{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#default_exp core.script"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"from local.core.foundation import *\n",
"from local.core.utils import *\n",
"from local.core.imports import *\n",
"from local.test import *\n",
"\n",
"from argparse import ArgumentParser"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from local.notebook.showdoc import show_doc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"_all_ = ['Param']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## call_parse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def _param_pre(self): return '--' if self.opt else ''\n",
"def _param_kwargs(self): return {k:v for k,v in self.__dict__.items() if v is not None and k!='opt'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"mk_class('Param', help=None, type=None, opt=True, action=None, nargs=None, const=None, choices=None, required=None,\n",
" pre=property(_param_pre), kwargs=property(_param_kwargs),\n",
" doc=\"A parameter in a function used in `anno_parser` or `call_parse`\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> Param(**\\*`args`**, **\\*\\*`kwargs`**)\n",
"\n",
"A parameter in a function used in [`anno_parser`](/script.html#anno_parser) or [`call_parse`](/script.html#call_parse)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Param, title_level=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'help': 1}"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = Param(opt=True, help=1)\n",
"p.kwargs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def anno_parser(func):\n",
" \"Look at params (annotated with `Param`) in func and return an `ArgumentParser`\"\n",
" p = ArgumentParser(description=func.__doc__)\n",
" for k,v in inspect.signature(func).parameters.items():\n",
" param = func.__annotations__.get(k, Param())\n",
" kwargs = param.kwargs\n",
" if v.default != inspect.Parameter.empty: kwargs['default'] = v.default\n",
" p.add_argument(f\"{param.pre}{k}\", **kwargs)\n",
" return p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f1(p1:Param(\"h1\", str)='1', p2:Param(\"h2\", float)=1e-3):\n",
" \"mydoc\"\n",
" pass\n",
"\n",
"t = anno_parser(f1)\n",
"h = t.parse_args(['--p1', 'a'])\n",
"test_eq(h.p1, 'a')\n",
"test_eq(h.p2, 1e-3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def call_parse(func):\n",
" \"Decorator to create a simple CLI from `func` using `anno_parser`\"\n",
" name = inspect.currentframe().f_back.f_globals['__name__']\n",
" if name == \"__main__\":\n",
" args = anno_parser(func).parse_args()\n",
" func(**args.__dict__)\n",
" else: return func"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted 00_test.ipynb.\n",
"Converted 01_core.ipynb.\n",
"Converted 01a_utils.ipynb.\n",
"Converted 01b_dispatch.ipynb.\n",
"Converted 01c_transform.ipynb.\n",
"Converted 02_script.ipynb.\n",
"Converted 03_torch_core.ipynb.\n",
"Converted 04_dataloader.ipynb.\n",
"Converted 05_data_core.ipynb.\n",
"Converted 06_data_transforms.ipynb.\n",
"Converted 07_vision_core.ipynb.\n",
"Converted 08_pets_tutorial.ipynb.\n",
"Converted 09_vision_augment.ipynb.\n",
"Converted 10_data_block.ipynb.\n",
"Converted 11_layers.ipynb.\n",
"Converted 11a_vision_models_xresnet.ipynb.\n",
"Converted 12_optimizer.ipynb.\n",
"Converted 13_learner.ipynb.\n",
"Converted 14_callback_schedule.ipynb.\n",
"Converted 14a_callback_data.ipynb.\n",
"Converted 15_callback_hook.ipynb.\n",
"Converted 15a_vision_models_unet.ipynb.\n",
"Converted 16_callback_progress.ipynb.\n",
"Converted 17_callback_tracker.ipynb.\n",
"Converted 18_callback_fp16.ipynb.\n",
"Converted 19_callback_mixup.ipynb.\n",
"Converted 20_metrics.ipynb.\n",
"Converted 21_vision_learner.ipynb.\n",
"Converted 22_tutorial_imagenette.ipynb.\n",
"Converted 23_tutorial_transfer_learning.ipynb.\n",
"Converted 30_text_core.ipynb.\n",
"Converted 31_text_data.ipynb.\n",
"Converted 32_text_models_awdlstm.ipynb.\n",
"Converted 33_text_models_core.ipynb.\n",
"Converted 34_callback_rnn.ipynb.\n",
"Converted 35_tutorial_wikitext.ipynb.\n",
"Converted 36_text_models_qrnn.ipynb.\n",
"Converted 37_text_learner.ipynb.\n",
"Converted 38_tutorial_ulmfit.ipynb.\n",
"Converted 40_tabular_core.ipynb.\n",
"Converted 41_tabular_model.ipynb.\n",
"Converted 42_tabular_rapids.ipynb.\n",
"Converted 50_data_block_examples.ipynb.\n",
"Converted 60_medical_imaging.ipynb.\n",
"Converted 65_medical_text.ipynb.\n",
"Converted 90_notebook_core.ipynb.\n",
"Converted 91_notebook_export.ipynb.\n",
"Converted 92_notebook_showdoc.ipynb.\n",
"Converted 93_notebook_export2html.ipynb.\n",
"Converted 94_notebook_test.ipynb.\n",
"Converted 95_index.ipynb.\n",
"Converted 96_data_external.ipynb.\n",
"Converted 97_utils_test.ipynb.\n",
"Converted notebook2jekyll.ipynb.\n"
]
}
],
"source": [
"#hide\n",
"from local.notebook.export import notebook2script\n",
"notebook2script(all_fs=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}