Pydantic custom field. My Model: from pydantic import BaseModel.

Pydantic custom field. The field argument is no longer available.

Pydantic custom field. the second argument is the field value to validate; it can be named as you please Jan 28, 2022 · you could use a Pydantic model like this one: from pydantic import BaseModel. Pydantic V1 validator signature¶ Oct 24, 2023 · from typing import Annotated from pydantic import BaseModel, Field, field_validator from pydantic. fields: dict[str, str] = {} That way, any number of fields could be processed, while the field type would be validated, e. The root value can be passed to the model __init__ via the __root__ keyword argument, or as the first and only argument to parse Validators. Pydantic provides functionality to serialize model in three ways: To a Python dict made up of the associated Python objects. foobar ), models can be converted, dumped, serialized, and exported in a number of ways. Provide details and share your research! But avoid …. delta_seconds_float: timedelta # export as float Configuration with dataclass from the standard library or TypedDict. mypy pydantic. errors pydantic. str / int / bool Aug 5, 2020 · My thought was then to define the _key field as a @property-decorated function in the class. deprecated backport, or a boolean. The idea is to register additional constructors via model_validator and a custom decorator to map the right constructor based on the keyword arguments passed by the user. Literal prior to Python 3. I'm retrieving data from an api on jobs (dummy example below) and need to map the fields to a Pydantic model. In this example, User is a Pydantic model with three fields: name, age, and is_active. project_id='id' project_name=None project_type=None depot='newdepot' system=None. 8 as well. It will look like this: from abc import ABC. If using the dataclass from the standard library or TypedDict, you should use __pydantic_config__ instead. functional_serializers pydantic. However, as can be seen above, pydantic will attempt to 'match' any of the types defined under Union and will use the first one that matches. exclude=True, title="val". The problem is that the keys in the dictionary are different from the names of the model fields. (Implicit decorators, be gone!) This is helpful if your validation is specific to a field — or if you want share this validation between modules. type_: type. Otherwise pydantic will try to validate the string as a Float first before passing it to the custom validator which converts empty string to None. You can specify an alias in the following ways: alias on the Field. Dec 24, 2022 · I'm trying to build a custom field in Fastapi-users pydantic schema as follows: class UserRead(schemas. Jun 3, 2020 · If you're reusing the validator, probably better to implement as a custom data type. from pydantic import BaseModel, validator class PleaseCoorperate(BaseModel): self0: str next0: str @validator('self0') def self0_math_test(cls, v): # v set the values passed for self0 # your math here return new_self0 @validator('next0', always=True) # always if you want to run it even when next0 is not passed Jun 22, 2021 · As of 2023 (almost 2024), by using the version 2. 9. @field_serializer; @model_serializer; PlainSerializer; WrapSerializer; Serialization can be customised on a field using the @field_serializer decorator, and on a model using the @model_serializer decorator. The root type can be any type supported by Pydantic, and is specified by the generic parameter to RootModel. 8; prior to Python 3. Of course, only because Pydanitic is involved. In that case, the generator will be consumed and stored on the model as a list and its values will be validated against the type parameter of the Sequence (e. datetime. A deprecation message, an instance of warnings. With the Timestamp situation, consider that these two examples are effectively the same: Foo(bar=Timestamp("never!!1")) and Foo(bar="never!!1"). Use ellipsis () to indicate the field is Mar 1, 2022 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. PositiveInt module-attribute. But you can use the following trick: Note: This is a suboptimal solution. from pydantic import BaseModel class TextInfo ( BaseModel ): big_text_field: str = "a" * 1000 info = TextInfo () whether to allow infinity (+inf an -inf) and NaN values to float fields, defaults to True, set to False for compatibility with JSON, see #3994 for more details, added in V1. Another way to look at it is to define the base as optional and then create a validator to check when all required: from pydantic import BaseModel, root For a timestamp to parse into a field of type date, the time components must all be zero: It is also raised when using pydantic. type: int. json import timedelta_isoformat. TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. ) however, the advantage of adding excluded parameters in the Config class seems to be that you can get the list of excluded parameters with. class JsonData(BaseModel): ts: int. . Apr 14, 2023 · I am playing with the custom field types in v2 and found that there no hook to allow the custom field type to access the annotations of the field: import dataclasses from typing import Annotated, Any from pydantic import BaseModel, ConfigDict, Field from pydantic_core import core_schema @dataclasses. Datetime Types¶. You can force them to run with Field(validate_default=True). To exclude a field you can also use exclude in Field: from pydantic import BaseModel, Field. I've reused custom validators for more complex validations. alias_generators import to_pascal class Item(BaseModel): model_config = ConfigDict(alias_generator=to_pascal, populate_by_name=True) name: str language_code: str If one would like to create a Field alias without using an alias_generator, they could achieve this as follows: While pydantic uses pydantic-core internally to handle validation and serialization, it is a new API for Pydantic V2, thus it is one of the areas most likely to be tweaked in the future and you should try to stick to the built-in constructs like those provided by annotated-types, pydantic. config pydantic. Oct 27, 2023 · # The model will compute: bar=123 # Field validation and serialization rules apply baz=456 # Field validation and serialization rules apply # Such that: print(bar) #> Bar='123' print(baz) #> Baz='456' Question--> Is this possible to do using @computed_field, or is there another recommended way to do this? Aug 1, 2023 · With Annotated Validators, you can create a validator on a field (i. Models are simply classes which inherit from pydantic. Apr 3, 2021 · You can use pydantic validators. Beyond accessing model attributes directly via their field names (e. type: Literal['INSIDE', 'OUTSIDE Oct 4, 2021 · As of the pydantic 2. Pydantic ignore extra fields is a feature of the pydantic library that allows you to ignore extra fields when validating a data model. The root type can be any type supported by pydantic, and is specified by the type hint on the __root__ field. But since the BaseModel has an implementation for __setattr__, using setters for a @property doesn't work for me. from pydantic. int in Sequence[int]). Oct 10, 2022 · Here's the code: selected_card: CardIdentifier # just my enum. One of the primary ways of defining schema in Pydantic is via models. g. x of Pydantic and Pydantic-Settings (remember to install it), you can just do the following: from pydantic import BaseModel, root_validator from pydantic_settings import BaseSettings class CarList(BaseModel): cars: List[str] colors: List[str] class CarDealership(BaseModel): name: str cars: CarList @root_validator def check_length(cls, v): cars This means that in the health response pydantic class, - If you create robot_serial in the proper way to have a pydantic field that can be either a string or null but must always be passed in to the constructor - annotation Optional[str] and do not provide a default - then pydantic will say there's a field missing if you explicitly pass in null Apr 16, 2023 · I have a pydantic model that I want to dynamically exclude fields on. Jan 4, 2024 · from pydantic import BaseModel class User(BaseModel): name: str age: int is_active: bool = True. But my googling and searching in the docs didn't Feb 3, 2022 · def composite_name(self): return f"{self. Once you start adding things like private fields, its going against that mindset. RootModel and custom root types¶ Pydantic models can be defined with a "custom root type" by subclassing pydantic. age: Optional[int] This applies both to @field_validator validators and Annotated validators. x (old answer) The current version of pydantic does not support creating jsonable dict straightforwardly. (Python >= 3. I wrote this code, but it doesn't work. class Mdl(BaseModel): val: str = Field(. Please use the info parameter instead. Maybe in a slightly more verbose form (like "use data types if validation is always bound to certain type (e. alias_generators pydantic. In addition you have solved the question, which default to use, when the field is not provided by the user. Asking for help, clarification, or responding to other answers. However, you are generally better off using a @model_validator(mode='before') where the function is Nov 17, 2022 · 1. Jul 10, 2022 · Performance. config, but it is a dictionary instead of an object like it was in Pydantic V1. Pydantic offers out-of-the-box support for data validation and serialization. manager = 1. class TMDB_Category(BaseModel): name: str = Field(validation_alias="strCategory") description: str = Field(validation_alias="strCategoryDescription") Serialization alias can be set with serialization_alias. UUID class (which is defined under the attribute's Union annotation) but as the uuid. dataclass class MyClass : a: str b: bool Nov 1, 2023 · Pydanticを使用することで、Pythonコードでのデータバリデーションとデータシリアライゼーションを簡単かつ効率的に行うことができます。 この記事では、Pydanticの基本的な使い方から、より高度なバリデーションとシリアライゼーションまで幅広く紹介します。 But required and optional fields are properly differentiated only since Python 3. ignore. Setting validate_default to True has the closest behavior to using always=True in validator in Pydantic v1. @validator("type_", pre=True) def parse_cls(cls, value: object) -> type: An alias is an alternative name for a field, used when serializing and deserializing data. color pydantic. Given the code below, it appears that the validators are not called when using the parse_* methods. 11) This works with FastAPI and the generated OpenAPI schema will reflect it properly. class Person(BaseModel): name: constr(min_length=1) Both seem to perform the same validation (even raise the exact same exception info when name is an empty string). The @validate_call decorator allows the arguments passed to a function to be parsed and validated using the function's annotations before the function is called. See the Conversion Table for more details on how Pydantic converts data in both strict and lax modes. Nov 19, 2021 · I thought about this and it perhaps might indeed be the best solution. Jul 15, 2022 · The Config. 10 Change behaviour globally¶ If you wish to change the behaviour of pydantic globally, you can create your own custom BaseModel with custom Config since the config is Custom Root Types¶ Pydantic models can be defined with a custom root type by declaring the __root__ field. from pydantic import BaseModel. Nov 24, 2022 · I wanna add a custom property for certain fields and be able to pull all field names with particular value for that property. NamedTuple Similar to tuple, but creates instances of the given namedtuple class. fields pydantic. Literal (or typing_extensions. 1. Nov 6, 2022 · from pydantic import BaseModel, ConfigDict from pydantic. Sep 23, 2021 · 7. FieldValidationInfo. Aug 19, 2021 · coordinates: list[list[list[float]]] this is taken from a json schema where the most inner array has maxItems=2, minItems=2. Pydantic uses the terms "serialize" and "dump" interchangeably. This is a new feature of the Python standard library as of Python 3. This can be useful when you are working with data that may contain unexpected fields, or when you want to allow users to extend your data model with their own custom fields. 0. Custom serializers¶ Pydantic provides several functional serializers to customise how a model is serialized to a dictionary or JSON. However, Pydantic does not seem to register those as model fields. deprecated or the typing_extensions. Jul 6, 2023 · Pydantic 1. The issue you are experiencing relates to the order of which pydantic executes validation. parse_raw(""". define custom validators as needed. Field, or BeforeValidator and so on. Types, custom field types, and constraints (like max_length) are mapped to the corresponding spec formats in the following priority order (when there is an equivalent available): JSON Schema Core; JSON Schema Validation; OpenAPI Data Types; The standard format JSON field is used to define Pydantic extensions for more complex string sub-types. The types module contains custom types used by pydantic. Dec 16, 2021 · from pydantic import BaseModel, Field. : class MyModel(BaseModel): fie Aug 11, 2021 · I'm having trouble getting pydantic to deserialize it correctly for some use cases. setting this in the field is working only on the outer level of the list. coordinates: list[list[list[float]]] = Field(maxItems=2, minItems=2) Jan 5, 2022 · 9. class Employee(BaseModel): name: str. model. It seems this values argument is no longer passed as the @field_validator signature has changed. I wanted to define a pydantic schema for A then this is what I did: class ASchema(BaseModel): categories: list[str] @field_serializer("categories") def serialize_categories(self, categories, _info): return ["t1", "t2"] I would say my intention is pretty straightforward, during the serialization, I want that categories that is a Apr 17, 2022 · Is it possible to return a list of validation errors for a specific field, without having separate validators for the same field? It would be nice to get all errors back in 1 shot for the field, instead of having to get separate responses back for each failed validation. – Jan 8, 2021 · Pydantic 1. The type for "fluffy" and "tiger" are Animal , however when deserializing the "bob" the Person , his pet is the correct Dog type. class Person(BaseModel): name: str = Field(, min_length=1) And: from pydantic import BaseModel, constr. from pydantic import BaseModel, ValidationError, validator class UserModel(BaseModel): name: str username: str password1: str password2: str @validator('name') def name_must_contain_space(cls, v): if from pydantic import BaseModel, ConfigDict class Model(BaseModel): model_config = ConfigDict(strict=True) name: str age: int. In the code below you only need the Config allow_population_by_field_name if you also want to instantiate the object with the original thumbnail. Both refer to the process of converting a model to a dictionary or JSON-encoded string. In the above example the id of user_03 was defined as a uuid. class Model(BaseModel): the_id: UUID = Field(default_factory=uuid4) Sep 24, 2023 · from pydantic import BaseModel from typing import Union class MyModel(BaseModel): my_field: Union[CarModel, BikeModel] # How to use custom validators here? I would like to know how to define the my_field in my Pydantic model to accept both car and bike models as valid input types and apply the respective custom validation classes. If you have a generator you want to validate, you can still use Sequence as described above. The root value can be passed to the model __init__ or model_validate as via the first and only argument. RootModel. seconds (if >= -2e10 and <= 2e10) or milliseconds (if < -2e10or > 2e10) since 1 January 1970 Mar 25, 2024 · Enter Pydantic, a popular data validation and serialization library. from pydantic import BaseModel, Schema class Files(BaseModel): '''Class for file URLs''' urls: Set[str] = Schema(, title='Urls to files') This will restrict the request body to set of urls defined as str. This is useful for parsing csv files into objs Beta Was this translation helpful? Customisation — Pydantic allows custom validators and serializers to alter how data is processed in many powerful ways. from typing import Union, Literal. type) directly. Meaning you can: leverage Python’s type hints to validate fields, use the custom fields and built-in validators Pydantic offers, and. datetime ¶. The types of Jul 15, 2022 · One of its fields must be supplied by user, however, the second one can be present but it is totally okay if it is missing. must be a str; validation_alias on the Field. While under the hood this uses the same approach of model creation and initialisation (see Validators for more details), it provides an extremely easy way to from typing import Pattern import re from pydantic import BaseModel, Field class Rule ( BaseModel) : # this is not how filed works, but you get the idea pattern: Pattern = Field ( init_func=lambda x: re. @field_validators are "class methods", so the first argument value they receive is the UserModel class, not an instance of UserModel. # state of the game, has info on which player has which cards on hand. IPvAnyNetwork: allows either an IPv4Network or an IPv6Network. NamedTuple¶ subclasses of typing. validators should either return the parsed value or raise a Sep 25, 2019 · I'm using a pydantic model to represent a request body in fastapi. StrictBool = Annotated[ bool, Strict ()] A boolean that must be either True or False. UUID]): twitter_account: Optional['TwitterAccount'] On UserRead validation from pydantic import BaseModel,Field, validator class Blog(BaseModel): title: str = Field(,min_length=5) is_active: bool @validator("title") def validate_no_sql_injection(cls, value): if "delete from" in value: raise ValueError("Our terms strictly prohobit SQLInjection Attacks") return value Blog(title="delete from",is_active=True) # Output Mar 22, 2022 · I'm trying to figure out how to validate and transform data within a Pydantic model. A custom validator with pre=True will allow you to attempt to find a class with the provided name. 8, it requires the typing-extensions package. class BaseModel(PydanticBaseModel): class Config: arbitrary_types_allowed = True. Custom validation and complex relationships between objects can be achieved using the validator decorator. However, some default behavior of stdlib dataclasses may prevail. We recommend you use the @classmethod decorator on them below the @field_validator decorator to get proper type checking. allow_population_by_field_name = True. delta_seconds_int: timedelta # export as int in seconds. Pydantic supports the following datetime types:. 10: how to add custom validation with @validate_arguments if options which provides Field is not sufficient 0 Pydantic: Field with regex param ends with AttributeError: 'str' object has no attribute 'match' Apr 4, 2023 · Your examples with int and bool are all correct, but there is no Pydantic in play. 7 and above. the third argument is an instance of pydantic. networks Jul 7, 2021 · Here's a bit of code as an example: from datetime import datetime, timedelta. See Strict Mode for more details. from dataclasses import dataclass from datetime import datetime from pydantic import ConfigDict @dataclass class User: __pydantic_config__ = ConfigDict(strict=True) id May 17, 2021 · This is of course in conflict with the Optional, but it looks like pydantic gives higher priority to . IPvAnyInterface: allows either an IPv4Interface or an IPv6Interface. must be a str; alias_generator on the Config Aug 23, 2023 · Description. I can do this by overriding the dict function on the model so it can take my custom flag, e. My Model: from pydantic import BaseModel. {. populate_by_name=True, For pydantic 1. last_name}" This is half-way through, as it won't allow assignment of composite_name (which would trigger a routine splitting it into first_name and last_name ). A recommendation such as this would be nice to see in the docs. functional_validators pydantic. When working with text data it's common to have awkward reprs. The json is converted to a Python dictionary first. 8) as a lightweight way to specify that a field may accept only specific literal values: Dec 14, 2022 · 3. These are perfect candidate for your solution. class Example(BaseModel): delta_iso: timedelta # export using timedelta_isoformat. IPvAnyAddress: allows either an IPv4Address or an IPv6Address. And vice versa. I couldn't find a way to set a validation for this in pydantic. 8. Given that date format has its own core schema (ex: will validate a timestamp or similar conversion), you will want to execute your validation prior to the core validation. Can someone tell me the best way to do this. Validation Decorator. Since the Field replaces the field's default, this first argument can be used to set the default. dataclasses and extra=forbid: Sep 19, 2021 · In Pydantic, you can use aliases for this. In case of missing age, I don't want it to be present on pydantic model instance at all. The V2 plan mentions May 3, 2021 · One reason why you might want to have a specific class (as opposed to an instance of that class) as the field type is when you want to use that field to instantiate something later on using that field. IGNORECASE )) Rule ( pattern=r'my regex') # works, and will ignore case. However, the migration docs don't mention a values equivalent in v2 and the v2 validator documentation page has not yet been updated for v2. Documentation. types returned from collections. UUID can be marshalled into an int it chose to match against the int type and disregarded the other types. Looking at the pydantic-core benchmarks today, pydantic V2 is between 4x and 50x faster than pydantic V1. NamedTuple, but since field types are not specified, all fields are treated as having type Any Pydantic provides types for IP addresses and networks, which support the standard library IP address, interface, and network types. You need to change alias to have validation_alias. str ), use validators if you need to target several types (e. It essentially extends the answer by @James P to handle all alternative init methods in __init__ and allow for an arbitrary number of those: May 28, 2020 · I don't know if this justifies the use of pydantic here's what I want to use pydantic for: Use a set of Fileds for internal use and expose them via @property decorators; Set the value of the fields from the @property setters. json_encoders mechanism in the current pydantic is not as useful for this, because it requires that every model that includes the custom field type also includes its JSON encocder in its config. Pydantic Pydantic pydantic pydantic. Here’s a simple example of a custom type, PositiveNumber, which only accepts positive numbers as input: Body - Fields¶ The same way you can declare additional validation and metadata in path operation function parameters with Query, Path and Body, you can declare validation and metadata inside of Pydantic models using Pydantic's Field. datetime; an existing datetime object; int or float; assumed as Unix time, i. e. 0 release, this behaviour has been updated to use model_config populate_by_name option which is False by default. pydantic supports the use of typing. You can access the configuration via info. class Group(Enum): user = 0. Jun 10, 2021 · Not answering the question directly, but it's related. Infinite Generators¶. : # No "extra" fields; yields an empty dict: data = JsonData. __fields__ returns ModelField without switchable. As a result of the move to Rust for the validation logic (and significant improvements in how validation objects are structured) pydantic V2 will be significantly faster than pydantic V1. datetime fields will accept values of type:. compile ( x, re. Models share many similarities with Python's Four signatures are supported: - `(self, value: Any, info: FieldSerializationInfo)` - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)` - `(value: Any, info: SerializationInfo)` - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)` Args: fields: Which field(s) the method should be If you want to make environment variable names case-sensitive, you can set the case_sensitive config setting: from pydantic_settings import BaseSettings, SettingsConfigDict class Settings(BaseSettings): model_config = SettingsConfigDict(case_sensitive=True) redis_host: str = 'localhost'. To a Python dict made up only of "jsonable" types. Python 3. StrictBool module-attribute. If True, a default deprecation message will be emitted when accessing the field. If you want to bind an enum to a pydantic model without relying on its value, you can create an enum on the fly. thumbnail: Optional["str"] = Field(None, alias="thumbnailUrl") class Config: Jul 20, 2023 · values: a dict containing the name-to-value mapping of any previously-validated fields. As you see cls. We therefore recommend using typing-extensions with Python 3. dataclasses pydantic. If the goal is to validate one field by using other (already validated) fields of the parent and child class, the full signature of the validation function is def validate_something(cls, field_value, values, field, config) (the argument names values,field and config must match) where the value of the fields can be accessed with the field name May 22, 2020 · Running this gives: project_id='id' project_name='name' project_type='type' depot='depot' system='system'. Jan 5, 2024 · type: str. However, I was hoping to rely on pydantic's built-in validation methods as much as I could, while simultaneously learning a bit more about using class attributes with pydantic models (and @dataclass, which I assume would have similar behaviour). var_name: int = Field(alias='var_alias') model_config = ConfigDict(. If you only use thumbnailUrl when creating the object you don't need it: id: str. Moreover, the attribute must actually be named key and use an alias (with Field( alias="_key"), as pydantic treats underscore-prefixed fields as internal and does not expose them. In all three modes, the output can be customized by excluding specific fields, excluding unset fields, excluding default values, and excluding None The field and config parameters are not available in Pydantic V2. @eudoxos You are right the above answer only works for computed read-only properties. Pydantic is best used for having 1:1 representations in code of what represents the serialized data. They will fail or succeed identically. You can think of models as similar to structs in languages like C, or as the requirements of a single endpoint in an API. 2), the most canonical way to distinguish models when parsing in a Union (in case of ambiguity) is to explicitly add a type specifier Literal. Sep 25, 2021 · As of today (pydantic v1. BaseModel and define fields as annotated attributes. The task is to make a validator for two dependent fields. Here is a working example first trying to grab a built-in and failing that assuming the class is in global namespace: name: str. Serialization. Sep 13, 2023 · Fully Customized Type. BaseUser[uuid. Here's an example: from pydantic import BaseModel from typing import Optional, Type class Foo(BaseModel): # x is NOT optional x: int class Bar Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand Jan 21, 2021 · However, as can be seen above, pydantic will attempt to 'match' any of the types defined under Union and will use the first one that matches. Learn more… Ecosystem — around 8,000 packages on PyPI use Pydantic, including massively popular libraries like FastAPI , huggingface , Django Ninja , SQLModel , & LangChain . If MCC is empty, then INSIDE should be passed in the type field. PositiveInt = Annotated[ int, Gt( )] An integer that must be greater than zero. To a JSON string. json_schema pydantic. From what I can see in the docs, we can switch on or off the repr for a field, but not modify it. Import Field¶ First, you have to import it: Jan 18, 2024 · from pydantic import BaseModel class Tag(BaseModel): id: int name: str color: str = "red" This should declare the field as not required and would not break the validation either. extra = Extra. @validator('selected_card') def player_has_card_on_hand(cls, v, values, config, field): # To tell whether the player has card on hand, I need access to my <GameInstance> object which tracks entire. @classmethod. can be an instance of str, AliasPath, or AliasChoices; serialization_alias on the Field. Jun 20, 2020 · I want to change the validation message from pydantic model class, code for model class is below: class Input(BaseModel): ip: IPvAnyAddress @validator("ip&quot;, always=True) def Mar 5, 2023 · @NirBrachel You still could, but you would need to provide a custom json encoder to the class which does the filtering for you. functional_validators import AfterValidator class ComputerModel(BaseModel): brand: str storage: int @field_validator('storage', mode='after') @classmethod def check_storage(cls, storage: int): allowed = (128, 256, 512, 1000, 1024, 2000, 2048) if validators are "class methods", so the first argument value they receive is the UserModel class, not an instance of UserModel. For example, any extra fields present on a Pydantic dataclass using extra='allow' are omitted when the dataclass is print ed. If MCC is not empty, then you need to check that OUTSIDE is passed in the type field. From the documentation of Field: default: (a positional argument) the default value of the field. Pydantic dataclasses support extra configuration to ignore, forbid, or allow extra fields passed to the initializer. namedtuple Similar to subclass of typing. first_name} {self. the second argument is the field value to validate; it can be named as you please. The field argument is no longer available. x, you need to use allow_population_by_field_name model config option. wq lg wb oy jl qp lj pp it rp