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[misc] upgrade format to py39 (#7256)

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hoshi-hiyouga
2025-03-12 00:08:41 +08:00
committed by GitHub
parent 5995800bce
commit 264538cb26
113 changed files with 984 additions and 1407 deletions
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37
src/llamafactory/webui/control.py
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@@ -14,7 +14,7 @@
import json
import os
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Optional
from transformers.trainer_utils import get_last_checkpoint
@@ -39,8 +39,7 @@ if is_gradio_available():
def can_quantize(finetuning_type: str) -> "gr.Dropdown":
r"""
Judges if the quantization is available in this finetuning type.
r"""Judge if the quantization is available in this finetuning type.
Inputs: top.finetuning_type
Outputs: top.quantization_bit
@@ -52,8 +51,7 @@ def can_quantize(finetuning_type: str) -> "gr.Dropdown":
def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
r"""
Gets the available quantization bits.
r"""Get the available quantization bits.
Inputs: top.quantization_method
Outputs: top.quantization_bit
@@ -68,9 +66,8 @@ def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
return gr.Dropdown(choices=available_bits)
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Tuple[List[str], bool]:
r"""
Modifys states after changing the training stage.
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> tuple[list[str], bool]:
r"""Modify states after changing the training stage.
Inputs: train.training_stage
Outputs: train.dataset, train.packing
@@ -78,9 +75,8 @@ def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Tuple
return [], TRAINING_STAGES[training_stage] == "pt"
def get_model_info(model_name: str) -> Tuple[str, str]:
r"""
Gets the necessary information of this model.
def get_model_info(model_name: str) -> tuple[str, str]:
r"""Get the necessary information of this model.
Inputs: top.model_name
Outputs: top.model_path, top.template
@@ -88,9 +84,8 @@ def get_model_info(model_name: str) -> Tuple[str, str]:
return get_model_path(model_name), get_template(model_name)
def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> Tuple[str, "gr.Slider", Dict[str, Any]]:
r"""
Gets training infomation for monitor.
def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> tuple[str, "gr.Slider", dict[str, Any]]:
r"""Get training infomation for monitor.
If do_train is True:
Inputs: top.lang, train.output_path
@@ -110,7 +105,7 @@ def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> Tup
trainer_log_path = os.path.join(output_path, TRAINER_LOG)
if os.path.isfile(trainer_log_path):
trainer_log: List[Dict[str, Any]] = []
trainer_log: list[dict[str, Any]] = []
with open(trainer_log_path, encoding="utf-8") as f:
for line in f:
trainer_log.append(json.loads(line))
@@ -143,8 +138,7 @@ def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> Tup
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
r"""
Lists all available checkpoints.
r"""List all available checkpoints.
Inputs: top.model_name, top.finetuning_type
Outputs: top.checkpoint_path
@@ -166,8 +160,7 @@ def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
def list_config_paths(current_time: str) -> "gr.Dropdown":
r"""
Lists all the saved configuration files.
r"""List all the saved configuration files.
Inputs: train.current_time
Outputs: train.config_path
@@ -182,8 +175,7 @@ def list_config_paths(current_time: str) -> "gr.Dropdown":
def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
r"""
Lists all available datasets in the dataset dir for the training stage.
r"""List all available datasets in the dataset dir for the training stage.
Inputs: *.dataset_dir, *.training_stage
Outputs: *.dataset
@@ -195,8 +187,7 @@ def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_S
def list_output_dirs(model_name: Optional[str], finetuning_type: str, current_time: str) -> "gr.Dropdown":
r"""
Lists all the directories that can resume from.
r"""List all the directories that can resume from.
Inputs: top.model_name, top.finetuning_type, train.current_time
Outputs: train.output_dir