


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_ = False	,				lowerCAmelCase_ = 1	,				lowerCAmelCase_ = 1	,				lowerCAmelCase_ = 10_000	,				)->	List[str]:
				assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

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

				lowerCAmelCase_	: List[str]      			=			torch.exp(lowerCAmelCase_	)
				lowerCAmelCase_	: int      			=			timesteps[:, None].float() * emb[None, :]

				# scale embeddings
				lowerCAmelCase_	: Optional[int]      			=			scale * emb

				# concat sine and cosine embeddings
				lowerCAmelCase_	: Optional[int]      			=			torch.cat([torch.sin(lowerCAmelCase_	), torch.cos(lowerCAmelCase_	)]	,				dim=-1	)

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

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



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

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Dict:
				if embed_dim % 2 != 0:
								raise ValueError('''embed_dim must be divisible by 2'''	)

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

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Optional[int]:
				if embed_dim % 2 != 0:
								raise ValueError('''embed_dim must be divisible by 2'''	)

				lowerCAmelCase_	: Tuple      			=			np.arange(embed_dim // 2	,				dtype=np.floataa	)
				omega /= embed_dim / 2.0
				lowerCAmelCase_	: Optional[int]      			=			1.0 / 10_000**omega  # (D/2,)

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

				lowerCAmelCase_	: int      			=			np.sin(lowerCAmelCase_	)  # (M, D/2)
				lowerCAmelCase_	: Any      			=			np.cos(lowerCAmelCase_	)  # (M, D/2)

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







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase=2_2_4				,  __lowercase=2_2_4				,  __lowercase=1_6				,  __lowercase=3				,  __lowercase=7_6_8				,  __lowercase=False				,  __lowercase=True				,  __lowercase=True				,  )   ->      str:
										super().__init__()

										lowerCAmelCase_	: Optional[Any]      			=			(height // patch_size) * (width // patch_size)
										lowerCAmelCase_	: List[str]      			=			flatten
										lowerCAmelCase_	: str      			=			layer_norm

										lowerCAmelCase_	: List[str]      			=			nn.Convad(
										    __lowercase				,  __lowercase				,  kernel_size=(patch_size, patch_size)				,  stride=__lowercase				,  bias=__lowercase							)
										if layer_norm:
														lowerCAmelCase_	: Tuple      			=			nn.LayerNorm(__lowercase				,  elementwise_affine=__lowercase				,  eps=1e-6							)
										else:
														lowerCAmelCase_	: List[str]      			=			None

										lowerCAmelCase_	: Optional[Any]      			=			get_ad_sincos_pos_embed(__lowercase				,  int(num_patches**0.5							)							)
										self.register_buffer('''pos_embed'''				,  torch.from_numpy(__lowercase							).float().unsqueeze(0							)				,  persistent=__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Union[str, Any]:
										lowerCAmelCase_	: Optional[Any]      			=			self.proj(__lowercase							)
										if self.flatten:
														lowerCAmelCase_	: int      			=			latent.flatten(2							).transpose(1				,  2							)  # BCHW -> BNC
										if self.layer_norm:
														lowerCAmelCase_	: int      			=			self.norm(__lowercase							)
										return latent + self.pos_embed







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase = "silu"				,  __lowercase = None				,  __lowercase = None				,  __lowercase=None				,  )   ->      int:
										super().__init__()

										lowerCAmelCase_	: Any      			=			nn.Linear(__lowercase				,  __lowercase							)

										if cond_proj_dim is not None:
														lowerCAmelCase_	: Optional[Any]      			=			nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase							)
										else:
														lowerCAmelCase_	: Any      			=			None

										lowerCAmelCase_	: str      			=			get_activation(__lowercase							)

										if out_dim is not None:
														lowerCAmelCase_	: int      			=			out_dim
										else:
														lowerCAmelCase_	: Dict      			=			time_embed_dim
										lowerCAmelCase_	: List[Any]      			=			nn.Linear(__lowercase				,  __lowercase							)

										if post_act_fn is None:
														lowerCAmelCase_	: List[Any]      			=			None
										else:
														lowerCAmelCase_	: Optional[int]      			=			get_activation(__lowercase							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase=None							)   ->      Optional[Any]:
										if condition is not None:
														lowerCAmelCase_	: List[Any]      			=			sample + self.cond_proj(__lowercase							)
										lowerCAmelCase_	: int      			=			self.linear_a(__lowercase							)

										if self.act is not None:
														lowerCAmelCase_	: List[Any]      			=			self.act(__lowercase							)

										lowerCAmelCase_	: Union[str, Any]      			=			self.linear_a(__lowercase							)

										if self.post_act is not None:
														lowerCAmelCase_	: Tuple      			=			self.post_act(__lowercase							)
										return sample







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Optional[int]:
										super().__init__()
										lowerCAmelCase_	: List[str]      			=			num_channels
										lowerCAmelCase_	: Optional[Any]      			=			flip_sin_to_cos
										lowerCAmelCase_	: Optional[int]      			=			downscale_freq_shift




						def   lowercase_	(			self				,  __lowercase							)   ->      int:
										lowerCAmelCase_	: Optional[int]      			=			get_timestep_embedding(
										    __lowercase				,  self.num_channels				,  flip_sin_to_cos=self.flip_sin_to_cos				,  downscale_freq_shift=self.downscale_freq_shift				,  )
										return t_emb







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 2_5_6				,  __lowercase = 1.0				,  __lowercase=True				,  __lowercase=True				,  __lowercase=False							)   ->      Union[str, Any]:
										super().__init__()
										lowerCAmelCase_	: Optional[int]      			=			nn.Parameter(torch.randn(__lowercase							) * scale				,  requires_grad=__lowercase							)
										lowerCAmelCase_	: Dict      			=			log
										lowerCAmelCase_	: str      			=			flip_sin_to_cos

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

														lowerCAmelCase_	: Optional[Any]      			=			self.W




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										if self.log:
														lowerCAmelCase_	: Dict      			=			torch.log(__lowercase							)

										lowerCAmelCase_	: Optional[Any]      			=			x[:, None] * self.weight[None, :] * 2 * np.pi

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







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      Dict:
										super().__init__()

										lowerCAmelCase_	: List[Any]      			=			height
										lowerCAmelCase_	: List[str]      			=			width
										lowerCAmelCase_	: Union[str, Any]      			=			num_embed
										lowerCAmelCase_	: Optional[Any]      			=			embed_dim

										lowerCAmelCase_	: Optional[Any]      			=			nn.Embedding(self.num_embed				,  __lowercase							)
										lowerCAmelCase_	: str      			=			nn.Embedding(self.height				,  __lowercase							)
										lowerCAmelCase_	: int      			=			nn.Embedding(self.width				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Any      			=			self.emb(__lowercase							)

										lowerCAmelCase_	: List[Any]      			=			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
										lowerCAmelCase_	: Optional[int]      			=			height_emb.unsqueeze(2							)

										lowerCAmelCase_	: 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
										lowerCAmelCase_	: Optional[Any]      			=			width_emb.unsqueeze(1							)

										lowerCAmelCase_	: List[Any]      			=			height_emb + width_emb

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

										lowerCAmelCase_	: Dict      			=			emb + pos_emb[:, : emb.shape[1], :]

										return emb







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




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




						def   lowercase_	(			self				,  __lowercase				,  __lowercase=None							)   ->      Dict:
										if force_drop_ids is None:
														lowerCAmelCase_	: List[Any]      			=			torch.rand(labels.shape[0]				,  device=labels.device							) < self.dropout_prob
										else:
														lowerCAmelCase_	: Dict      			=			torch.tensor(force_drop_ids == 1							)
										lowerCAmelCase_	: int      			=			torch.where(__lowercase				,  self.num_classes				,  __lowercase							)
										return labels




						def   lowercase_	(			self				,  __lowercase				,  __lowercase=None							)   ->      Optional[Any]:
										lowerCAmelCase_	: Tuple      			=			self.dropout_prob > 0
										if (self.training and use_dropout) or (force_drop_ids is not None):
														lowerCAmelCase_	: str      			=			self.token_drop(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: List[Any]      			=			self.embedding_table(__lowercase							)
										return embeddings







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 1_0_2_4				,  __lowercase = 7_6_8				,  __lowercase = 7_6_8				,  __lowercase = 1_0				,  )   ->      str:
										super().__init__()

										lowerCAmelCase_	: str      			=			num_image_text_embeds
										lowerCAmelCase_	: List[str]      			=			nn.Linear(__lowercase				,  self.num_image_text_embeds * cross_attention_dim							)
										lowerCAmelCase_	: Optional[int]      			=			nn.Linear(__lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: Optional[int]      			=			text_embeds.shape[0]

										# image
										lowerCAmelCase_	: Any      			=			self.image_embeds(__lowercase							)
										lowerCAmelCase_	: Dict      			=			image_text_embeds.reshape(__lowercase				,  self.num_image_text_embeds				,  -1							)

										# text
										lowerCAmelCase_	: Optional[Any]      			=			self.text_proj(__lowercase							)

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







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 7_6_8				,  __lowercase = 7_6_8				,  __lowercase = 3_2				,  )   ->      Any:
										super().__init__()

										lowerCAmelCase_	: Tuple      			=			num_image_text_embeds
										lowerCAmelCase_	: Tuple      			=			nn.Linear(__lowercase				,  self.num_image_text_embeds * cross_attention_dim							)
										lowerCAmelCase_	: Optional[int]      			=			nn.LayerNorm(__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      List[Any]:
										lowerCAmelCase_	: List[str]      			=			image_embeds.shape[0]

										# image
										lowerCAmelCase_	: Tuple      			=			self.image_embeds(__lowercase							)
										lowerCAmelCase_	: str      			=			image_embeds.reshape(__lowercase				,  self.num_image_text_embeds				,  -1							)
										lowerCAmelCase_	: str      			=			self.norm(__lowercase							)
										return image_embeds







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase=0.1							)   ->      Any:
										super().__init__()

										lowerCAmelCase_	: Dict      			=			Timesteps(num_channels=2_5_6				,  flip_sin_to_cos=__lowercase				,  downscale_freq_shift=1							)
										lowerCAmelCase_	: List[Any]      			=			TimestepEmbedding(in_channels=2_5_6				,  time_embed_dim=__lowercase							)
										lowerCAmelCase_	: List[Any]      			=			LabelEmbedding(__lowercase				,  __lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase=None							)   ->      str:
										lowerCAmelCase_	: Tuple      			=			self.time_proj(__lowercase							)
										lowerCAmelCase_	: Dict      			=			self.timestep_embedder(timesteps_proj.to(dtype=__lowercase							)							)  # (N, D)

										lowerCAmelCase_	: Tuple      			=			self.class_embedder(__lowercase							)  # (N, D)

										lowerCAmelCase_	: Optional[Any]      			=			timesteps_emb + class_labels  # (N, D)

										return conditioning







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase = 6_4							)   ->      Tuple:
										super().__init__()
										lowerCAmelCase_	: Union[str, Any]      			=			nn.LayerNorm(__lowercase							)
										lowerCAmelCase_	: Tuple      			=			AttentionPooling(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: Optional[int]      			=			nn.Linear(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: Tuple      			=			nn.LayerNorm(__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: str      			=			self.norma(__lowercase							)
										lowerCAmelCase_	: Any      			=			self.pool(__lowercase							)
										lowerCAmelCase_	: int      			=			self.proj(__lowercase							)
										lowerCAmelCase_	: Optional[Any]      			=			self.norma(__lowercase							)
										return hidden_states







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 7_6_8				,  __lowercase = 7_6_8				,  __lowercase = 1_5_3_6							)   ->      Optional[Any]:
										super().__init__()
										lowerCAmelCase_	: Optional[int]      			=			nn.Linear(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: Tuple      			=			nn.LayerNorm(__lowercase							)
										lowerCAmelCase_	: Optional[int]      			=			nn.Linear(__lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      str:
										# text
										lowerCAmelCase_	: int      			=			self.text_proj(__lowercase							)
										lowerCAmelCase_	: Tuple      			=			self.text_norm(__lowercase							)

										# image
										lowerCAmelCase_	: List[Any]      			=			self.image_proj(__lowercase							)

										return time_image_embeds + time_text_embeds







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 7_6_8				,  __lowercase = 1_5_3_6							)   ->      Optional[int]:
										super().__init__()
										lowerCAmelCase_	: Any      			=			nn.Linear(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: Optional[int]      			=			nn.LayerNorm(__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										# image
										lowerCAmelCase_	: Dict      			=			self.image_proj(__lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			self.image_norm(__lowercase							)
										return time_image_embeds







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase = 7_6_8				,  __lowercase = 1_5_3_6							)   ->      str:
										super().__init__()
										lowerCAmelCase_	: Optional[int]      			=			nn.Linear(__lowercase				,  __lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			nn.LayerNorm(__lowercase							)
										lowerCAmelCase_	: Any      			=			nn.Sequential(
										    nn.Convad(3				,  1_6				,  3				,  padding=1							)				,  nn.SiLU()				,  nn.Convad(1_6				,  1_6				,  3				,  padding=1							)				,  nn.SiLU()				,  nn.Convad(1_6				,  3_2				,  3				,  padding=1				,  stride=2							)				,  nn.SiLU()				,  nn.Convad(3_2				,  3_2				,  3				,  padding=1							)				,  nn.SiLU()				,  nn.Convad(3_2				,  9_6				,  3				,  padding=1				,  stride=2							)				,  nn.SiLU()				,  nn.Convad(9_6				,  9_6				,  3				,  padding=1							)				,  nn.SiLU()				,  nn.Convad(9_6				,  2_5_6				,  3				,  padding=1				,  stride=2							)				,  nn.SiLU()				,  nn.Convad(2_5_6				,  4				,  3				,  padding=1							)				,  )




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      List[Any]:
										# image
										lowerCAmelCase_	: str      			=			self.image_proj(__lowercase							)
										lowerCAmelCase_	: int      			=			self.image_norm(__lowercase							)
										lowerCAmelCase_	: Optional[Any]      			=			self.input_hint_block(__lowercase							)
										return time_image_embeds, hint







class 			snake_case__(							nn.Module							):







						'''simple docstring'''




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




						def   lowercase_	(			self				,  __lowercase							)   ->      Union[str, Any]:
										lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: str      			=			x.size()

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

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

										# (bs*n_heads, class_token_length, dim_per_head)
										lowerCAmelCase_	: List[str]      			=			shape(self.q_proj(__lowercase							)							)
										# (bs*n_heads, length+class_token_length, dim_per_head)
										lowerCAmelCase_	: Optional[Any]      			=			shape(self.k_proj(__lowercase							)							)
										lowerCAmelCase_	: Optional[Any]      			=			shape(self.v_proj(__lowercase							)							)

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

										# (bs*n_heads, dim_per_head, class_token_length)
										lowerCAmelCase_	: Any      			=			torch.einsum('''bts,bcs->bct'''				,  __lowercase				,  __lowercase							)

										# (bs, length+1, width)
										lowerCAmelCase_	: Optional[int]      			=			a.reshape(__lowercase				,  -1				,  1							).transpose(1				,  2							)

										return a[:, 0, :]  # cls_token



import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_UpperCAmelCase     :					Tuple       =logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase     :					Optional[Any]       ="""
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""







@dataclass
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




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

										self.register_modules(
										    prior=__lowercase				,  text_encoder=__lowercase				,  tokenizer=__lowercase				,  scheduler=__lowercase				,  renderer=__lowercase				,  )




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										if latents is None:
														lowerCAmelCase_	: Dict      			=			randn_tensor(__lowercase				,  generator=__lowercase				,  device=__lowercase				,  dtype=__lowercase							)
										else:
														if latents.shape != shape:
																		raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
														lowerCAmelCase_	: Dict      			=			latents.to(__lowercase							)

										lowerCAmelCase_	: Tuple      			=			latents * scheduler.init_noise_sigma
										return latents




						def   lowercase_	(			self				,  __lowercase=0							)   ->      List[Any]:
										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

										lowerCAmelCase_	: Tuple      			=			torch.device(f"""cuda:{gpu_id}"""							)

										lowerCAmelCase_	: Tuple      			=			[self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(__lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase=0							)   ->      Optional[int]:
										if is_accelerate_available() and is_accelerate_version('''>='''				,  '''0.17.0.dev0'''							):
														from accelerate import cpu_offload_with_hook
										else:
														raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

										lowerCAmelCase_	: Union[str, Any]      			=			torch.device(f"""cuda:{gpu_id}"""							)

										if self.device.type != "cpu":
														self.to('''cpu'''				,  silence_dtype_warnings=__lowercase							)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCAmelCase_	: str      			=			None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
														lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			cpu_offload_with_hook(__lowercase				,  __lowercase				,  prev_module_hook=__lowercase							)

										if self.safety_checker is not None:
														lowerCAmelCase_			,					lowerCAmelCase_	: Optional[Any]      			=			cpu_offload_with_hook(self.safety_checker				,  __lowercase				,  prev_module_hook=__lowercase							)

										# We'll offload the last model manually.
										lowerCAmelCase_	: Union[str, Any]      			=			hook




						@property
						def   lowercase_	(			self							)   ->      Optional[Any]:
										if self.device != torch.device('''meta'''							) or not hasattr(self.text_encoder				,  '''_hf_hook'''							):
														return self.device
										for module in self.text_encoder.modules():
														if (
														    hasattr(__lowercase				,  '''_hf_hook'''							)
														    and hasattr(module._hf_hook				,  '''execution_device'''							)
														    and module._hf_hook.execution_device is not None
														):
																		return torch.device(module._hf_hook.execution_device							)
										return self.device




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      Dict:
										len(__lowercase							) if isinstance(__lowercase				,  __lowercase							) else 1

										# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
										lowerCAmelCase_	: Any      			=			0
										# get prompt text embeddings
										lowerCAmelCase_	: Any      			=			self.tokenizer(
										    __lowercase				,  padding='''max_length'''				,  max_length=self.tokenizer.model_max_length				,  truncation=__lowercase				,  return_tensors='''pt'''				,  )
										lowerCAmelCase_	: Dict      			=			text_inputs.input_ids
										lowerCAmelCase_	: Optional[Any]      			=			self.tokenizer(__lowercase				,  padding='''longest'''				,  return_tensors='''pt'''							).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: List[Any]      			=			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
														logger.warning(
														    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
														    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)

										lowerCAmelCase_	: Any      			=			self.text_encoder(text_input_ids.to(__lowercase							)							)
										lowerCAmelCase_	: Dict      			=			text_encoder_output.text_embeds

										lowerCAmelCase_	: Union[str, Any]      			=			prompt_embeds.repeat_interleave(__lowercase				,  dim=0							)
										# in Shap-E it normalize the prompt_embeds and then later rescale it
										lowerCAmelCase_	: Optional[int]      			=			prompt_embeds / torch.linalg.norm(__lowercase				,  dim=-1				,  keepdim=__lowercase							)

										if do_classifier_free_guidance:
														lowerCAmelCase_	: Optional[Any]      			=			torch.zeros_like(__lowercase							)

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														lowerCAmelCase_	: str      			=			torch.cat([negative_prompt_embeds, prompt_embeds]							)

										# Rescale the features to have unit variance
										lowerCAmelCase_	: List[Any]      			=			math.sqrt(prompt_embeds.shape[1]							) * prompt_embeds

										return prompt_embeds




						@torch.no_grad()
						@replace_example_docstring(__lowercase							)
						def __call__(			self				,  __lowercase				,  __lowercase = 1				,  __lowercase = 2_5				,  __lowercase = None				,  __lowercase = None				,  __lowercase = 4.0				,  __lowercase = 6_4				,  __lowercase = "pil"				,  __lowercase = True				,  )   ->      Tuple:

										if isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: List[str]      			=			1
										elif isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: Optional[int]      			=			len(__lowercase							)
										else:
														raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__lowercase							)}"""							)

										lowerCAmelCase_	: Union[str, Any]      			=			self._execution_device

										lowerCAmelCase_	: Dict      			=			batch_size * num_images_per_prompt

										lowerCAmelCase_	: List[str]      			=			guidance_scale > 1.0
										lowerCAmelCase_	: List[Any]      			=			self._encode_prompt(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)

										# prior

										self.scheduler.set_timesteps(__lowercase				,  device=__lowercase							)
										lowerCAmelCase_	: Any      			=			self.scheduler.timesteps

										lowerCAmelCase_	: Union[str, Any]      			=			self.prior.config.num_embeddings
										lowerCAmelCase_	: Dict      			=			self.prior.config.embedding_dim

										lowerCAmelCase_	: List[str]      			=			self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim)				,  prompt_embeds.dtype				,  __lowercase				,  __lowercase				,  __lowercase				,  self.scheduler				,  )

										# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
										lowerCAmelCase_	: Tuple      			=			latents.reshape(latents.shape[0]				,  __lowercase				,  __lowercase							)

										for i, t in enumerate(self.progress_bar(__lowercase							)							):
														# expand the latents if we are doing classifier free guidance
														lowerCAmelCase_	: int      			=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
														lowerCAmelCase_	: str      			=			self.scheduler.scale_model_input(__lowercase				,  __lowercase							)

														lowerCAmelCase_	: List[str]      			=			self.prior(
														    __lowercase				,  timestep=__lowercase				,  proj_embedding=__lowercase				,  ).predicted_image_embedding

														# remove the variance
														lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			noise_pred.split(
														    scaled_model_input.shape[2]				,  dim=2							)  # batch_size, num_embeddings, embedding_dim

														if do_classifier_free_guidance is not None:
																		lowerCAmelCase_			,					lowerCAmelCase_	: Tuple      			=			noise_pred.chunk(2							)
																		lowerCAmelCase_	: Any      			=			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														lowerCAmelCase_	: Optional[int]      			=			self.scheduler.step(
														    __lowercase				,  timestep=__lowercase				,  sample=__lowercase				,  ).prev_sample

										if output_type == "latent":
														return ShapEPipelineOutput(images=__lowercase							)

										lowerCAmelCase_	: Any      			=			[]
										for i, latent in enumerate(__lowercase							):
														lowerCAmelCase_	: str      			=			self.renderer.decode(
														    latent[None, :]				,  __lowercase				,  size=__lowercase				,  ray_batch_size=4_0_9_6				,  n_coarse_samples=6_4				,  n_fine_samples=1_2_8				,  )
														images.append(__lowercase							)

										lowerCAmelCase_	: List[str]      			=			torch.stack(__lowercase							)

										if output_type not in ["np", "pil"]:
														raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}"""							)

										lowerCAmelCase_	: Optional[int]      			=			images.cpu().numpy()

										if output_type == "pil":
														lowerCAmelCase_	: Tuple      			=			[self.numpy_to_pil(__lowercase							) for image in images]

										# Offload last model to CPU
										if hasattr(self				,  '''final_offload_hook'''							) and self.final_offload_hook is not None:
														self.final_offload_hook.offload()

										if not return_dict:
														return (images,)

										return ShapEPipelineOutput(images=__lowercase							)