flow_preprocessing.preprocessing_logic.config.PreprocessorConfig

class flow_preprocessing.preprocessing_logic.config.PreprocessorConfig(*, huggingface_target_repo_name, huggingface_target_repo_private=False, append=False, export_mode='line', crop=False, allow_empty_lines=False, batch_size=32, augmentation_loops=None, min_width_line=None, min_height_line=None, split_train_ratio=None, split_seed=42, split_shuffle=True, segment=None, segmenter_config=None, huggingface_token=None)

Extended configuration for the preprocessor.

Parameters:

• huggingface_target_repo_name (str)

• huggingface_target_repo_private (bool | None)

• append (bool | None)

• export_mode (str | None)

• crop (bool | None)

• allow_empty_lines (bool | None)

• batch_size (int | None)

• augmentation_loops (Annotated[int | None, Ge(ge=0)])

• min_width_line (Annotated[int | None, Gt(gt=0)])

• min_height_line (Annotated[int | None, Gt(gt=0)])

• split_train_ratio (Annotated[float | None, Gt(gt=0.0), Le(le=1.0)])

• split_seed (Annotated[int, Ge(ge=0)])

• split_shuffle (bool | None)

• segment (Literal['yolo', 'kraken'] | None)

• segmenter_config (SegmenterConfig | SegmenterBaseConfig | dict | None)

• huggingface_token (SecretStr | None)

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
requires_xml_parsing	Check if the export mode requires XML parsing.
huggingface_token
huggingface_target_repo_name
huggingface_target_repo_private
append
export_mode
crop
allow_empty_lines
batch_size
augmentation_loops
min_width_line
min_height_line
split_train_ratio
split_seed
split_shuffle
segment
segmenter_config