
'''simple docstring'''

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	torch.Tensor   ,     snake_case_				:	int   ,     snake_case_				:	bool = False   ,     snake_case_				:	float = 1   ,     snake_case_				:	float = 1   ,     snake_case_				:	int = 1_0000   ,     )       ->	Union[str, Any]:
 assert len(timesteps.shape							) == 1, "Timesteps should be a 1d-array"

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

 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.exp(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 SCREAMING_SNAKE_CASE  :  Any								=		scale * emb

 # concat sine and cosine embeddings
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.cat([torch.sin(snake_case_							), torch.cos(snake_case_							)]   ,     dim=-1							)

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

 # zero pad
 if embedding_dim % 2 == 1:
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.nn.functional.pad(snake_case_   ,     (0, 1, 0, 0)							)
 return emb



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

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



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

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

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



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

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

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

 SCREAMING_SNAKE_CASE  :  List[Any]								=		np.sin(snake_case_							)  # (M, D/2)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		np.cos(snake_case_							)  # (M, D/2)

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


class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE=224    ,__SCREAMING_SNAKE_CASE=224    ,__SCREAMING_SNAKE_CASE=16    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		(height // patch_size) * (width // patch_size)
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		flatten
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		layer_norm

    SCREAMING_SNAKE_CASE  :  List[Any]								=		nn.Convad(
        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,kernel_size=(patch_size, patch_size)    ,stride=__SCREAMING_SNAKE_CASE    ,bias=__SCREAMING_SNAKE_CASE      )
    if layer_norm:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,elementwise_affine=__SCREAMING_SNAKE_CASE    ,eps=1e-6      )
    else:
     SCREAMING_SNAKE_CASE  :  Any								=		None

    SCREAMING_SNAKE_CASE  :  List[str]								=		get_ad_sincos_pos_embed(__SCREAMING_SNAKE_CASE    ,int(num_patches**0.5      )      )
    self.register_buffer('pos_embed'    ,torch.from_numpy(__SCREAMING_SNAKE_CASE      ).float().unsqueeze(0      )    ,persistent=__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.proj(__SCREAMING_SNAKE_CASE      )
    if self.flatten:
     SCREAMING_SNAKE_CASE  :  str								=		latent.flatten(2      ).transpose(1    ,2      )  # BCHW -> BNC
    if self.layer_norm:
     SCREAMING_SNAKE_CASE  :  int								=		self.norm(__SCREAMING_SNAKE_CASE      )
    return latent + self.pos_embed



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = "silu"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE=None    ,):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  List[str]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    if cond_proj_dim is not None:
     SCREAMING_SNAKE_CASE  :  int								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,bias=__SCREAMING_SNAKE_CASE      )
    else:
     SCREAMING_SNAKE_CASE  :  Tuple								=		None

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		get_activation(__SCREAMING_SNAKE_CASE      )

    if out_dim is not None:
     SCREAMING_SNAKE_CASE  :  Any								=		out_dim
    else:
     SCREAMING_SNAKE_CASE  :  Any								=		time_embed_dim
    SCREAMING_SNAKE_CASE  :  List[str]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    if post_act_fn is None:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    else:
     SCREAMING_SNAKE_CASE  :  str								=		get_activation(__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
    if condition is not None:
     SCREAMING_SNAKE_CASE  :  Dict								=		sample + self.cond_proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.linear_a(__SCREAMING_SNAKE_CASE      )

    if self.act is not None:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.act(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  str								=		self.linear_a(__SCREAMING_SNAKE_CASE      )

    if self.post_act is not None:
     SCREAMING_SNAKE_CASE  :  str								=		self.post_act(__SCREAMING_SNAKE_CASE      )
    return sample



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		num_channels
    SCREAMING_SNAKE_CASE  :  str								=		flip_sin_to_cos
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		downscale_freq_shift






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		get_timestep_embedding(
        __SCREAMING_SNAKE_CASE    ,self.num_channels    ,flip_sin_to_cos=self.flip_sin_to_cos    ,downscale_freq_shift=self.downscale_freq_shift    ,)
    return t_emb



class 		_a     (							nn.Module  ):







   """simple docstring"""




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

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

     SCREAMING_SNAKE_CASE  :  int								=		self.W






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    if self.log:
     SCREAMING_SNAKE_CASE  :  List[str]								=		torch.log(__SCREAMING_SNAKE_CASE      )

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

    if self.flip_sin_to_cos:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.cat([torch.cos(__SCREAMING_SNAKE_CASE      ), torch.sin(__SCREAMING_SNAKE_CASE      )]    ,dim=-1      )
    else:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.cat([torch.sin(__SCREAMING_SNAKE_CASE      ), torch.cos(__SCREAMING_SNAKE_CASE      )]    ,dim=-1      )
    return out



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  str								=		height
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		width
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_embed
    SCREAMING_SNAKE_CASE  :  Tuple								=		embed_dim

    SCREAMING_SNAKE_CASE  :  int								=		nn.Embedding(self.num_embed    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		nn.Embedding(self.height    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		nn.Embedding(self.width    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.emb(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		self.height_emb(torch.arange(self.height    ,device=index.device      ).view(1    ,self.height      )      )

    # 1 x H x D -> 1 x H x 1 x D
    SCREAMING_SNAKE_CASE  :  Dict								=		height_emb.unsqueeze(2      )

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

    SCREAMING_SNAKE_CASE  :  List[str]								=		height_emb + width_emb

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

    SCREAMING_SNAKE_CASE  :  Tuple								=		emb + pos_emb[:, : emb.shape[1], :]

    return emb



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Tuple								=		dropout_prob > 0
    SCREAMING_SNAKE_CASE  :  int								=		nn.Embedding(num_classes + use_cfg_embedding    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		num_classes
    SCREAMING_SNAKE_CASE  :  int								=		dropout_prob






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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.dropout_prob > 0
    if (self.training and use_dropout) or (force_drop_ids is not None):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.token_drop(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		self.embedding_table(__SCREAMING_SNAKE_CASE      )
    return embeddings



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 1024    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 10    ,):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  int								=		num_image_text_embeds
    SCREAMING_SNAKE_CASE  :  Dict								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,self.num_image_text_embeds * cross_attention_dim      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		text_embeds.shape[0]

    # image
    SCREAMING_SNAKE_CASE  :  str								=		self.image_embeds(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_text_embeds.reshape(__SCREAMING_SNAKE_CASE    ,self.num_image_text_embeds    ,-1      )

    # text
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.text_proj(__SCREAMING_SNAKE_CASE      )

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



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 32    ,):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  Any								=		num_image_text_embeds
    SCREAMING_SNAKE_CASE  :  List[str]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,self.num_image_text_embeds * cross_attention_dim      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		image_embeds.shape[0]

    # image
    SCREAMING_SNAKE_CASE  :  str								=		self.image_embeds(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_embeds.reshape(__SCREAMING_SNAKE_CASE    ,self.num_image_text_embeds    ,-1      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.norm(__SCREAMING_SNAKE_CASE      )
    return image_embeds



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=0.1      ):
    super().__init__()

    SCREAMING_SNAKE_CASE  :  List[str]								=		Timesteps(num_channels=256    ,flip_sin_to_cos=__SCREAMING_SNAKE_CASE    ,downscale_freq_shift=1      )
    SCREAMING_SNAKE_CASE  :  Any								=		TimestepEmbedding(in_channels=256    ,time_embed_dim=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LabelEmbedding(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.time_proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE      )      )  # (N, D)

    SCREAMING_SNAKE_CASE  :  int								=		self.class_embedder(__SCREAMING_SNAKE_CASE      )  # (N, D)

    SCREAMING_SNAKE_CASE  :  int								=		timesteps_emb + class_labels  # (N, D)

    return conditioning



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 64      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		AttentionPooling(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.norma(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.pool(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		self.proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.norma(__SCREAMING_SNAKE_CASE      )
    return hidden_states



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 1536      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  int								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    # text
    SCREAMING_SNAKE_CASE  :  Dict								=		self.text_proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.text_norm(__SCREAMING_SNAKE_CASE      )

    # image
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.image_proj(__SCREAMING_SNAKE_CASE      )

    return time_image_embeds + time_text_embeds



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 1536      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  List[Any]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    # image
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.image_proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		self.image_norm(__SCREAMING_SNAKE_CASE      )
    return time_image_embeds



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 768    ,__SCREAMING_SNAKE_CASE = 1536      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		nn.Linear(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.LayerNorm(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		nn.Sequential(
        nn.Convad(3    ,16    ,3    ,padding=1      )    ,nn.SiLU()    ,nn.Convad(16    ,16    ,3    ,padding=1      )    ,nn.SiLU()    ,nn.Convad(16    ,32    ,3    ,padding=1    ,stride=2      )    ,nn.SiLU()    ,nn.Convad(32    ,32    ,3    ,padding=1      )    ,nn.SiLU()    ,nn.Convad(32    ,96    ,3    ,padding=1    ,stride=2      )    ,nn.SiLU()    ,nn.Convad(96    ,96    ,3    ,padding=1      )    ,nn.SiLU()    ,nn.Convad(96    ,256    ,3    ,padding=1    ,stride=2      )    ,nn.SiLU()    ,nn.Convad(256    ,4    ,3    ,padding=1      )    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    # image
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.image_proj(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.image_norm(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		self.input_hint_block(__SCREAMING_SNAKE_CASE      )
    return time_image_embeds, hint



class 		_a     (							nn.Module  ):







   """simple docstring"""




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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  int								=		x.size()

    def shape(__SCREAMING_SNAKE_CASE      ):
     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
     SCREAMING_SNAKE_CASE  :  str								=		x.view(__SCREAMING_SNAKE_CASE    ,-1    ,self.num_heads    ,self.dim_per_head      )
     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
     SCREAMING_SNAKE_CASE  :  Dict								=		x.transpose(1    ,2      )
     # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		x.reshape(bs * self.num_heads    ,-1    ,self.dim_per_head      )
     # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		x.transpose(1    ,2      )
     return x

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

    # (bs*n_heads, class_token_length, dim_per_head)
    SCREAMING_SNAKE_CASE  :  Any								=		shape(self.q_proj(__SCREAMING_SNAKE_CASE      )      )
    # (bs*n_heads, length+class_token_length, dim_per_head)
    SCREAMING_SNAKE_CASE  :  int								=		shape(self.k_proj(__SCREAMING_SNAKE_CASE      )      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		shape(self.v_proj(__SCREAMING_SNAKE_CASE      )      )

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

    # (bs*n_heads, dim_per_head, class_token_length)
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.einsum('bts,bcs->bct'    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    # (bs, length+1, width)
    SCREAMING_SNAKE_CASE  :  List[Any]								=		a.reshape(__SCREAMING_SNAKE_CASE    ,-1    ,1      ).transpose(1    ,2      )

    return a[:, 0, :]  # cls_token




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str = "mytext.txt"   ,     snake_case_				:	bool = False							)       ->	None:
 import socket

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		1_2312  # Reserve a port for your service.
 SCREAMING_SNAKE_CASE  :  Any								=		socket.socket()  # Create a socket object
 SCREAMING_SNAKE_CASE  :  int								=		socket.gethostname()  # Get local machine name
 sock.bind((host, port)							)  # Bind to the port
 sock.listen(5							)  # Now wait for client connection.

 print('Server listening....'							)

 while True:
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sock.accept()  # Establish connection with client.
  print(f"""Got connection from {addr}"""							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		conn.recv(1024							)
  print(f"""Server received: {data = }"""							)

  with open(snake_case_   ,     'rb'							) as in_file:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		in_file.read(1024							)
   while data:
    conn.send(snake_case_							)
    print(f"""Sent {data!r}"""							)
    SCREAMING_SNAKE_CASE  :  List[Any]								=		in_file.read(1024							)

  print('Done sending'							)
  conn.close()
  if testing:  # Allow the test to complete
   break

 sock.shutdown(1							)
 sock.close()


if __name__ == "__main__":
   send_file()


