vision_unlearning.evaluator.text_to_image
=========================================

.. py:module:: vision_unlearning.evaluator.text_to_image


Attributes
----------

.. autoapisummary::

   vision_unlearning.evaluator.text_to_image.logger


Classes
-------

.. autoapisummary::

   vision_unlearning.evaluator.text_to_image.EvaluatorTextToImage


Functions
---------

.. autoapisummary::

   vision_unlearning.evaluator.text_to_image.evaluate_painting_style
   vision_unlearning.evaluator.text_to_image.log_validation
   vision_unlearning.evaluator.text_to_image.plot_gradient_conflict_hist


Module Contents
---------------

.. py:data:: logger

.. py:class:: EvaluatorTextToImage(/, **data: Any)

   Bases: :py:obj:`pydantic.BaseModel`


   !!! abstract "Usage Documentation"
       [Models](../concepts/models.md)

   A base class for creating Pydantic models.

   .. attribute:: __class_vars__

      The names of the class variables defined on the model.

   .. attribute:: __private_attributes__

      Metadata about the private attributes of the model.

   .. attribute:: __signature__

      The synthesized `__init__` [`Signature`][inspect.Signature] of the model.

   .. attribute:: __pydantic_complete__

      Whether model building is completed, or if there are still undefined fields.

   .. attribute:: __pydantic_core_schema__

      The core schema of the model.

   .. attribute:: __pydantic_custom_init__

      Whether the model has a custom `__init__` function.

   .. attribute:: __pydantic_decorators__

      Metadata containing the decorators defined on the model.
      This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.

   .. attribute:: __pydantic_generic_metadata__

      A dictionary containing metadata about generic Pydantic models.
      The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__]
      and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias],
      and the `parameter` item maps to the `__parameter__` attribute of generic classes.

   .. attribute:: __pydantic_parent_namespace__

      Parent namespace of the model, used for automatic rebuilding of models.

   .. attribute:: __pydantic_post_init__

      The name of the post-init method for the model, if defined.

   .. attribute:: __pydantic_root_model__

      Whether the model is a [`RootModel`][pydantic.root_model.RootModel].

   .. attribute:: __pydantic_serializer__

      The `pydantic-core` `SchemaSerializer` used to dump instances of the model.

   .. attribute:: __pydantic_validator__

      The `pydantic-core` `SchemaValidator` used to validate instances of the model.

   .. attribute:: __pydantic_fields__

      A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.

   .. attribute:: __pydantic_computed_fields__

      A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

   .. attribute:: __pydantic_extra__

      A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
      is set to `'allow'`.

   .. attribute:: __pydantic_fields_set__

      The names of fields explicitly set during instantiation.

   .. attribute:: __pydantic_private__

      Values of private attributes set on the model instance.


   .. py:attribute:: model_config

      Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].


   .. py:attribute:: pipeline_original
      :type:  Optional[diffusers.StableDiffusionPipeline]


   .. py:attribute:: pipeline_learned
      :type:  Optional[diffusers.StableDiffusionPipeline]


   .. py:attribute:: pipeline_unlearned
      :type:  diffusers.StableDiffusionPipeline


   .. py:attribute:: prompts_forget
      :type:  List[str]


   .. py:attribute:: prompts_retain
      :type:  List[str]


   .. py:attribute:: metric_clip
      :type:  vision_unlearning.metrics.MetricImageTextSimilarity


   .. py:attribute:: compute_runtimes
      :type:  bool
      :value: True



   .. py:attribute:: plot_show
      :type:  bool
      :value: True



   .. py:method:: evaluate() -> Tuple[List[huggingface_hub.repocard_data.EvalResult], Dict[str, PIL.Image.Image]]


.. py:function:: evaluate_painting_style(metadata: List[Dict[str, str]], metric_painting_style: vision_unlearning.metrics.MetricPaintingStyle, dataset_path: str, device: str) -> dict

   @param metadata: list of dictionaries with keys "file_name" and "text"; follows this schema: Follows this schema: https://huggingface.co/docs/datasets/v2.4.0/en/image_load#image-captioning
   @return metrics (as float, not yet as EvalResult)
   Compute metrics from already generated images


.. py:function:: log_validation(pipeline, accelerator, epoch, num_validation_images, validation_prompt, seed, is_final_validation=False) -> Dict[str, PIL.Image.Image]

   Adapted from The HuggingFace Inc. team. All rights reserved.
   Licensed under the Apache License, Version 2.0.
   Source: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py


.. py:function:: plot_gradient_conflict_hist(similarities: List[float], title: str, color: str) -> PIL.Image.Image