






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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin

@dataclass
class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			torch.FloatTensor







class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	int = 32		,     __a				:	int = 64		,     __a				:	int = 20		,     __a				:	int = 768		,     __a				:	List[Any]=77		,     __a				:	Tuple=4		,     __a				:	float = 0.0		,     __a				:	str = "silu"		,     __a				:	Optional[str] = None		,     __a				:	Optional[str] = None		,     __a				:	Optional[str] = "linear"		,     __a				:	Optional[str] = "prd"		,     __a				:	Optional[int] = None		,     __a				:	Optional[int] = None		,     __a				:	Optional[int] = None		,     )			->  int:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     num_attention_heads
												__lowercase      :							Optional[int]				=     attention_head_dim
												__lowercase      :							Any				=     num_attention_heads * attention_head_dim
												__lowercase      :							Union[str, Any]				=     additional_embeddings

												__lowercase      :							Dict				=     time_embed_dim or inner_dim
												__lowercase      :							Optional[Any]				=     embedding_proj_dim or embedding_dim
												__lowercase      :							int				=     clip_embed_dim or embedding_dim

												__lowercase      :							int				=     Timesteps(__a		,     __a		,     0							)
												__lowercase      :							Union[str, Any]				=     TimestepEmbedding(__a		,     __a		,     out_dim=__a		,     act_fn=__a							)

												__lowercase      :							Optional[int]				=     nn.Linear(__a		,     __a							)

												if embedding_proj_norm_type is None:
																			__lowercase      :							List[str]				=     None
												elif embedding_proj_norm_type == "layer":
																			__lowercase      :							Any				=     nn.LayerNorm(__a							)
												else:
																			raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"							)

												__lowercase      :							Union[str, Any]				=     nn.Linear(__a		,     __a							)

												if encoder_hid_proj_type is None:
																			__lowercase      :							Tuple				=     None
												elif encoder_hid_proj_type == "linear":
																			__lowercase      :							Dict				=     nn.Linear(__a		,     __a							)
												else:
																			raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"							)

												__lowercase      :							List[str]				=     nn.Parameter(torch.zeros(1		,     num_embeddings + additional_embeddings		,     __a							)							)

												if added_emb_type == "prd":
																			__lowercase      :							List[Any]				=     nn.Parameter(torch.zeros(1		,     1		,     __a							)							)
												elif added_emb_type is None:
																			__lowercase      :							Dict				=     None
												else:
																			raise ValueError(
																			    F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."							)

												__lowercase      :							Optional[Any]				=     nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            __a		,     __a		,     __a		,     dropout=__a		,     activation_fn="""gelu"""		,     attention_bias=__a		,     )
												        for d in range(__a							)
												    ]							)

												if norm_in_type == "layer":
																			__lowercase      :							Union[str, Any]				=     nn.LayerNorm(__a							)
												elif norm_in_type is None:
																			__lowercase      :							Dict				=     None
												else:
																			raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."							)

												__lowercase      :							Any				=     nn.LayerNorm(__a							)

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

												__lowercase      :							Any				=     torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]		,     -10000.0							)
												causal_attention_mask.triu_(1							)
												__lowercase      :							Union[str, Any]				=     causal_attention_mask[None, ...]
												self.register_buffer("""causal_attention_mask"""		,     __a		,     persistent=__a							)

												__lowercase      :							Tuple				=     nn.Parameter(torch.zeros(1		,     __a							)							)
												__lowercase      :							str				=     nn.Parameter(torch.zeros(1		,     __a							)							)



					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Dict[str, AttentionProcessor]:


												"""simple docstring"""

												__lowercase      :							Dict				=     {}

												def fn_recursive_add_processors(__a				:	str		,     __a				:	torch.nn.Module		,     __a				:	Dict[str, AttentionProcessor]							):
																			if hasattr(__a		,     """set_processor"""							):
																										__lowercase      :							Tuple				=     module.processor

																			for sub_name, child in module.named_children():
																										fn_recursive_add_processors(F"{name}.{sub_name}"		,     __a		,     __a							)

																			return processors

												for name, module in self.named_children():
																			fn_recursive_add_processors(__a		,     __a		,     __a							)

												return processors



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	Union[AttentionProcessor, Dict[str, AttentionProcessor]]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     len(self.attn_processors.keys()							)

												if isinstance(__a		,     __a							) and len(__a							) != count:
																			raise ValueError(
																			    F"A dict of processors was passed, but the number of processors {len(__a							)} does not match the"
																			    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."							)

												def fn_recursive_attn_processor(__a				:	str		,     __a				:	torch.nn.Module		,     __a				:	Dict							):
																			if hasattr(__a		,     """set_processor"""							):
																										if not isinstance(__a		,     __a							):
																																	module.set_processor(__a							)
																										else:
																																	module.set_processor(processor.pop(F"{name}.processor"							)							)

																			for sub_name, child in module.named_children():
																										fn_recursive_attn_processor(F"{name}.{sub_name}"		,     __a		,     __a							)

												for name, module in self.named_children():
																			fn_recursive_attn_processor(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Dict:


												"""simple docstring"""

												self.set_attn_processor(AttnProcessor()							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	str		,     __a				:	Union[torch.Tensor, float, int]		,     __a				:	torch.FloatTensor		,     __a				:	Optional[torch.FloatTensor] = None		,     __a				:	Optional[torch.BoolTensor] = None		,     __a				:	bool = True		,     )			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     hidden_states.shape[0]

												__lowercase      :							int				=     timestep
												if not torch.is_tensor(__a							):
																			__lowercase      :							Tuple				=     torch.tensor([timesteps]		,     dtype=torch.long		,     device=hidden_states.device							)
												elif torch.is_tensor(__a							) and len(timesteps.shape							) == 0:
																			__lowercase      :							Optional[Any]				=     timesteps[None].to(hidden_states.device							)

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												__lowercase      :							Any				=     timesteps * torch.ones(__a		,     dtype=timesteps.dtype		,     device=timesteps.device							)

												__lowercase      :							Any				=     self.time_proj(__a							)

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might be fp16, so we need to cast here.
												__lowercase      :							int				=     timesteps_projected.to(dtype=self.dtype							)
												__lowercase      :							List[Any]				=     self.time_embedding(__a							)

												if self.embedding_proj_norm is not None:
																			__lowercase      :							Optional[Any]				=     self.embedding_proj_norm(__a							)

												__lowercase      :							Tuple				=     self.embedding_proj(__a							)
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																			__lowercase      :							Dict				=     self.encoder_hidden_states_proj(__a							)
												elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																			raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""							)

												__lowercase      :							List[Any]				=     self.proj_in(__a							)

												__lowercase      :							List[str]				=     self.positional_embedding.to(hidden_states.dtype							)

												__lowercase      :							int				=     []
												__lowercase      :							Dict				=     0

												if encoder_hidden_states is not None:
																			additional_embeds.append(__a							)
																			additional_embeddings_len += encoder_hidden_states.shape[1]

												if len(proj_embeddings.shape							) == 2:
																			__lowercase      :							Optional[int]				=     proj_embeddings[:, None, :]

												if len(hidden_states.shape							) == 2:
																			__lowercase      :							Optional[Any]				=     hidden_states[:, None, :]

												__lowercase      :							Union[str, Any]				=     additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																			__lowercase      :							List[str]				=     self.prd_embedding.to(hidden_states.dtype							).expand(__a		,     -1		,     -1							)
																			additional_embeds.append(__a							)

												__lowercase      :							str				=     torch.cat(
												    __a		,     dim=1		,     )

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												__lowercase      :							List[Any]				=     additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																			__lowercase      :							str				=     F.pad(
																			    __a		,     (
																			        0,
																			        0,
																			        additional_embeddings_len,
																			        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																			    )		,     value=0.0		,     )

												__lowercase      :							str				=     hidden_states + positional_embeddings

												if attention_mask is not None:
																			__lowercase      :							List[str]				=     (1 - attention_mask.to(hidden_states.dtype							)) * -10000.0
																			__lowercase      :							Union[str, Any]				=     F.pad(__a		,     (0, self.additional_embeddings)		,     value=0.0							)
																			__lowercase      :							List[Any]				=     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
																			__lowercase      :							Tuple				=     attention_mask.repeat_interleave(self.config.num_attention_heads		,     dim=0							)

												if self.norm_in is not None:
																			__lowercase      :							Optional[Any]				=     self.norm_in(__a							)

												for block in self.transformer_blocks:
																			__lowercase      :							Optional[Any]				=     block(__a		,     attention_mask=__a							)

												__lowercase      :							Union[str, Any]				=     self.norm_out(__a							)

												if self.prd_embedding is not None:
																			__lowercase      :							Optional[Any]				=     hidden_states[:, -1]
												else:
																			__lowercase      :							int				=     hidden_states[:, additional_embeddings_len:]

												__lowercase      :							Optional[int]				=     self.proj_to_clip_embeddings(__a							)

												if not return_dict:
																			return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=__a							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     (prior_latents * self.clip_std) + self.clip_mean
												return prior_latents







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps