



'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Tuple			)		->						Any:
				snake_case__      :   List[Any]      =    UNetaDModel(**UpperCamelCase__			)
				snake_case__      :   int      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   List[Any]      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.weight'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.bias'''].data

				snake_case__      :   Tuple      =    checkpoint['''all_modules.2.weight'''].data
				snake_case__      :   Optional[int]      =    checkpoint['''all_modules.2.bias'''].data

				snake_case__      :   Optional[Any]      =    checkpoint['''all_modules.3.weight'''].data
				snake_case__      :   List[str]      =    checkpoint['''all_modules.3.bias'''].data

				snake_case__      :   Any      =    checkpoint[list(checkpoint.keys()			)[-4]].data
				snake_case__      :   List[str]      =    checkpoint[list(checkpoint.keys()			)[-3]].data
				snake_case__      :   Optional[Any]      =    checkpoint[list(checkpoint.keys()			)[-2]].data
				snake_case__      :   Any      =    checkpoint[list(checkpoint.keys()			)[-1]].data

				snake_case__      :   Any      =    4

				def set_attention_weights(UpperCamelCase__				:Dict   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			):
								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
								snake_case__      :   str      =    old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
								snake_case__      :   List[str]      =    old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

								snake_case__      :   List[str]      =    old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
								snake_case__      :   Tuple      =    old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

				def set_resnet_weights(UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			):
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
								snake_case__      :   str      =    old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
								snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
								snake_case__      :   Union[str, Any]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

								if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
												snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
												snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

				for i, block in enumerate(new_model_architecture.downsample_blocks			):
								snake_case__      :   Optional[int]      =    hasattr(UpperCamelCase__   ,				'''attentions'''			)
								for j in range(len(block.resnets			)			):
												set_resnet_weights(block.resnets[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
																module_index += 1

								if hasattr(UpperCamelCase__   ,				'''downsamplers'''			) and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
												snake_case__      :   Any      =    checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
												snake_case__      :   Optional[int]      =    checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
												module_index += 1

				set_resnet_weights(new_model_architecture.mid_block.resnets[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1
				set_attention_weights(new_model_architecture.mid_block.attentions[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1
				set_resnet_weights(new_model_architecture.mid_block.resnets[1]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1

				for i, block in enumerate(new_model_architecture.up_blocks			):
								snake_case__      :   Optional[Any]      =    hasattr(UpperCamelCase__   ,				'''attentions'''			)
								for j in range(len(block.resnets			)			):
												set_resnet_weights(block.resnets[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
								if has_attentions:
												set_attention_weights(
												    block.attentions[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)  # why can there only be a single attention layer for up?
												module_index += 1

								if hasattr(UpperCamelCase__   ,				'''resnet_up'''			) and block.resnet_up is not None:
												snake_case__      :   Union[str, Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												snake_case__      :   str      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   Union[str, Any]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												set_resnet_weights(block.resnet_up   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1

				snake_case__      :   List[Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
				module_index += 1
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
				_lowercase       :						Tuple						=argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin",
				    type=str,
				    required=False,
				    help="Path to the checkpoint to convert.",
				)

				parser.add_argument(
				    "--config_file",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json",
				    type=str,
				    required=False,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument(
				    "--dump_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
				    type=str,
				    required=False,
				    help="Path to the output model.",
				)

				_lowercase       :						Any						=parser.parse_args()

				_lowercase       :						List[Any]						=torch.load(args.checkpoint_path, map_location="cpu")

				with open(args.config_file) as f:
								_lowercase       :						List[Any]						=json.loads(f.read())

				_lowercase       :						List[str]						=convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

				if "sde" in config:
								del config["sde"]

				_lowercase       :						Tuple						=UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								_lowercase       :						str						=ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								_lowercase       :						List[Any]						=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)








'''simple docstring'''



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      int     ,   _lowercase   :      bool = False     ,   _lowercase   :      float = 1     ,   _lowercase   :      float = 1     ,   _lowercase   :      int = 10000     ,   ):





     '''simple docstring'''






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

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

     __UpperCAmelCase      :					Optional[int]    				=     torch.exp(_lowercase    )
     __UpperCAmelCase      :					Union[str, Any]    				=     timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     __UpperCAmelCase      :					List[str]    				=     scale * emb

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

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

     # zero pad
     if embedding_dim % 2 == 1:
          __UpperCAmelCase      :					Optional[Any]    				=     torch.nn.functional.pad(_lowercase     ,   (0, 1, 0, 0)    )
     return emb




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Union[str, Any]     ,   _lowercase   :      Optional[Any]=False     ,   _lowercase   :      Optional[Any]=0    ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					List[Any]    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					Optional[int]    				=     np.meshgrid(_lowercase     ,   _lowercase    )  # here w goes first
     __UpperCAmelCase      :					int    				=     np.stack(_lowercase     ,   axis=0    )

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




def       _a      (		_lowercase   :      Dict     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






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

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

     __UpperCAmelCase      :					Union[str, Any]    				=     np.concatenate([emb_h, emb_w]     ,   axis=1    )  # (H*W, D)
     return emb




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






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

     __UpperCAmelCase      :					Optional[int]    				=     np.arange(embed_dim // 2     ,   dtype=np.floataa    )
     omega /= embed_dim / 2.0
     __UpperCAmelCase      :					Optional[int]    				=     1.0 / 10000**omega  # (D/2,)

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

     __UpperCAmelCase      :					Dict    				=     np.sin(_lowercase    )  # (M, D/2)
     __UpperCAmelCase      :					List[str]    				=     np.cos(_lowercase    )  # (M, D/2)

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



class       a							(						nn.Module			):


     """simple docstring"""






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

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

          __UpperCAmelCase      :					Tuple    				=     nn.Convad(
              snake_case				,					snake_case				,					kernel_size=(patch_size, patch_size)				,					stride=snake_case				,					bias=snake_case     )
          if layer_norm:
               __UpperCAmelCase      :					Tuple    				=     nn.LayerNorm(snake_case				,					elementwise_affine=snake_case				,					eps=1E-6     )
          else:
               __UpperCAmelCase      :					Optional[int]    				=     None

          __UpperCAmelCase      :					Union[str, Any]    				=     get_ad_sincos_pos_embed(snake_case				,					int(num_patches**0.5     )     )
          self.register_buffer('''pos_embed'''				,					torch.from_numpy(snake_case     ).float().unsqueeze(0     )				,					persistent=snake_case     )






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



class       a							(						nn.Module			):


     """simple docstring"""






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

          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )

          if cond_proj_dim is not None:
               __UpperCAmelCase      :					Optional[int]    				=     nn.Linear(snake_case				,					snake_case				,					bias=snake_case     )
          else:
               __UpperCAmelCase      :					Optional[Any]    				=     None

          __UpperCAmelCase      :					Any    				=     get_activation(snake_case     )

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

          if post_act_fn is None:
               __UpperCAmelCase      :					Optional[int]    				=     None
          else:
               __UpperCAmelCase      :					Tuple    				=     get_activation(snake_case     )






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						Dict				,					snake_case	:						Optional[int]=None     )		->				List[str]:
          if condition is not None:
               __UpperCAmelCase      :					str    				=     sample + self.cond_proj(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     self.linear_a(snake_case     )

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

          __UpperCAmelCase      :					Dict    				=     self.linear_a(snake_case     )

          if self.post_act is not None:
               __UpperCAmelCase      :					Optional[int]    				=     self.post_act(snake_case     )
          return sample



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						int				,					snake_case	:						int				,					snake_case	:						bool				,					snake_case	:						float     )		->				List[Any]:
          super().__init__()
          __UpperCAmelCase      :					int    				=     num_channels
          __UpperCAmelCase      :					int    				=     flip_sin_to_cos
          __UpperCAmelCase      :					Tuple    				=     downscale_freq_shift






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

          if set_W_to_weight:
               # to delete later
               __UpperCAmelCase      :					Optional[Any]    				=     nn.Parameter(torch.randn(snake_case     ) * scale				,					requires_grad=snake_case     )

               __UpperCAmelCase      :					Tuple    				=     self.W






     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int     )		->				List[Any]:
          if self.log:
               __UpperCAmelCase      :					Union[str, Any]    				=     torch.log(snake_case     )

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

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



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[int]				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					)		->				Optional[Any]:
          super().__init__()

          __UpperCAmelCase      :					Dict    				=     height
          __UpperCAmelCase      :					int    				=     width
          __UpperCAmelCase      :					Dict    				=     num_embed
          __UpperCAmelCase      :					str    				=     embed_dim

          __UpperCAmelCase      :					Optional[Any]    				=     nn.Embedding(self.num_embed				,					snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Embedding(self.height				,					snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     nn.Embedding(self.width				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						Optional[int]     )		->				Dict:
          __UpperCAmelCase      :					Tuple    				=     self.emb(snake_case     )

          __UpperCAmelCase      :					Tuple    				=     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      :					List[str]    				=     height_emb.unsqueeze(2     )

          __UpperCAmelCase      :					Union[str, Any]    				=     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      :					Optional[Any]    				=     width_emb.unsqueeze(1     )

          __UpperCAmelCase      :					List[Any]    				=     height_emb + width_emb

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

          __UpperCAmelCase      :					Optional[Any]    				=     emb + pos_emb[:, : emb.shape[1], :]

          return emb



class       a							(						nn.Module			):


     """simple docstring"""






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

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






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



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						int = 1024				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 10				,					)		->				List[Any]:
          super().__init__()

          __UpperCAmelCase      :					int    				=     num_image_text_embeds
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				int:
          __UpperCAmelCase      :					Tuple    				=     text_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Tuple    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     image_text_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )

          # text
          __UpperCAmelCase      :					Optional[Any]    				=     self.text_proj(snake_case     )

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



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[int]				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 32				,					)		->				Optional[Any]:
          super().__init__()

          __UpperCAmelCase      :					List[str]    				=     num_image_text_embeds
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor     )		->				Union[str, Any]:
          __UpperCAmelCase      :					Tuple    				=     image_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Any    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					List[Any]    				=     image_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )
          __UpperCAmelCase      :					List[Any]    				=     self.norm(snake_case     )
          return image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






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

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






     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						Any				,					snake_case	:						str				,					snake_case	:						List[str]=None     )		->				Tuple:
          __UpperCAmelCase      :					Tuple    				=     self.time_proj(snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     self.timestep_embedder(timesteps_proj.to(dtype=snake_case     )     )  # (N, D)

          __UpperCAmelCase      :					Union[str, Any]    				=     self.class_embedder(snake_case     )  # (N, D)

          __UpperCAmelCase      :					List[str]    				=     timesteps_emb + class_labels  # (N, D)

          return conditioning



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						str				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int = 64     )		->				Dict:
          super().__init__()
          __UpperCAmelCase      :					str    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     AttentionPooling(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						str				,					snake_case	:						List[str]     )		->				int:
          __UpperCAmelCase      :					str    				=     self.norma(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.pool(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.proj(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.norma(snake_case     )
          return hidden_states



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Any:
          super().__init__()
          __UpperCAmelCase      :					Tuple    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Linear(snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Optional[int]:
          # text
          __UpperCAmelCase      :					Tuple    				=     self.text_proj(snake_case     )
          __UpperCAmelCase      :					int    				=     self.text_norm(snake_case     )

          # image
          __UpperCAmelCase      :					List[str]    				=     self.image_proj(snake_case     )

          return time_image_embeds + time_text_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Any:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						torch.FloatTensor     )		->				Any:
          # image
          __UpperCAmelCase      :					Dict    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					str    				=     self.image_norm(snake_case     )
          return time_image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Union[str, Any]:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     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 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Dict:
          # image
          __UpperCAmelCase      :					Union[str, Any]    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.image_norm(snake_case     )
          __UpperCAmelCase      :					int    				=     self.input_hint_block(snake_case     )
          return time_image_embeds, hint



class       a							(						nn.Module			):


     """simple docstring"""






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






     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						List[Any]     )		->				Optional[int]:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					str    				=     x.size()

          def shape(snake_case	:						Union[str, Any]     ):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               __UpperCAmelCase      :					Dict    				=     x.view(snake_case				,					-1				,					self.num_heads				,					self.dim_per_head     )
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               __UpperCAmelCase      :					Any    				=     x.transpose(1				,					2     )
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               __UpperCAmelCase      :					List[Any]    				=     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      :					Optional[int]    				=     x.transpose(1				,					2     )
               return x

          __UpperCAmelCase      :					List[str]    				=     x.mean(dim=1				,					keepdim=snake_case     ) + self.positional_embedding.to(x.dtype     )
          __UpperCAmelCase      :					Optional[int]    				=     torch.cat([class_token, x]				,					dim=1     )  # (bs, length+1, width)

          # (bs*n_heads, class_token_length, dim_per_head)
          __UpperCAmelCase      :					Union[str, Any]    				=     shape(self.q_proj(snake_case     )     )
          # (bs*n_heads, length+class_token_length, dim_per_head)
          __UpperCAmelCase      :					Any    				=     shape(self.k_proj(snake_case     )     )
          __UpperCAmelCase      :					int    				=     shape(self.v_proj(snake_case     )     )

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

          # (bs*n_heads, dim_per_head, class_token_length)
          __UpperCAmelCase      :					int    				=     torch.einsum('''bts,bcs->bct'''				,					snake_case				,					snake_case     )

          # (bs, length+1, width)
          __UpperCAmelCase      :					Union[str, Any]    				=     a.reshape(snake_case				,					-1				,					1     ).transpose(1				,					2     )

          return a[:, 0, :]  # cls_token