



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def  __a  (					lowerCAmelCase_     :      torch.Tensor    ,lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      bool = False    ,lowerCAmelCase_     :      float = 1    ,lowerCAmelCase_     :      float = 1    ,lowerCAmelCase_     :      int = 1_00_00    ,)   ->				Union[str, Any]:






						'''simple docstring'''



						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  __a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      List[str]=False    ,lowerCAmelCase_     :      Union[str, Any]=0       )   ->				Tuple:






						'''simple docstring'''



						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  __a  (					lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Any       )   ->				List[str]:






						'''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_=     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  __a  (					lowerCAmelCase_     :      List[str]    ,lowerCAmelCase_     :      List[Any]       )   ->				Optional[int]:






						'''simple docstring'''



						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_00_00**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			:		Any		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		str=16		,						__UpperCAmelCase			:		Any=3		,						__UpperCAmelCase			:		Optional[int]=768		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		List[str]=True		,						)    ->				Union[str, Any]:
							super().__init__()

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

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

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
							UpperCAmelCase_=     self.proj(__UpperCAmelCase  )
							if self.flatten:
													UpperCAmelCase_=     latent.flatten(2  ).transpose(1		,						2  )  # BCHW -> BNC
							if self.layer_norm:
													UpperCAmelCase_=     self.norm(__UpperCAmelCase  )
							return latent + self.pos_embed






class 	lowercase		(				nn.Module):






	"""simple docstring"""


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

							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )

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

							UpperCAmelCase_=     get_activation(__UpperCAmelCase  )

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

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




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

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

							UpperCAmelCase_=     self.linear_a(__UpperCAmelCase  )

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






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		bool		,						__UpperCAmelCase			:		float  )    ->				int:
							super().__init__()
							UpperCAmelCase_=     num_channels
							UpperCAmelCase_=     flip_sin_to_cos
							UpperCAmelCase_=     downscale_freq_shift




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

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

													UpperCAmelCase_=     self.W




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		Dict  )    ->				List[Any]:
							if self.log:
													UpperCAmelCase_=     torch.log(__UpperCAmelCase  )

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

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






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						)    ->				int:
							super().__init__()

							UpperCAmelCase_=     height
							UpperCAmelCase_=     width
							UpperCAmelCase_=     num_embed
							UpperCAmelCase_=     embed_dim

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Optional[Any]  )    ->				int:
							UpperCAmelCase_=     self.emb(__UpperCAmelCase  )

							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			:		int		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		List[Any]  )    ->				Optional[Any]:
							super().__init__()
							UpperCAmelCase_=     dropout_prob > 0
							UpperCAmelCase_=     nn.Embedding(num_classes + use_cfg_embedding		,						__UpperCAmelCase  )
							UpperCAmelCase_=     num_classes
							UpperCAmelCase_=     dropout_prob




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		int=None  )    ->				Optional[Any]:
							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(__UpperCAmelCase		,						self.num_classes		,						__UpperCAmelCase  )
							return labels




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






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_024		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 10		,						)    ->				List[Any]:
							super().__init__()

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		torch.FloatTensor		,						__UpperCAmelCase			:		torch.FloatTensor  )    ->				Tuple:
							UpperCAmelCase_=     text_embeds.shape[0]

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

							# text
							UpperCAmelCase_=     self.text_proj(__UpperCAmelCase  )

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






class 	lowercase		(				nn.Module):






	"""simple docstring"""


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

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		torch.FloatTensor  )    ->				Any:
							UpperCAmelCase_=     image_embeds.shape[0]

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






class 	lowercase		(				nn.Module):






	"""simple docstring"""


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

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[int]=None  )    ->				Optional[Any]:
							UpperCAmelCase_=     self.time_proj(__UpperCAmelCase  )
							UpperCAmelCase_=     self.timestep_embedder(timesteps_proj.to(dtype=__UpperCAmelCase  )  )  # (N, D)

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

							UpperCAmelCase_=     timesteps_emb + class_labels  # (N, D)

							return conditioning






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int = 64  )    ->				int:
							super().__init__()
							UpperCAmelCase_=     nn.LayerNorm(__UpperCAmelCase  )
							UpperCAmelCase_=     AttentionPooling(__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     nn.LayerNorm(__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Tuple  )    ->				Tuple:
							UpperCAmelCase_=     self.norma(__UpperCAmelCase  )
							UpperCAmelCase_=     self.pool(__UpperCAmelCase  )
							UpperCAmelCase_=     self.proj(__UpperCAmelCase  )
							UpperCAmelCase_=     self.norma(__UpperCAmelCase  )
							return hidden_states






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		Dict		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 1_536  )    ->				int:
							super().__init__()
							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     nn.LayerNorm(__UpperCAmelCase  )
							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		torch.FloatTensor		,						__UpperCAmelCase			:		torch.FloatTensor  )    ->				Tuple:
							# text
							UpperCAmelCase_=     self.text_proj(__UpperCAmelCase  )
							UpperCAmelCase_=     self.text_norm(__UpperCAmelCase  )

							# image
							UpperCAmelCase_=     self.image_proj(__UpperCAmelCase  )

							return time_image_embeds + time_text_embeds






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		Optional[int]		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 1_536  )    ->				Dict:
							super().__init__()
							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     nn.LayerNorm(__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		torch.FloatTensor  )    ->				Optional[int]:
							# image
							UpperCAmelCase_=     self.image_proj(__UpperCAmelCase  )
							UpperCAmelCase_=     self.image_norm(__UpperCAmelCase  )
							return time_image_embeds






class 	lowercase		(				nn.Module):






	"""simple docstring"""


	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 768		,						__UpperCAmelCase			:		int = 1_536  )    ->				Union[str, Any]:
							super().__init__()
							UpperCAmelCase_=     nn.Linear(__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     nn.LayerNorm(__UpperCAmelCase  )
							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 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		torch.FloatTensor		,						__UpperCAmelCase			:		torch.FloatTensor  )    ->				str:
							# image
							UpperCAmelCase_=     self.image_proj(__UpperCAmelCase  )
							UpperCAmelCase_=     self.image_norm(__UpperCAmelCase  )
							UpperCAmelCase_=     self.input_hint_block(__UpperCAmelCase  )
							return time_image_embeds, hint






class 	lowercase		(				nn.Module):






	"""simple docstring"""


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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		List[Any]  )    ->				List[str]:
							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     x.size()

							def shape(__UpperCAmelCase			:		str  ):
													# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
													UpperCAmelCase_=     x.view(__UpperCAmelCase		,						-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=__UpperCAmelCase  ) + 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(__UpperCAmelCase  )  )
							# (bs*n_heads, length+class_token_length, dim_per_head)
							UpperCAmelCase_=     shape(self.k_proj(__UpperCAmelCase  )  )
							UpperCAmelCase_=     shape(self.v_proj(__UpperCAmelCase  )  )

							# (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"""		,						__UpperCAmelCase		,						__UpperCAmelCase  )

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

							return a[:, 0, :]  # cls_token









# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 	lowercase		:






	"""simple docstring"""


	a__   :      CommonSchedulerState
	a__   :      jnp.ndarray

	# setable values
	a__   :      jnp.ndarray
	a__   :      jnp.ndarray
	a__   :      Optional[int]	     =							None




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
							return cls(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)






@dataclass
class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      DDIMSchedulerState






class 	lowercase		(				snake_case__				,					snake_case__):






	"""simple docstring"""


	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]

	a__   :      jnp.dtype




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
							return True




	@register_to_config
	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
							UpperCAmelCase_=     dtype




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
							if common is None:
													UpperCAmelCase_=     CommonSchedulerState.create(self  )

							# At every step in ddim, we are looking into the previous alphas_cumprod
							# For the final step, there is no previous alphas_cumprod because we are already at 0
							# `set_alpha_to_one` decides whether we set this parameter simply to one or
							# whether we use the final alpha of the "non-previous" one.
							UpperCAmelCase_=     (
							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
							)

							# standard deviation of the initial noise distribution
							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )

							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]

							return DDIMSchedulerState.create(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
							return sample




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset

							return state.replace(
							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(
							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
							UpperCAmelCase_=     1 - alpha_prod_t
							UpperCAmelCase_=     1 - alpha_prod_t_prev

							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
							if state.num_inference_steps is None:
													raise ValueError(
													    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps

							UpperCAmelCase_=     state.common.alphas_cumprod
							UpperCAmelCase_=     state.final_alpha_cumprod

							# 2. compute alphas, betas
							UpperCAmelCase_=     alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )

							UpperCAmelCase_=     1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													UpperCAmelCase_=     model_output
							elif self.config.prediction_type == "sample":
													UpperCAmelCase_=     model_output
													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
													    """ `v_prediction`"""  )

							# 4. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
							return self.config.num_train_timesteps




