Upload standard.py with huggingface_hub
Browse files- standard.py +79 -5
standard.py
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from typing import List
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from .card import TaskCard
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from .dataclass import InternalField
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from .formats import ICLFormat
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from .instructions import Instruction
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from .operator import SourceSequntialOperator, StreamingOperator
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from .recipe import Recipe
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from .renderers import StandardRenderer
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from .schema import ToUnitxtGroup
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from .templates import Template
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class
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card: TaskCard
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template: Template = None
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instruction: Instruction = None
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format: ICLFormat = ICLFormat()
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demos_pool_size: int = None
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num_demos: int =
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demos_pool_name: str = "demos_pool"
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demos_taken_from: str = "train"
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steps: List[StreamingOperator] = InternalField(default_factory=list)
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def prepare(self):
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self.steps = [
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self.card.loader,
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)
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)
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if self.num_demos
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sampler = self.card.sampler
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if self.sampler is not None:
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)
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)
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render = StandardRenderer(
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instruction=self.instruction,
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template=self.template,
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)
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class StandardRecipeWithIndexes(
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instruction_card_index: int = None
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template_card_index: int = None
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self.instruction = self.card.instructions[int(self.instruction_card_index)]
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super().prepare()
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from typing import List
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from .card import TaskCard
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from .dataclass import InternalField, OptionalField
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from .formats import ICLFormat
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from .instructions import Instruction
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from .operator import SourceSequntialOperator, StreamingOperator
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from .operators import StreamRefiner
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from .recipe import Recipe
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from .renderers import StandardRenderer
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from .schema import ToUnitxtGroup
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from .templates import Template
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class BaseRecipe(Recipe, SourceSequntialOperator):
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card: TaskCard
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template: Template = None
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instruction: Instruction = None
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format: ICLFormat = ICLFormat()
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max_train_instances: int = None
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max_validation_instances: int = None
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max_test_instances: int = None
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train_refiner: StreamRefiner = OptionalField(default_factory=lambda: StreamRefiner(apply_to_streams=["train"]))
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validation_refiner: StreamRefiner = OptionalField(
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default_factory=lambda: StreamRefiner(apply_to_streams=["validation"])
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)
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test_refiner: StreamRefiner = OptionalField(default_factory=lambda: StreamRefiner(apply_to_streams=["test"]))
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demos_pool_size: int = None
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num_demos: int = 0
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demos_pool_name: str = "demos_pool"
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demos_taken_from: str = "train"
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steps: List[StreamingOperator] = InternalField(default_factory=list)
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def verify(self):
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super().verify()
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if self.num_demos > 0:
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if self.demos_pool_size is None or self.demos_pool_size < 1:
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raise ValueError(
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"When using demonstrations both num_demos and demos_pool_size should be assigned with postive integers."
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)
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if self.demos_pool_size < self.num_demos:
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raise ValueError(
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f"demos_pool_size must be bigger than num_demos={self.num_demos}, Got demos_pool_size={self.demos_pool_size}"
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)
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def prepare(self):
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self.steps = [
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self.card.loader,
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)
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)
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if self.num_demos > 0:
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sampler = self.card.sampler
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if self.sampler is not None:
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)
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)
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self.train_refiner.max_instances = self.max_train_instances
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self.steps.append(self.train_refiner)
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self.validation_refiner.max_instances = self.max_validation_instances
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self.steps.append(self.validation_refiner)
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self.test_refiner.max_instances = self.max_test_instances
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self.steps.append(self.test_refiner)
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render = StandardRenderer(
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instruction=self.instruction,
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template=self.template,
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)
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class StandardRecipeWithIndexes(BaseRecipe):
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instruction_card_index: int = None
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template_card_index: int = None
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self.instruction = self.card.instructions[int(self.instruction_card_index)]
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super().prepare()
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class StandardRecipe(StandardRecipeWithIndexes):
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"""
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This class represents a standard recipe for data processing and preperation.
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This class can be used to prepare a recipe
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with all necessary steps, refiners and renderers included. It allows to set various
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parameters and steps in a sequential manner for preparing the recipe.
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Attributes:
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card (TaskCard): TaskCard object associated with the recipe.
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template (Template, optional): Template object to be used for the recipe.
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instruction (Instruction, optional): Instruction object to be used for the recipe.
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format (ICLFormat, optional): ICLFormat object to be used for the recipe.
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train_refiner (StreamRefiner, optional): Train refiner to be used in the recipe.
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max_train_instances (int, optional): Maximum training instances for the refiner.
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validation_refiner (StreamRefiner, optional): Validation refiner to be used in the recipe.
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max_validation_instances (int, optional): Maximum validation instances for the refiner.
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test_refiner (StreamRefiner, optional): Test refiner to be used in the recipe.
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max_test_instances (int, optional): Maximum test instances for the refiner.
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demos_pool_size (int, optional): Size of the demos pool.
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num_demos (int, optional): Number of demos to be used.
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demos_pool_name (str, optional): Name of the demos pool. Default is "demos_pool".
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demos_taken_from (str, optional): Specifies from where the demos are taken. Default is "train".
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demos_field (str, optional): Field name for demos. Default is "demos".
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sampler (Sampler, optional): Sampler object to be used in the recipe.
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steps (List[StreamingOperator], optional): List of StreamingOperator objects to be used in the recipe.
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instruction_card_index (int, optional): Index of instruction card to be used
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for preparing the recipe.
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template_card_index (int, optional): Index of template card to be used for
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preparing the recipe.
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Methods:
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prepare(): This overridden method is used for preparing the recipe
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by arranging all the steps, refiners, and renderers in a sequential manner.
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Raises:
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AssertionError: If both template and template_card_index, or instruction and instruction_card_index
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are specified at the same time.
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"""
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pass
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