



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   ( __UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			42





class 			_lowerCAmelCase   ( __UpperCAmelCase    ,					__UpperCAmelCase			):






			"""simple docstring"""





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







									super().__init__()
									__lowercase					=				num_attention_heads
									__lowercase					=				attention_head_dim
									__lowercase					=				num_attention_heads * attention_head_dim
									__lowercase					=				additional_embeddings

									__lowercase					=				time_embed_dim or inner_dim
									__lowercase					=				embedding_proj_dim or embedding_dim
									__lowercase					=				clip_embed_dim or embedding_dim

									__lowercase					=				Timesteps(lowercase			,     lowercase			,     0   )
									__lowercase					=				TimestepEmbedding(lowercase			,     lowercase			,     out_dim=lowercase			,     act_fn=lowercase   )

									__lowercase					=				nn.Linear(lowercase			,     lowercase   )

									if embedding_proj_norm_type is None:
															__lowercase					=				None
									elif embedding_proj_norm_type == "layer":
															__lowercase					=				nn.LayerNorm(lowercase   )
									else:
															raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"   )

									__lowercase					=				nn.Linear(lowercase			,     lowercase   )

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

									__lowercase					=				nn.Parameter(torch.zeros(1			,     num_embeddings + additional_embeddings			,     lowercase   )   )

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

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

									if norm_in_type == "layer":
															__lowercase					=				nn.LayerNorm(lowercase   )
									elif norm_in_type is None:
															__lowercase					=				None
									else:
															raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."   )

									__lowercase					=				nn.LayerNorm(lowercase   )

									__lowercase					=				nn.Linear(lowercase			,     lowercase   )

									__lowercase					=				torch.full(
									    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,     -10_000.0   )
									causal_attention_mask.triu_(1   )
									__lowercase					=				causal_attention_mask[None, ...]
									self.register_buffer("""causal_attention_mask"""			,     lowercase			,     persistent=lowercase   )

									__lowercase					=				nn.Parameter(torch.zeros(1			,     lowercase   )   )
									__lowercase					=				nn.Parameter(torch.zeros(1			,     lowercase   )   )





			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 		snake_case__			(				self	:       Dict   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				{}

									def fn_recursive_add_processors(lowercase	:       Any			,     lowercase	:       List[Any]			,     lowercase	:       Optional[Any]   ):
															if hasattr(lowercase			,     """set_processor"""   ):
																					__lowercase					=				module.processor

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

															return processors

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

									return processors





			def 		snake_case__			(				self	:       int			,     lowercase	:       Dict   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				len(self.attn_processors.keys()   )

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

									def fn_recursive_attn_processor(lowercase	:       List[str]			,     lowercase	:       List[Any]			,     lowercase	:       Tuple   ):
															if hasattr(lowercase			,     """set_processor"""   ):
																					if not isinstance(lowercase			,     lowercase   ):
																											module.set_processor(lowercase   )
																					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}"			,     lowercase			,     lowercase   )

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





			def 		snake_case__			(				self	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







									self.set_attn_processor(AttnProcessor()   )





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       Dict			,     lowercase	:       int			,     lowercase	:       List[str]			,     lowercase	:       List[Any] = None			,     lowercase	:       List[Any] = None			,     lowercase	:       List[str] = True			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				hidden_states.shape[0]

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

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

									__lowercase					=				self.time_proj(lowercase   )

									# 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					=				timesteps_projected.to(dtype=self.dtype   )
									__lowercase					=				self.time_embedding(lowercase   )

									if self.embedding_proj_norm is not None:
															__lowercase					=				self.embedding_proj_norm(lowercase   )

									__lowercase					=				self.embedding_proj(lowercase   )
									if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
															__lowercase					=				self.encoder_hidden_states_proj(lowercase   )
									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					=				self.proj_in(lowercase   )

									__lowercase					=				self.positional_embedding.to(hidden_states.dtype   )

									__lowercase					=				[]
									__lowercase					=				0

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

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

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

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

									if self.prd_embedding is not None:
															__lowercase					=				self.prd_embedding.to(hidden_states.dtype   ).expand(lowercase			,     -1			,     -1   )
															additional_embeds.append(lowercase   )

									__lowercase					=				torch.cat(
									    lowercase			,     dim=1			,     )

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

									__lowercase					=				hidden_states + positional_embeddings

									if attention_mask is not None:
															__lowercase					=				(1 - attention_mask.to(hidden_states.dtype   )) * -10_000.0
															__lowercase					=				F.pad(lowercase			,     (0, self.additional_embeddings)			,     value=0.0   )
															__lowercase					=				(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
															__lowercase					=				attention_mask.repeat_interleave(self.config.num_attention_heads			,     dim=0   )

									if self.norm_in is not None:
															__lowercase					=				self.norm_in(lowercase   )

									for block in self.transformer_blocks:
															__lowercase					=				block(lowercase			,     attention_mask=lowercase   )

									__lowercase					=				self.norm_out(lowercase   )

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

									__lowercase					=				self.proj_to_clip_embeddings(lowercase   )

									if not return_dict:
															return (predicted_image_embedding,)

									return PriorTransformerOutput(predicted_image_embedding=lowercase   )







			def 		snake_case__			(				self	:       Dict			,     lowercase	:       List[str]   )						->					Tuple:
									"""simple docstring"""







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










def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				sum(lowercase__   )

						__lowercase					=				[[False for x in range(s + 1   )] for y in range(n + 1   )]

						for i in range(1						,       n + 1   ):
												__lowercase					=				True

						for i in range(1						,       s + 1   ):
												__lowercase					=				False

						for i in range(1						,       n + 1   ):
												for j in range(1						,       s + 1   ):
																		__lowercase					=				dp[i][j - 1]

																		if arr[i - 1] <= j:
																								__lowercase					=				dp[i][j] or dp[i - 1][j - arr[i - 1]]

						for j in range(int(s / 2   )						,       -1						,       -1   ):
												if dp[n][j] is True:
																		__lowercase					=				s - 2 * j
																		break

						return diff





