


'''simple docstring'''






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 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Any   		=  4_2





class 						__UpperCamelCase	(						lowercase__						,  lowercase__							):





							@register_to_config
							def __init__(  self		:Optional[Any]  ,_UpperCamelCase		:str = 3_2  ,_UpperCamelCase		:List[Any] = 6_4  ,_UpperCamelCase		:Any = 2_0  ,_UpperCamelCase		:Any = 7_6_8  ,_UpperCamelCase		:Any=7_7  ,_UpperCamelCase		:Tuple=4  ,_UpperCamelCase		:int = 0.0  ,_UpperCamelCase		:Optional[Any] = "silu"  ,_UpperCamelCase		:Optional[Any] = None  ,_UpperCamelCase		:Union[str, Any] = None  ,_UpperCamelCase		:Optional[int] = "linear"  ,_UpperCamelCase		:List[str] = "prd"  ,_UpperCamelCase		:Tuple = None  ,_UpperCamelCase		:List[str] = None  ,_UpperCamelCase		:Union[str, Any] = None  ,):
									super().__init__()
									snake_case_		:							Dict			       =						num_attention_heads
									snake_case_		:							int			       =						attention_head_dim
									snake_case_		:							List[Any]			       =						num_attention_heads * attention_head_dim
									snake_case_		:							List[str]			       =						additional_embeddings

									snake_case_		:							Tuple			       =						time_embed_dim or inner_dim
									snake_case_		:							List[Any]			       =						embedding_proj_dim or embedding_dim
									snake_case_		:							Union[str, Any]			       =						clip_embed_dim or embedding_dim

									snake_case_		:							str			       =						Timesteps(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,0			)
									snake_case_		:							Union[str, Any]			       =						TimestepEmbedding(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,out_dim=_SCREAMING_SNAKE_CASE  ,act_fn=_SCREAMING_SNAKE_CASE			)

									snake_case_		:							Dict			       =						nn.Linear(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			)

									if embedding_proj_norm_type is None:
											snake_case_		:							int			       =						None
									elif embedding_proj_norm_type == "layer":
											snake_case_		:							Tuple			       =						nn.LayerNorm(_SCREAMING_SNAKE_CASE			)
									else:
											raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''			)

									snake_case_		:							List[Any]			       =						nn.Linear(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			)

									if encoder_hid_proj_type is None:
											snake_case_		:							Optional[int]			       =						None
									elif encoder_hid_proj_type == "linear":
											snake_case_		:							Tuple			       =						nn.Linear(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			)
									else:
											raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''			)

									snake_case_		:							Optional[int]			       =						nn.Parameter(torch.zeros(1  ,num_embeddings + additional_embeddings  ,_SCREAMING_SNAKE_CASE			)			)

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

									snake_case_		:							Dict			       =						nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,dropout=_SCREAMING_SNAKE_CASE  ,activation_fn="""gelu"""  ,attention_bias=_SCREAMING_SNAKE_CASE  ,)
									        for d in range(_SCREAMING_SNAKE_CASE			)
									    ]			)

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

									snake_case_		:							List[str]			       =						nn.LayerNorm(_SCREAMING_SNAKE_CASE			)

									snake_case_		:							Optional[Any]			       =						nn.Linear(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			)

									snake_case_		:							Tuple			       =						torch.full(
									    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]  ,-1_0_0_0_0.0			)
									causal_attention_mask.triu_(1			)
									snake_case_		:							List[str]			       =						causal_attention_mask[None, ...]
									self.register_buffer("""causal_attention_mask"""  ,_SCREAMING_SNAKE_CASE  ,persistent=_SCREAMING_SNAKE_CASE			)

									snake_case_		:							str			       =						nn.Parameter(torch.zeros(1  ,_SCREAMING_SNAKE_CASE			)			)
									snake_case_		:							str			       =						nn.Parameter(torch.zeros(1  ,_SCREAMING_SNAKE_CASE			)			)





							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						{}

									def fn_recursive_add_processors(_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[int]			):
											if hasattr(_SCREAMING_SNAKE_CASE  ,"""set_processor"""			):
													snake_case_		:							Optional[Any]			       =						module.processor

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

											return processors

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

									return processors





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						len(self.attn_processors.keys()			)

									if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			) and len(_SCREAMING_SNAKE_CASE			) != count:
											raise ValueError(
											    F'''A dict of processors was passed, but the number of processors {len(_SCREAMING_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(_UpperCamelCase		:str  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Optional[Any]			):
											if hasattr(_SCREAMING_SNAKE_CASE  ,"""set_processor"""			):
													if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			):
															module.set_processor(_SCREAMING_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}'''  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE			)

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





							def 		a__   (  self		:Any			):
									self.set_attn_processor(AttnProcessor()			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:List[Any] = None  ,_UpperCamelCase		:int = None  ,_UpperCamelCase		:Optional[Any] = True  ,):
									snake_case_		:							str			       =						hidden_states.shape[0]

									snake_case_		:							List[Any]			       =						timestep
									if not torch.is_tensor(_SCREAMING_SNAKE_CASE			):
											snake_case_		:							Union[str, Any]			       =						torch.tensor([timesteps]  ,dtype=torch.long  ,device=hidden_states.device			)
									elif torch.is_tensor(_SCREAMING_SNAKE_CASE			) and len(timesteps.shape			) == 0:
											snake_case_		:							int			       =						timesteps[None].to(hidden_states.device			)

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

									snake_case_		:							int			       =						self.time_proj(_SCREAMING_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.
									snake_case_		:							Any			       =						timesteps_projected.to(dtype=self.dtype			)
									snake_case_		:							Tuple			       =						self.time_embedding(_SCREAMING_SNAKE_CASE			)

									if self.embedding_proj_norm is not None:
											snake_case_		:							Dict			       =						self.embedding_proj_norm(_SCREAMING_SNAKE_CASE			)

									snake_case_		:							str			       =						self.embedding_proj(_SCREAMING_SNAKE_CASE			)
									if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
											snake_case_		:							List[Any]			       =						self.encoder_hidden_states_proj(_SCREAMING_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"""			)

									snake_case_		:							List[Any]			       =						self.proj_in(_SCREAMING_SNAKE_CASE			)

									snake_case_		:							Any			       =						self.positional_embedding.to(hidden_states.dtype			)

									snake_case_		:							Tuple			       =						[]
									snake_case_		:							Optional[int]			       =						0

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

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

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

									snake_case_		:							Optional[int]			       =						additional_embeds + [
									    proj_embeddings,
									    time_embeddings[:, None, :],
									    hidden_states,
									]

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

									snake_case_		:							List[str]			       =						torch.cat(
									    _SCREAMING_SNAKE_CASE  ,dim=1  ,)

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

									snake_case_		:							Any			       =						hidden_states + positional_embeddings

									if attention_mask is not None:
											snake_case_		:							Optional[Any]			       =						(1 - attention_mask.to(hidden_states.dtype			)) * -1_0_0_0_0.0
											snake_case_		:							List[str]			       =						F.pad(_SCREAMING_SNAKE_CASE  ,(0, self.additional_embeddings)  ,value=0.0			)
											snake_case_		:							Tuple			       =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype			)
											snake_case_		:							Union[str, Any]			       =						attention_mask.repeat_interleave(self.config.num_attention_heads  ,dim=0			)

									if self.norm_in is not None:
											snake_case_		:							Tuple			       =						self.norm_in(_SCREAMING_SNAKE_CASE			)

									for block in self.transformer_blocks:
											snake_case_		:							Any			       =						block(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE			)

									snake_case_		:							str			       =						self.norm_out(_SCREAMING_SNAKE_CASE			)

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

									snake_case_		:							Optional[int]			       =						self.proj_to_clip_embeddings(_SCREAMING_SNAKE_CASE			)

									if not return_dict:
											return (predicted_image_embedding,)

									return PriorTransformerOutput(predicted_image_embedding=_SCREAMING_SNAKE_CASE			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							str			       =						(prior_latents * self.clip_std) + self.clip_mean
									return prior_latents



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))