159 lines
4.6 KiB
Python
159 lines
4.6 KiB
Python
from abc import ABC, abstractmethod
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from enum import Enum
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from typing import Union, List, TypedDict, Tuple
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from torch import Tensor
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from torch.nn import Embedding
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from transformers.models.clip.modeling_clip import CLIPTextTransformer
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from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
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class ActionArity(Enum):
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NONE = 0
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SINGLE = 1
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MULTI = 2
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class PostModifiers(TypedDict):
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"""
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A dictionary of post modifiers for an action result.
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"""
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position_embed_scale: Union[float, None]
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bypass_pos_embed: Union[bool, None]
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class Action(ABC):
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@property
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@abstractmethod
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def chars(self) -> Union[List[str], None]:
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pass
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@property
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@abstractmethod
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def arity(self) -> ActionArity:
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"""
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Determines the arity of the action. This is used to determine how many arguments the action supports.
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:return:
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"""
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pass
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@property
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@abstractmethod
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def display_name(self) -> str:
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pass
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@property
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@abstractmethod
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def action_name(self) -> str:
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pass
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@property
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@abstractmethod
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def description(self) -> str:
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pass
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@property
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def usage_examples(self) -> List[str]:
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return []
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@property
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@abstractmethod
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def grammar(self) -> str:
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"""
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The grammar for this action. This is used to parse the action from the prompt.
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:return:
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"""
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pass
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@abstractmethod
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def token_length(self) -> int:
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"""
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The length of the tokens that this action will add to the prompt.
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:return:
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"""
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pass
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@abstractmethod
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def get_all_segments(self) -> List[PromptSegment]:
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"""
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Get all segments, including nested segments.
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:return:
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"""
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pass
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@abstractmethod
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def __init__(self, *args, **kwargs) -> None:
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"""
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Initialize the action. This is called when the action is parsed from the prompt.
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:param args: The arguments for the action.
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"""
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pass
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@abstractmethod
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def get_result(self, embedding_module: Embedding) -> Union[Tensor, Tuple[Tensor, PostModifiers]]:
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"""
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Get the result of this action. This is called when the embeddings are being calculated.
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:param embedding_module: The embedding module to use to get the base embeddings for tokens.
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:return:
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"""
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pass
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def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
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"""
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For actions that need access to the TextTransformer, this method is called. Results are expected to be returned via get_result still.
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:param transformer: An instance of CLIPTextTransformer
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"""
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pass
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def depth_repr(self, depth: int = 1) -> str:
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raise NotImplementedError()
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class SingleArgAction(Action, ABC):
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arity = ActionArity.SINGLE
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def get_all_segments(self) -> List[PromptSegment]:
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segments = []
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for seg_or_action in self.arg:
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if isinstance(seg_or_action, Action):
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segments.extend(seg_or_action.get_all_segments())
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else:
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segments.append(seg_or_action)
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return segments
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def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
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for seg_or_action in self.arg:
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if isinstance(seg_or_action, Action):
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seg_or_action.process_with_transformer(transformer, embedding_module)
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def __init__(self, arg: List[Union[PromptSegment, Action]]):
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# TODO: Target is a list now... what does this mean for us..
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self.arg = arg
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def __repr__(self) -> str:
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return f"{self.display_name}({self.arg})"
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class MultiArgAction(Action, ABC):
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arity = ActionArity.MULTI
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def get_all_segments(self) -> List[PromptSegment]:
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segments = []
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for arg in self.all_args:
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for seg_or_action in arg:
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if isinstance(seg_or_action, Action):
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segments.extend(seg_or_action.get_all_segments())
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else:
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segments.append(seg_or_action)
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return segments
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def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
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for arg in self.all_args:
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for seg_or_action in arg:
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if isinstance(seg_or_action, Action):
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seg_or_action.process_with_transformer(transformer, embedding_module)
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def __init__(
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self,
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args: List[List[Union[PromptSegment, Action]]],
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):
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self.all_args = args
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