').strip()
     _UpperCamelCase				:		List[Any]       			=		[int(item) for item in user_input.split(',')]
     print(f"{insertion_sort(unsorted) = }")



