




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 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       42




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):

						@register_to_config
						def __init__(		self :Optional[int]     ,			__snake_case :int = 32     ,			__snake_case :int = 64     ,			__snake_case :int = 20     ,			__snake_case :int = 7_68     ,			__snake_case :List[Any]=77     ,			__snake_case :Union[str, Any]=4     ,			__snake_case :float = 0.0     ,			__snake_case :str = "silu"     ,			__snake_case :Optional[str] = None     ,			__snake_case :Optional[str] = None     ,			__snake_case :Optional[str] = "linear"     ,			__snake_case :Optional[str] = "prd"     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = None     ,			):
										'''simple docstring'''

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

										__magic_name__						:						Tuple										=time_embed_dim or inner_dim
										__magic_name__						:						str										=embedding_proj_dim or embedding_dim
										__magic_name__						:						List[Any]										=clip_embed_dim or embedding_dim

										__magic_name__						:						Union[str, Any]										=Timesteps(__snake_case     ,			__snake_case     ,			0			)
										__magic_name__						:						Dict										=TimestepEmbedding(__snake_case     ,			__snake_case     ,			out_dim=__snake_case     ,			act_fn=__snake_case			)

										__magic_name__						:						Tuple										=nn.Linear(__snake_case     ,			__snake_case			)

										if embedding_proj_norm_type is None:
														__magic_name__						:						Tuple										=None
										elif embedding_proj_norm_type == "layer":
														__magic_name__						:						Tuple										=nn.LayerNorm(__snake_case			)
										else:
														raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"			)

										__magic_name__						:						Tuple										=nn.Linear(__snake_case     ,			__snake_case			)

										if encoder_hid_proj_type is None:
														__magic_name__						:						Any										=None
										elif encoder_hid_proj_type == "linear":
														__magic_name__						:						List[str]										=nn.Linear(__snake_case     ,			__snake_case			)
										else:
														raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"			)

										__magic_name__						:						Dict										=nn.Parameter(torch.zeros(1     ,			num_embeddings + additional_embeddings     ,			__snake_case			)			)

										if added_emb_type == "prd":
														__magic_name__						:						Union[str, Any]										=nn.Parameter(torch.zeros(1     ,			1     ,			__snake_case			)			)
										elif added_emb_type is None:
														__magic_name__						:						Optional[Any]										=None
										else:
														raise ValueError(
														    f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."			)

										__magic_name__						:						Optional[int]										=nn.ModuleList(
										    [
										        BasicTransformerBlock(
										            __snake_case     ,			__snake_case     ,			__snake_case     ,			dropout=__snake_case     ,			activation_fn="""gelu"""     ,			attention_bias=__snake_case     ,			)
										        for d in range(__snake_case			)
										    ]			)

										if norm_in_type == "layer":
														__magic_name__						:						Optional[Any]										=nn.LayerNorm(__snake_case			)
										elif norm_in_type is None:
														__magic_name__						:						Optional[Any]										=None
										else:
														raise ValueError(f"Unsupported norm_in_type: {norm_in_type}."			)

										__magic_name__						:						Tuple										=nn.LayerNorm(__snake_case			)

										__magic_name__						:						Union[str, Any]										=nn.Linear(__snake_case     ,			__snake_case			)

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

										__magic_name__						:						Union[str, Any]										=nn.Parameter(torch.zeros(1     ,			__snake_case			)			)
										__magic_name__						:						Tuple										=nn.Parameter(torch.zeros(1     ,			__snake_case			)			)

						@property
						# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Dict										={}

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

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

														return processors

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

										return processors

						def      A__    (		self :Any     ,			__snake_case :Union[AttentionProcessor, Dict[str, AttentionProcessor]]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=len(self.attn_processors.keys()			)

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

										def fn_recursive_attn_processor(__snake_case :str     ,			__snake_case :torch.nn.Module     ,			__snake_case :List[Any]			):
														if hasattr(__snake_case     ,			"""set_processor"""			):
																		if not isinstance(__snake_case     ,			__snake_case			):
																						module.set_processor(__snake_case			)
																		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}"     ,			__snake_case     ,			__snake_case			)

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

						def      A__    (		self :str			):
										'''simple docstring'''

										self.set_attn_processor(AttnProcessor()			)

						def      A__    (		self :int     ,			__snake_case :List[Any]     ,			__snake_case :Union[torch.Tensor, float, int]     ,			__snake_case :torch.FloatTensor     ,			__snake_case :Optional[torch.FloatTensor] = None     ,			__snake_case :Optional[torch.BoolTensor] = None     ,			__snake_case :bool = True     ,			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=hidden_states.shape[0]

										__magic_name__						:						str										=timestep
										if not torch.is_tensor(__snake_case			):
														__magic_name__						:						Dict										=torch.tensor([timesteps]     ,			dtype=torch.long     ,			device=hidden_states.device			)
										elif torch.is_tensor(__snake_case			) and len(timesteps.shape			) == 0:
														__magic_name__						:						Optional[int]										=timesteps[None].to(hidden_states.device			)

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

										__magic_name__						:						Any										=self.time_proj(__snake_case			)

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

										if self.embedding_proj_norm is not None:
														__magic_name__						:						int										=self.embedding_proj_norm(__snake_case			)

										__magic_name__						:						Optional[int]										=self.embedding_proj(__snake_case			)
										if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
														__magic_name__						:						Union[str, Any]										=self.encoder_hidden_states_proj(__snake_case			)
										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"""			)

										__magic_name__						:						Any										=self.proj_in(__snake_case			)

										__magic_name__						:						Tuple										=self.positional_embedding.to(hidden_states.dtype			)

										__magic_name__						:						Dict										=[]
										__magic_name__						:						List[str]										=0

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

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

										if len(hidden_states.shape			) == 2:
														__magic_name__						:						Union[str, Any]										=hidden_states[:, None, :]

										__magic_name__						:						Optional[Any]										=additional_embeds + [
										    proj_embeddings,
										    time_embeddings[:, None, :],
										    hidden_states,
										]

										if self.prd_embedding is not None:
														__magic_name__						:						List[Any]										=self.prd_embedding.to(hidden_states.dtype			).expand(__snake_case     ,			-1     ,			-1			)
														additional_embeds.append(__snake_case			)

										__magic_name__						:						Tuple										=torch.cat(
										    __snake_case     ,			dim=1     ,			)

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

										__magic_name__						:						str										=hidden_states + positional_embeddings

										if attention_mask is not None:
														__magic_name__						:						Tuple										=(1 - attention_mask.to(hidden_states.dtype			)) * -10000.0
														__magic_name__						:						Optional[int]										=F.pad(__snake_case     ,			(0, self.additional_embeddings)     ,			value=0.0			)
														__magic_name__						:						Any										=(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype			)
														__magic_name__						:						Tuple										=attention_mask.repeat_interleave(self.config.num_attention_heads     ,			dim=0			)

										if self.norm_in is not None:
														__magic_name__						:						List[str]										=self.norm_in(__snake_case			)

										for block in self.transformer_blocks:
														__magic_name__						:						str										=block(__snake_case     ,			attention_mask=__snake_case			)

										__magic_name__						:						Tuple										=self.norm_out(__snake_case			)

										if self.prd_embedding is not None:
														__magic_name__						:						Optional[Any]										=hidden_states[:, -1]
										else:
														__magic_name__						:						Union[str, Any]										=hidden_states[:, additional_embeddings_len:]

										__magic_name__						:						str										=self.proj_to_clip_embeddings(__snake_case			)

										if not return_dict:
														return (predicted_image_embedding,)

										return PriorTransformerOutput(predicted_image_embedding=__snake_case			)


						def      A__    (		self :int     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=(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 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''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
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




