protobuf_to_pydantic 0.3.0.2

Generate the `pydantic.BaseModel` class (and the corresponding source code) with parameter verification function through the Protobuf file

protobuf_to_pydantic

Generate Pydantic Model or source code with parameter verification function based on Protobuf file (Proto3).

中文文档

Feature

Feature：

• Generate source code through Protobuf plugin。
• Generate Pydantic Model or source code by parsing Protobuf Message in Python runtime.
• Compatible with V1 and V2 versions of Pydantic。
• Supports multiple verification rules and is compatible with proto-gen-validate (subsequent versions will support the rules of proto-gen-validate 1.0)。
• Support custom functionality through templates。
• Supports protovalidate verification rules（proto-gen-validate version >= 1.0）

The following is a functional overview diagram of protobuf-to-pydantic. In the picture P2P represents protobuf-to-pydantic, Protoc represents the command for Protobuf to generate code, and plugin represents Plugin for Protoc:

Installation

By default, protobuf-to-pydantic can be installed directly via the following command:

pip install protobuf_to_pydantic

If want to use the full functionality of protobuf-to-pydantic, can install protobuf-to-pydantic with the following command:.

pip install protobuf_to_pydantic[all]

Usage

1.code generation

protobuf-to-pydantic currently has two methods to generate Pydantic Model objects based on Protobuf files.：

• 1: Plugin Mode: Use the Protoc plug-in to generate the corresponding Python code file through the Protobuf file。
• 2: Runtime Mode: Generate the corresponding Pydantic Model object through the Message object in Python runtime。

1.1.Plugin Mode

1.1.0.Install dependencies

The protobuf-to-pydantic plug-in depends on mypy-protobuf, need to install mypy-protobuf through the following command first:

python -m pip install protobuf-to-pydantic[mypy-protobuf]

or

poetry add protobuf-to-pydantic -E mypy-protobuf

1.1.1.Use plugins

Plug-in is the Pydantic Model source code generation method recommended by protobuf-to-pydantic. It supports the most complete functions and is also very simple to use.

Assume that it is usually generated through the following command Code corresponding to Protobuf file:

python -m grpc_tools.protoc -I. example.proto
# or
protoc -I. --python_out=. example.proto

After installing protobuf-to-pydantic,can use the protobuf-to-pydantic plugin with the --protobuf-to-pydantic_out option with the following command:

python -m grpc_tools.protoc -I. --protobuf-to-pydantic_out=. example.proto
# or
protoc -I. --protobuf-to-pydantic_out=. example.proto

In this command, --protobuf-to-pydantic_out=. means using the prorobuf-to-pydantic plug-in, And it is declared that the output location of the protobuf-to-pydantic plug-in is .

. indicates the output path used by grpc_tools.proto.

After running the command, the protobuf-to-pydantic plugin writes the generated source code to a file with the filename suffix p2p.py, e.g., example.proto generates a file with the name example_p2p.py.

1.1.2.Plug-in configuration

The protobuf-to-pydantic plugin supports loading configuration by reading a Python file。

In order to ensure that the variables of the configuration file can be introduced normally, the configuration file should be stored in the current path where the command is run.

An example configuration that can be read by 'protobuf_to_pydantic' would look like:

import logging
from typing import List, Type

from google.protobuf.any_pb2 import Any  # type: ignore
from pydantic import confloat, conint
from pydantic.fields import FieldInfo

from protobuf_to_pydantic.template import Template

# Configure the log output format and log level of the plugin, which is very useful when debugging
logging.basicConfig(
  format="[%(asctime)s %(levelname)s] %(message)s",
  datefmt="%y-%m-%d %H:%M:%S",
  level=logging.DEBUG
)


class CustomerField(FieldInfo):
  pass


def customer_any() -> Any:
  return Any  # type: ignore


# For the configuration of the local template, see the use of the local template for details
local_dict = {
  "CustomerField": CustomerField,
  "confloat": confloat,
  "conint": conint,
  "customer_any": customer_any,
}
# Specifies the start of key comments
comment_prefix = "p2p"
# Specify the class of the template, can extend the template by inheriting this class, see the chapter on custom templates for details
template: Type[Template] = Template
# Specify the protobuf files of which packages to ignore, and the messages of the ignored packages will not be parsed
ignore_pkg_list: List[str] = ["validate", "p2p_validate"]
# Specifies the generated file name suffix (without .py)
file_name_suffix = "_p2p"

Next, in order to be able to read this file, need to change the --protobuf-to-pydantic_out=. to --protobuf-to-pydantic_out=config_path=plugin_config.py:.. where the left side of : indicates that the configuration file path to be read is plugin_config.py, and the right side of : declares that the output location of the protobuf-to-pydantic plugin is . The final complete command is as follows：

python -m grpc_tools.protoc -I. --protobuf-to-pydantic_out=config_path=plugin_config.py:. example.proto
# or
protoc -I. --protobuf-to-pydantic_out=config_path=plugin_config.py:. example.proto

Through this command, can load the corresponding configuration and run the protobuf-to-pydantic plug-in。

In addition to the configuration options in the example configuration file, the protobuf-to-pydantic plug-in also supports other configuration options. The specific configuration instructions are as follows：

Configuration name	Functional module	Type	Hidden meaning
local_dict	Template	dict	Holds variables for the local template
template	Template	protobuf_to_pydantic.template.Template	Implementation of the template class
comment_prefix	Template	str	Comment prefix.Only strings with a fixed prefix will be used by the template
parse_comment	comment(plugin only)	bool	If true, the annotation rule is compatible
customer_import_set	Code generation	Set[str]	A collection of custom import statements, such as from typing import Setor import typing, that will write data in order to the source code file
customer_deque	Code generation	deque[str]	Custom source file content, used to add custom content
module_path	str	str	Used to define the root path of the project or module, which helps protobuf-to-pydanticto better automatically generate module import statements
pyproject_file_path	Code generation	str	Define the pyproject file path, which defaults to the current project path
code_indent	Code generation	int	Defines the number of indentation Spaces in the code; the default is 4
ignore_pkg_list	Code generation(plugin only)	list[str]	Definition ignores parsing of the specified package file
base_model_class	Model Code generation, Code generation	Type[BaseModel]	Define the parent class of the generated Model
file_name_suffix	Code generation	str	Define the generated file suffix, default _p2p.py
file_descriptor_proto_to_code	Code generation(plugin only)	Type[FileDescriptorProtoToCode]	Define the FileDescriptorProtoToCode to use
protobuf_type_config	Code generation(plugin only)	Dict[str, ProtobufTypeConfigModel]	Compatible with non-standard ones Message, SeeConfigModel note
pkg_config	Code generation(plugin only)	Dict[str, "ConfigModel"]	Adapt the corresponding configuration for each PKG

Note:

• 1:For more information, see the configuration instructions/protobuf_to_pydantic/plugin/config.py
• 2:See for directions of use/example/plugin_config.py

1.1.3.buf-cli

If you are using buf-cli to manage Protobuf files, then you can also use protobuf-to-pydantic in buf-cli, See How to use protobuf-to-pydantic in buf-cli

1.2.Runtime Mode

protobuf_to_pydantic can generate the corresponding PydanticModel object based on the Message object at runtime。

For example, the UserMessage in the following Protobuf file named demo.proto:

// path: ./demo.proto
syntax = "proto3";
package user;

enum SexType {
  man = 0;
  women = 1;
}

message UserMessage {
  string uid=1;
  int32 age=2;
  float height=3;
  SexType sex=4;
  bool is_adult=5;
  string user_name=6;
}

protoc can be used to generate the Python code file corresponding to the Protobuf file (the file name is demo_pb2.py), and the code related to the UserMessage is stored in the code file.

At Python runtime, The func msg_to_pydantic_model can be called to read the UserMessage object from the demo_pb2 module and generate the corresponding Pydantic Model object as follows:

from typing import Type
from protobuf_to_pydantic import msg_to_pydantic_model
from pydantic import BaseModel

# import protobuf gen python obj
from . import demo_pb2

UserModel: Type[BaseModel] = msg_to_pydantic_model(demo_pb2.UserMessage)
print(
    {
        k: v.field_info
        for k, v in UserModel.__fields__.items()
    }
)

# output
# {
#   `uid`: FieldInfo(default=``, extra={}),
#   `age`: FieldInfo(default=0, extra={}),
#   `height`: FieldInfo(default=0.0, extra={}),
#   `sex`: FieldInfo(default=0, extra={}),
#   `is_adult`: FieldInfo(default=False, extra={}),
#   `user_name`: FieldInfo(default=``, extra={})
#  }

Through the output results, it can be found that the generated pydantic.BaseModel object also contains uid, age, height, sex, is adult and user name fields, and the default property matches the zero value of the Protobuf type。

The msg_to_pydantic_model func is customizable just like plugins, with the following extension parameters:

Fields	Meaning
default_field	Generate a Field for each field in the Pydantic Model
comment_prefix	The prefix of a comment that can be parsed
parse_msg_desc_method	Parsing rules to use
local_dict	Variables used by the local template
pydantic_base	Generates the parent class of the Pydantic Model object
pydantic_module	Generate the Module of the Pydantic Model object
template	Template class to use
message_type_dict_by_type_name	Protobuf type mapping to Python type
message_default_factory_dict_by_type_name	Protobuf type mapping to the Python type factory

Among them, parse_msg_desc_method defines the rule information where protobuf_to_pydantic obtains the Message object.

1.2.1.parse_msg_desc_method

By default, the value of parse_msg_desc_method is empty. In this case, protobuf_to_pydantic obtains the parameter validation rules through the Option of the Message object.

If the parameter validation rules are declared through comments, then protobuf_to_pydantic can only obtain the parameter validation rules through the other.

• 1:The value of parse_msg_desc_method is the Python module corresponding to Message

  In this case, protobuf-to-pydantic can obtain additional information about each field in the Message object through the comments in the .pyi file corresponding to the Python module during the running process. For example, in the above sample code, the Python module corresponding to demo_pb2.UserMessage is demo_pb2.

  Note: This feature requires the use of the mypy-protobuf plug-in when generating the corresponding Python code from the Protobuf file, and the specified pyi file output path must be the same as the generated Python code path to take effect. Before execution, please install protobuf-to-pydantic using the python -m pip install protobuf-to-pydantic[mypy-protobuf] command

• 2:The value of parse_msg_desc_method is the path to the Protobuf file

  In addition to obtaining comments through .pyi files, protobuf-to-pydantic also supports obtaining information about each field through comments in the Protobuf file to which the Message object belongs. Using this feat is very simple. Just set the value of parse_msg_desc_method to the root directory path specified when the Message object is generated.

  When using this method, make sure to install protobuf-to-pydantic via python -m pip install protobuf-to-pydantic[lark] and that the Protobuf files are present in your project.

  For example, the project structure of the protobuf-to-pydantic sample code is as follows:

  ./protobuf_to_pydantic/
  ├── example/
  │ ├── python_example_proto_code/
  │ └── example_proto/
  ├── protobuf_to_pydantic/
  └── /
  

  The Protobuf file is stored in the example/example_proto folder, and then run the following command in the example directory to generate the Python code file corresponding to Protobuf:

  cd example
  
  python -m grpc_tools.protoc
    --python_out=./python_example_proto_code \
    --grpc_python_out=./python_example_proto_code \
    -I. \
  # or
  protoc
    --python_out=./python_example_proto_code \
    --grpc_python_out=./python_example_proto_code \
    -I. \
  

  Then the path that needs to be filled in for parse_msg_desc_method is ./protobuf_to_pydantic/example. For example, the following sample code:

  # pydantic Version v1
  from typing import Type
  from protobuf_to_pydantic import msg_to_pydantic_model
  from pydantic import BaseModel
  
  # import protobuf gen python obj
  from example.proto_3_20_pydanticv1.example.example_proto.demo import demo_pb2
  
  UserModel: Type[BaseModel] = msg_to_pydantic_model(
      demo_pb2.UserMessage, parse_msg_desc_method="./protobuf_to_pydantic/example"
  )
  print(
      {
          k: v.field_info
          for k, v in UserModel.__fields__.items()
      }
  )
  # output
  # {
  #   'uid': FieldInfo(default=PydanticUndefined, title='UID', description='user union id', extra={'example': '10086'}),
  #   'age': FieldInfo(default=0, title='use age', ge=0, extra={'example': 18}),
  #   'height': FieldInfo(default=0.0, ge=0, le=2, extra={}),
  #   'sex': FieldInfo(default=0, extra={}),
  #   'is_adult': FieldInfo(default=False, extra={}),
  #   'user_name': FieldInfo(default='', description='user name', min_length=1, max_length=10, extra={'example': 'so1n'})
  # }
  

  As you can see, the only difference in this code is the value of parse_msg_desc_method, but the output is the same.

1.3.Generate files

In addition to generating the corresponding Pydantic Model object at runtime, protobuf-to-pydantic also supports converting Pydantic Model objects to Python code text at runtime (only compatible with Pydantic Model objects generated by protobuf-to-pydantic). The pydantic_model_to_py_code func is used to generate the source code, and the pydantic_model_to_py_file func is used to generate the code file. The example code of the pydantic_model_to_py_file func is as follows:

from protobuf_to_pydantic import msg_to_pydantic_model, pydantic_model_to_py_file

# import protobuf gen python obj
from example.example_proto_python_code.example_proto.demo import demo_pb2

pydantic_model_to_py_file(
    "./demo_gen_code.py",
    msg_to_pydantic_model(demo_pb2.NestedMessage),
)

When the code runs, it convertsdemo_pb2.NestedMessageto a Pydantic Model object and passes it to the pydantic_model_to_py_file. pydantic_model_to_py_file generates the source code and writes it to a demo_gen_code.py file.

2.Parameter validation

In the previous section, the Pydantic Model object generated by the Protobuf file is very simple because the Protobuf file does not have enough parameters to verify the relevant information. In order for each field in the generated Pydantic Model object to have parameter validation capabilities, the corresponding parameter checking rules for the field need to be refined in the Protobuf file.

Currently, protobuf-to-pydantic supports three validation rules：

• 1.Text annotations
• 2.PGV(protoc-geb-validate)
• 3.P2P

With these rules, the Pydantic Model object generated by protobuf-to-pydantic will have parameter validation feature. Among them, text annotations and P2P rules are consistent, they both support most of the parameters in Pydantic Field, some of the variations and new parameters are seen 2.4.P2P and text annotation rule other parameter support

NOTE:

• 1.Text annotation rules are not the focus of subsequent functional iterative development, and it is recommended to use P2P verification rules.
• 2.In plugin mode, annotation rules are written slightly differently, Seedemo.proto
• 3.The plugin mode automatically selects the most suitable parameter verification rule.

2.1.Text annotations

In the Protobuf file, can write annotations for each field that meet the requirements of protobuf-to-pydantic, so that protobuf-to-pydantic can obtain the validation information of the parameters when parsing the Protobuf file, such as the following example

syntax = "proto3";
package user;

enum SexType {
  man = 0;
  women = 1;
}

// user info
message UserMessage {
  // p2p: {"required": true, "example": "10086"}
  // p2p: {"title": "UID"}
  string uid=1; // p2p: {"description": "user union id"}
  // p2p: {"example": 18, "title": "use age", "ge": 0}
  int32 age=2;
  // p2p: {"ge": 0, "le": 2.5}
  float height=3;
  SexType sex=4;
  bool is_adult=5;
  // p2p: {"description": "user name"}
  // p2p: {"default": "", "min_length": 1, "max_length": "10", "example": "so1n"}
  string user_name=6;
}

In this example, each annotation that can be used by protobuf_to_pydantic starts with p2p: (supports customization) and is followed by a complete Json string. If are familiar with the usage of pydantic, can find This Json string contains the verification information corresponding to pydantic.Field. For example, the uid field in UserMessage contains a total of 4 pieces of information as follows：

Column	Meaning
required	Indicates that the generated field does not have a default value
example	An example value representing the generated field is 10086
title	Indicates that the schema name of the field is UID
description	The schema documentation for the representation field is described as user_union_id

Note:

• 1.Currently only single-line comments are supported and comments must be a complete Json data (no line breaks).

When these annotations are written, protobuf_to_pydantic will bring the corresponding information for each field when converting the Message into the corresponding Pydantic.BaseModel object, as follows:

# pydantic version V1
from typing import Type
from protobuf_to_pydantic import msg_to_pydantic_model
from pydantic import BaseModel

# import protobuf gen python obj
from example.example_proto_python_code.example_proto.demo import demo_pb2

UserModel: Type[BaseModel] = msg_to_pydantic_model(demo_pb2.UserMessage, parse_msg_desc_method=demo_pb2)
print(
    {
        k: v.field_info
        for k, v in UserModel.__fields__.items()
    }
)
# output
# {
#   `uid`: FieldInfo(default=PydanticUndefined, title=`UID`, description=`user union id`, extra={`example`: `10086`}),
#   `age`: FieldInfo(default=0, title=`use age`, ge=0, extra={`example`: 18}),
#   `height`: FieldInfo(default=0.0, ge=0, le=2, extra={}),
#   `sex`: FieldInfo(default=0, extra={}),
#   `is_adult`: FieldInfo(default=False, extra={}),
#   `user_name`: FieldInfo(default=``, description=`user name`, min_length=1, max_length=10, extra={`example`: `so1n`})
# }

It can be seen that the output fields carry the corresponding information, which is consistent with the comments of the Protobuf file.

2.2.PGV(protoc-gen-validate)

At present, the commonly used parameter verification project in the Protobuf ecosystem is protoc-gen-validate， It has become a common standard in Protobuf because it supports multiple languages and requires only one writing of PGV rules to make the generated Message object support the corresponding validation rules.

Currently protobuf-to-pydantic only supports rules that protoc-gen-validate is less than version 1.0.0

protobuf-to-pydantic supports parsing of PGV validation rules and generates Pydantic Model objects with validation logic functions. Using PGV checksum rules in protobuf-to-pydantic is very simple, just write the corresponding PGV rules in the Protobuf file first, and then specify the value of parse_msg_desc_method to be PGV when calling msg_to_pydantic_model as the code below:

# pydantic version V1
from typing import Type
from protobuf_to_pydantic import msg_to_pydantic_model
from pydantic import BaseModel

# import protobuf gen python obj
from example.proto_3_20_pydanticv1.example.example_proto.validate import demo_pb2

UserModel: Type[BaseModel] = msg_to_pydantic_model(
    demo_pb2.FloatTest, parse_msg_desc_method="PGV"
)
print(
    {
        k: v.field_info
        for k, v in UserModel.__fields__.items()
    }
)
# output
# {
#   `const_test`: FieldInfo(default=1.0, const=True, extra={}),
#   `range_e_test`: FieldInfo(default=0.0, ge=1, le=10, extra={}),
#   `range_test`: FieldInfo(default=0.0, gt=1, lt=10, extra={}),
#   `in_test`: FieldInfo(default=0.0, extra={`in`: [1.0, 2.0, 3.0]}),
#   `not_in_test`: FieldInfo(default=0.0, extra={`not_in`: [1.0, 2.0, 3.0]}),
#   `ignore_test`: FieldInfo(default=0.0, extra={})
# }

Note:

• 1.For the usage of PGV, see: protoc-gen-validate doc
• 2.There are three ways to introduce validate
  • 2.1.Install PGV through pip install protoc_gen_validate
  • 2.2.Download validate.prototo the protobuf directory in the project。
  • 2.3.Install PGV through buf-cli

2.3.P2P

The PGV verification rules are written in the Option attribute of each field of Message and have a better code specification, so Protobuf that use PGV checksum rules will be more readable than Protobuf that use annotation .

At the same time, when writing PGV rules, can also experience the convenience of the IDE's auto-completion and the security of checksumming when generating the corresponding language objects from Protobuf files, but it only supports checksumming-related logic, which is not as rich as the file annotation mode.

The P2P verification rule that comes with protobuf-to-pydantic expands on the PGV verification rule by incorporating some of the functionality of the text annotation verification rule, which satisfies most of the customization of the properties of each Field in the Pydantic Model, such as the following Protobuf file.

syntax = "proto3";
package p2p_validate_test;

import "example_proto/common/p2p_validate.proto";


message FloatTest {
  float const_test = 1 [(p2p_validate.rules).float.const = 1];
  float range_e_test = 2 [(p2p_validate.rules).float = {ge: 1, le: 10}];
  float range_test = 3[(p2p_validate.rules).float = {gt: 1, lt: 10}];
  float in_test = 4[(p2p_validate.rules).float = {in: [1,2,3]}];
  float not_in_test = 5[(p2p_validate.rules).float = {not_in: [1,2,3]}];
  float default_test = 6[(p2p_validate.rules).float.default = 1.0];
  float not_enable_test = 7[(p2p_validate.rules).float.enable = false];
  float default_factory_test = 8[(p2p_validate.rules).float.default_factory = "p2p@builtin|float"];
  float miss_default_test = 9[(p2p_validate.rules).float.miss_default = true];
  float alias_test = 10 [(p2p_validate.rules).float.alias = "alias"];
  float desc_test = 11 [(p2p_validate.rules).float.description = "test desc"];
  float multiple_of_test = 12 [(p2p_validate.rules).float.multiple_of = 3.0];
  float example_test = 13 [(p2p_validate.rules).float.example = 1.0];
  float example_factory = 14 [(p2p_validate.rules).float.example_factory = "p2p@builtin|float"];
  float field_test = 15[(p2p_validate.rules).float.field = "p2p@local|CustomerField"];
  float type_test = 16[(p2p_validate.rules).float.type = "p2p@local|confloat"];
  float title_test = 17 [(p2p_validate.rules).float.title = "title_test"];
}

protobuf-to-pydantic can read the generated Message object at runtime and generate a Pydantic Model object with the corresponding information:

# pydantic version V1
from typing import Type
from protobuf_to_pydantic import msg_to_pydantic_model
from pydantic import BaseModel, confloat
from pydantic.fields import FieldInfo

# import protobuf gen python obj
from example.proto_3_20_pydanticv1.example.example_proto.p2p_validate import demo_pb2


class CustomerField(FieldInfo):
    pass


DemoModel: Type[BaseModel] = msg_to_pydantic_model(
    demo_pb2.FloatTest,
    local_dict={"CustomerField": CustomerField, "confloat": confloat},
)
print(
    {
        k: v.field_info
        for k, v in DemoModel.__fields__.items()
    }
)
# output:
# {
#   'const_test': FieldInfo(default=1.0, const=True, extra={}),
#   'range_e_test': FieldInfo(default=0.0, ge=1, le=10, extra={}),
#   'range_test': FieldInfo(default=0.0, gt=1, lt=10, extra={}),
#   'in_test': FieldInfo(default=0.0, extra={'in': [1.0, 2.0, 3.0]}),
#   'not_in_test': FieldInfo(default=0.0, extra={'not_in': [1.0, 2.0, 3.0]}),
#   'default_test': FieldInfo(default=1.0, extra={}),
#   'default_factory_test': FieldInfo(default=PydanticUndefined, default_factory=<class 'float'>, extra={}),
#   'miss_default_test': FieldInfo(extra={}),
#   'alias_test': FieldInfo(default=0.0, alias='alias', alias_priority=2, extra={}),
#   'desc_test': FieldInfo(default=0.0, description='test desc', extra={}),
#   'multiple_of_test': FieldInfo(default=0.0, multiple_of=3, extra={}),
#   'example_test': FieldInfo(default=0.0, extra={'example': 1.0}),
#   'example_factory': FieldInfo(default=0.0, extra={'example': <class 'float'>}),
#   'field_test': CustomerField(default=0.0, extra={}),
#   'type_test': FieldInfo(default=0.0, extra={}),
#   'title_test': FieldInfo(default=0.0, title='title_test', extra={})
#   }

Note:

• 1.See the 2.5.template for the usage of local_dict
• 2.If the reference to the Proto file fails, need to download p2p_validate.proto in the project and use it in the Protobuf file。

2.4.P2P and text annotation rule other parameter support

The protobuf-to-pydantic text annotation rules and the P2P rules support most of the parameters in FieldInfo, as described in the Pydantic Field doc。

The new parameters added to Pydantic V2 will be supported in next version , for now P2P rule naming is still written on the basis of Pydantic V1, but automatic mapping to Pydantic V2 naming is supported.

Other partial changes in meaning and new parameters are described as follows:

Parameter	Default value	Illustrate
required	False	By default, the default value of each field in the generated Pydantic Model object is the same as the zero value of its corresponding type. When required is True, no more default values are generated for the fields.
enable	True	By default, protobuf-to-pydantic generates all fields for Message, if don't want the generated Message to have this field, can set enable to False.
const	None	Used to specify a constant value for a field, though different Pydantic versions behave differently For Pydantic V1, the value of default in Field is set to the value specified by const, and const in Field is set to True.Note: Pydantic Model's const only supports bool variables, when const is True, the accepted value can only be the value set by default, and the default value carried by the message generated by protobuf is the zero value of the corresponding type does not match with Pydantic Model, so protobuf-to-pydantic makes some changes to the input of this value. For Pydantic V2, the value of default in Field remains the same, but the type annotation changes to typing_extensions.Literal[xxx]
type	None	By default, the default type of a field is the same as Protobuf's, but use the 2.5.template function to modify the type of a field.
extra	None	The extra parameter accepted by Pydantic is of type Python Dict, which is not supported by Protobuf, and requires the use of either 2.5.Templates or the corresponding Json structure protobuf-to-pydantic in the Protobuf file to parse it properly.
field	None	By default, the Field of the parameter is Pydantic FieldInfo, although it can be customized using the 2.5.Templates function
default_template	None	Similar to default, default values can be customized in fields that are not of string type using the 2.5.Templates feature.

In addition to the above parameters, also support for fast import of Pydantic type for string types. For example, if want to add a check for card numbers via the pydantic.types.PaymentCardNumber type, can specify the type of the pydantic_type parameter field to be PaymentCardNumber, which is similar to the use of template imports in the type rule, as follows:

• Text annotation rules：

  syntax = "proto3";
  package common_validate_test;
  
  // common example
  message UserPayMessage {
    string bank_number=1; // p2p: {"pydantic_type": "PaymentCardNumber"}
    string other_bank_number=2; // p2p: {"type": "p2p@import|pydantic.types|PaymentCardNumber"}
  }
  

• P2P rules：

  syntax = "proto3";
  package p2p_validate_test;
  
  import "example_proto/common/p2p_validate.proto";
  // p2p example
  message UserPayMessage {
    string bank_number=1[(p2p_validate.rules).string.pydantic_type = "PaymentCardNumber"];
    string other_bank_number=2[(p2p_validate.rules).string.type = "p2p@import|pydantic.types|PaymentCardNumber"];
  }
  

See Extra Types Overview for supported `Pydantic Types'.

2.5.Template

When working with definition fields, will find that some fields are filled with values that are methods or functions of one of the libraries in Python (e.g., the values of the type parameter and the default_factory parameter), which can't be accomplished with the Json syntax. At this point, templates can be used to solve the corresponding problem, and currently protobuf-to-pydantic supports a variety of template functi

Note: The p2p string at the beginning of a template can be defined via the comment_prefix variable

2.5.1.p2p@importTemplate

The p2p@import template is used to represent variables in other modules that need to be introduced before they can be used, as follows.

• Examples of text annotation rules：

  syntax = "proto3";
  package comment_validate_test;
  
  // comment example
  message UserPayMessage {
    string bank_number=1; // p2p: {"type": "p2p@import|pydantic.types|PaymentCardNumber"}
  }
  

• Examples of P2P rules (1)：

  syntax = "proto3";
  package p2p_validate_test;
  import "example_proto/common/p2p_validate.proto";
  
  message UserPayMessage {
    string bank_number=1[(p2p_validate.rules).string.type = "p2p@import|pydantic.types|PaymentCardNumber"];
  }
  

• Examples of P2P rules (2)：

  syntax = "proto3";
  package p2p_other_validate_test;
  import "example_proto/common/p2p_validate.proto";
  // p2p other example
  message UserPayMessage {
    string bank_number=1[(p2p_validate.rules).string.pydantic_type = "PaymentCardNumber"];
  }
  

The example Protobuf file uses a syntax in the format p2p@{methods of the template}|{modules to be imported:A}|{variables in modules:B}, indicating that a B object needs to be imported by from A import B and used by the corresponding rule. With the definition of the template, protobuf-to-pydantic converts the corresponding Message into a Pydantic Model, as follows:

from pydantic import BaseModel
from pydantic.fields import FieldInfo
# p2p@import|pydantic.types|PaymentCardNumber
from pydantic.types import PaymentCardNumber

class UserPayMessage(BaseModel):
    bank_number: PaymentCardNumber = FieldInfo(default="", extra={})

2.5.2.p2p@import_instance Template

The p2p@import_instance template introduces the class of a library and then instantiates it in combination with the specified parameters before it is used by the corresponding rule, which is used as follows:

syntax = "proto3";
package p2p_validate_test;
import "google/protobuf/any.proto";
import "example_proto/common/p2p_validate.proto";
// p2p example
message AnyTest {
  google.protobuf.Any default_test = 23 [
    (p2p_validate.rules).any.default = 'p2p@import_instance|google.protobuf.any_pb2|Any|{"type_url": "type.googleapis.com/google.protobuf.Duration"}'
  ];
}

The syntax used here is p2p@{methods of the template}|{modules to be introduced}|{classes to be introduced}|{initialization parameters}, and the definition of protobuf-to-pydantic through the template will turn the corresponding Message into the following Pydantic Model object:

from google.protobuf.any_pb2 import Any as AnyMessage
from pydantic import BaseModel
from pydantic.fields import FieldInfo


class AnyTest(BaseModel):
    default_test: AnyMessage = FieldInfo(
        default=AnyMessage(type_url="type.googleapis.com/google.protobuf.Duration")
    )

2.5.3.p2p@local Template

This template is used to introduce user-defined variables, using a syntax in the format {method of the template}|{local variable to be used}, as follows:

• Example of text annotation:

  syntax = "proto3";
  package comment_validate_test;
  import "google/protobuf/timestamp.proto";
  import "example_proto/common/p2p_validate.proto";
  // comment example
  message UserPayMessage {
    google.protobuf.Timestamp exp=1; // p2p: {"default_factory": "p2p@local|exp_time"}
  }
  

• Examples of P2P rules：

  syntax = "proto3";
  package p2p_validate_test;
  import "google/protobuf/timestamp.proto";
  import "example_proto/common/p2p_validate.proto";
  // p2p example
  message UserPayMessage {
    google.protobuf.Timestamp exp=1[(p2p_validate.rules).timestamp.default_factory= "p2p@local|exp_time"];
  }
  

However, the msg_to_pydantic_model func needs to be called with the parameter local_dict to register the corresponding value, the pseudo-code is as follows:

# a.py
import time

from example.proto_3_20_pydanticv1.example.example_proto.p2p_validate import demo_pb2
from protobuf_to_pydantic import msg_to_pydantic_model


def exp_time() -> float:
  return time.time()

msg_to_pydantic_model(
    demo_pb2.NestedMessage,
    local_dict={"exp_time": exp_time},  # <----  use local_dict
)

In this way, protobuf-to-pydantic generates a conforming Pydantic Model object:

# b.py
from datetime import datetime
from pydantic import BaseModel
from pydantic.fields import FieldInfo

from a import exp_time  # <-- exp_time in a.py

class UserPayMessage(BaseModel):
    exp: datetime = FieldInfo(default_factory=exp_time, extra={})

2.5.4.p2p@builtin Template

This template (which can be thought of as a simplified version of the p2p@local template) can be used directly when the variables to be used come from Python built-in functions,the syntax is used as follows:

• Examples of text annotation rules:

  syntax = "proto3";
  package comment_validate_test;
  import "google/protobuf/timestamp.proto";
  import "example_proto/common/p2p_validate.proto";
  // comment example
  message UserPayMessage {
    google.protobuf.Timestamp exp=1; // p2p: {"type": "p2p@builtin|float"}
  }
  

• Examples of P2P rules：

  syntax = "proto3";
  package p2p_validate_test;
  import "google/protobuf/timestamp.proto";
  import "example_proto/common/p2p_validate.proto";
  // p2p example
  message UserPayMessage {
    google.protobuf.Timestamp exp=1[(p2p_validate.rules).timestamp.type= "p2p@builtin|float"];
  }
  

Then can directly generate a conforming Pydantic Model object by calling the msg_to_pydantic_model function, as follows:

from pydantic import BaseModel
from pydantic.fields import FieldInfo


class UserPayMessage(BaseModel):
    exp: float = FieldInfo()

2.5.5.Customized templates

Currently protobuf-to-pydantic only supports a few simple templates, if have more template needs, can extend the templates by inheriting the Template class.

For example, there is an odd feature that requires the default value of a field to be the timestamp of the time when the Pydantic Model object was generated, but the timestamps used are available in lengths of 10 and 13, so the following Protobuf file needs to be written to support defining the length of the timestamps:

syntax = "proto3";
package p2p_validate_test;
import "google/protobuf/timestamp.proto";
import "example_proto/common/p2p_validate.proto";

message TimestampTest{
  int32 timestamp_10 = 1[(p2p_validate.rules).int32.default_template = "p2p@timestamp|10"];
  int32 timestamp_13 = 2[(p2p_validate.rules).int32.default_template = "p2p@timestamp|13"];
}

As you can see, the Protobuf file customizes the syntax of p2p@timestamp|{x}, where x has only two values, 10 and 13. The next step is to write code based on this template behavior, which looks like this.

import time
from protobuf_to_pydantic.gen_model import Template


class CustomTemplate(Template):
  def template_timestamp(self, length_str: str) -> int:
    timestamp: float = time.time()
    if length_str == "10":
      return int(timestamp)
    elif length_str == "13":
      return int(timestamp * 100)
    else:
      raise KeyError(f"timestamp template not support value:{length_str}")


from .demo_pb2 import TimestampTest  # fake code
from protobuf_to_pydantic import msg_to_pydantic_model

msg_to_pydantic_model(
  TimestampTest,
  template=CustomTemplate  # <-- Use a custom template class
)

This code first creates a class CustomTemplate that inherits from Template. During the execution process, it is found that when the parameter verification rule starts with p2p@, the parameter will be sent to the template_{template name} method corresponding to the Template class, so CustomTemplate defines the template_timestamp method to implement the p2p@timestamp template function. In addition, the length_str variable received in this method is either 10 in p2p@timestamp|10 or 13 in p2p@timestamp|13.

Then load the CustomTemplate through the msg_to_pydantic_model function, then the following code will be generated (assuming that the code is generated at a timestamp of 1600000000):

from pydantic import BaseModel
from pydantic.fields import FieldInfo

class TimestampTest(BaseModel):
    timestamp_10: int = FieldInfo(default=1600000000)
    timestamp_13: int = FieldInfo(default=1600000000000)

Note: In plugin mode, you can declare a template class to be loaded through a configuration file.

3.Code format

The code generated directly through protobuf-to-pydantic is not perfect, but it is possible to indirectly generate code that conforms to the Python specification through different formatting tools. Currently, protobuf-to-pydantic supports formatting tools such as autoflake, black and isort. If the corresponding formatting tool is installed in the current Python environment, then protobuf-to-pydantic will call the tool to format the generated code before outputting it to a file.

In addition, the decision to enable or disable a formatting tool can be made through the pyproject.toml configuration file, the pyproject.toml example of which reads as follows:

# Controls which formatting tools protobuf-to-pydantic uses,
# if false then no formatting tools are used (default is true)
[tool.protobuf-to-pydantic.format]
black = true
isort = true
autoflake = true

# black docc:https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html#configuration-format
[tool.black]
line-length = 120
target-version = ['py37']

# isort doc:https://pycqa.github.io/isort/docs/configuration/config_files.html#pyprojecttoml-preferred-format
[tool.isort]
profile = "black"
multi_line_output = 3
include_trailing_comma = true
force_grid_wrap = 0
use_parentheses = true
ensure_newline_before_comments = true
line_length = 120

# autoflake doc:https://github.com/PyCQA/autoflake#configuration
[tool.autoflake]
in-place = true
remove-all-unused-imports = true
remove-unused-variables = true

4.example

protobuf-to-pydantic provides some simple example code for reference only.

4.1.Generate code directly

Protobuf file: demo/demo.proto

Generate Pydantic Model(Pydantic V1): proto_pydanticv1/demo_gen_code.py

Generate Pydantic Model(Pydantic V2): proto_pydanticv2/demo_gen_code.py

4.2.Text annotation

Protobuf File: demo/demo.proto

Pydantic Model generated based on pyi file(Pydantic V1): proto_pydanticv1/demo_gen_code_by_text_comment_pyi.py

Pydantic Model generated based on pyi file(Pydantic V2): proto_pydanticv2/demo_gen_code_by_text_comment_pyi.py

Pydantic Model generated based on protobuf file(Pydantic V1): proto_pydanticv1/demo_gen_code_by_text_comment_protobuf_field.py Pydantic Model generated based on protobuf file(Pydantic V2): proto_pydanticv2/demo_gen_code_by_text_comment_protobuf_field.py validate/demo.proto](https://github.com/so1n/protobuf_to_pydantic/blob/master/example/example_proto/validate/demo.proto)

Generate Pydantic Model(Pydantic V1): proto_pydanticv1/demo_gen_code_by_pgv.py

Generate Pydantic Model(Pydantic V2): proto_pydanticv2/demo_gen_code_by_pgv.py

4.4.P2P rule

Protobuf file: p2p_validate/demo.proto

Generate Pydantic Model(Pydantic V1): proto_pydanticv1/demo_gen_code_by_p2p.py

Generate Pydantic Model(Pydantic V2): proto_pydanticv2/demo_gen_code_by_p2p.py

4.5.Protoc Plugin-in

Protobuf field:

• demo/demo.proto
• validate/demo.proto
• p2p_validate/demo.proto
• p2p_validate_by_comment/demo.proto

Pydantic Model generated via demo/demo.proto(Pydantic V1):example_proto/demo/demo_p2p.py

Pydantic Model generated via demo/demo.proto(Pydantic V2):example_proto/demo/demo_p2p.py

Pydantic Model generated via validate/demo.proto(Pydantic V1):example_proto/validate/demo_p2p.py

Pydantic Model generated via validate/demo.proto(Pydantic V2):example_proto/validate/demo_p2p.py

Pydantic Model generated via p2p_validate/demo.proto(Pydantic V1):example_proto/p2p_validate/demo_p2p.py

Pydantic Model generated via p2p_validate/demo.proto(Pydantic V2):example_proto/p2p_validate/demo_p2p.py

Pydantic Model generated via p2p_validate_by_comment/demo.proto(Pydantic V1):example/example_proto/p2p_validate_by_comment

Pydantic Model generated via p2p_validate_by_comment/demo.proto(Pydantic V2):example/example_proto/p2p_validate_by_comment