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Author: [Patrick Prunty](https://pprunty.github.io/pprunty/).

`motormongo` - An Object Document Mapper
for [MongoDB](https://www.mongodb.com) built on-top of [Motor](https://github.com/mongodb/motor), the MongoDB
recommended asynchronous Python driver for MongoDB Python applications, designed to work with Tornado or
asyncio and enable non-blocking access to MongoDB.

Asynchronous operations in a backend system, built using [FastAPI](https://github.com/tiangolo/fastapi) for
example, enhances performance and scalability by enabling non-blocking, concurrent handling of multiple requests,
leading to more efficient use of server resources.

The interface for instantiating Document classes follows similar logic
to [mongoengine](https://github.com/MongoEngine/mongoengine), enabling ease-of-transition and
migration from `mongoengine` to `motormongo`.

**Note:** I am currently working on patching any bugs in the latest releases, please contact me or create a GitHub issue
for
any bugs you may find (try upgrading if you encounter any issues as the bug may already have been fixed in subsequent
version). Thank you 😎.

1. [Installation](#installation)
2. [Quickstart](#quickstart)
3. [motormongo Fields](#motormongo-fields)
4. [CRUD classmethods](#class-methods)
5. [CRUD instance methods](#instance-methods)
6. [Aggregation Operations](#aggregation)
7. [FastAPI integration](#fastapi-integration)
8. [License](#license)

## Installation

To install motormongo, you can use `pip` inside your virtual environment:

```shell
python -m pip install motormongo
```

Alternatively, to install motormongo into your `poetry` environment:

```shell
poetry add motormongo
```

## Quickstart

### Step 1. Create a motormongo client:

```python
import asyncio
from motormongo import DataBase


async def init_db():
    # This 'connect' method needs to be called inside of an async function
    await DataBase.connect(uri="<mongo_uri>", database="<mongo_database>")


if __name__ == "__main__":
    asyncio.run(init_db())
```

or, in a FastAPI application:

```python
from fastapi import FastAPI
from motormongo import DataBase

app = FastAPI()


@app.on_event("startup")
async def startup_db_client():
    await DataBase.connect(uri="<mongo_uri>", db="<mongo_database>")


@app.on_event("shutdown")
async def shutdown_db_client():
    await DataBase.close()
```

The `mongo_uri` should look something like this:

```text
mongodb+srv://<username>:<password>@<cluster>.mongodb.net
```

and `database` should be the name of an existing MongoDB database in your MongoDB instance.

For details on how to set up a local or cloud MongoDB database instance,
see [here](https://www.mongodb.com/cloud/atlas/lp/try4?utm_source=google&utm_campaign=search_gs_pl_evergreen_atlas_general_prosp-brand_gic-null_emea-ie_ps-all_desktop_eng_lead&utm_term=using%20mongodb&utm_medium=cpc_paid_search&utm_ad=p&utm_ad_campaign_id=9510384711&adgroup=150907565274&cq_cmp=9510384711&gad_source=1&gclid=Cj0KCQiAyeWrBhDDARIsAGP1mWQ6B0kPYX9Tqmzku-4r-uUzOGL1PKDgSTlfpYeZ0I6HE3C-dgh1xF4aArHqEALw_wcB).

### Step 2. Define a motormongo Document:

Define a motormongo `User` document:

```python
import re
import bcrypt
from motormongo import Document, BinaryField, StringField


def hash_password(password) -> bytes:
    # Example hashing function
    return bcrypt.hashpw(password.encode('utf-8'), salt=bcrypt.gensalt())


class User(Document):
    username = StringField(help_text="The username for the user", min_length=3, max_length=50)
    email = StringField(help_text="The email for the user", regex=re.compile(r'^\S+@\S+\.\S+$'))  # Simple email regex
    password = BinaryField(help_text="The hashed password for the user", hash_function=hash_password)

    def verify_password(self, password: str) -> bool:
        """ Utility function which can be used to validate user's salted password later...
        
        ex.     user = await User.find_one({"_id": request.user_id})
                is_authenticated = user.verify_password(request.password)
        """
        return bcrypt.checkpw(password.encode("utf-8"), self.password)

    class Meta:
        collection = "users"  # < If not provided, will default to class name (ex. User->user, UserDetails->user_details)
        created_at_timestamp = True  # < Provide a DateTimeField for document creation
        updated_at_timestamp = True  # < Provide a DateTimeField for document updates
```

### Step 3: Create a MongoDB document using the User class

```python
import bcrypt

await User.insert_one(
    {
        "username": "johndoe",
        "email": "johndoe@portmarnock.ie",
        "password": "password123"
        # < hash_functon will hash the string literal password and store binary field in the database
    }
)
```

### Step 4: Validate user was created in your MongoDB collection

You can do this by using [MongoDB compass]() GUI, or alternatively, add a query to find all documents in the user
collection after doing the insert in step 3:

```python
users = User.find_many({})
if users:
    print("User collection contains the following documents:")
    for user in users:
        print(user.to_dict())
else:
    print("User collection failed to update! Check your MongoDB connection details and try again!")
```

### Step 5: Put all the code above into one file and run it

```shell
python main.py
```

or in a FastAPI application:

```shell
uvicorn main:app --reload
```

Please refer to [FastAPI Documentation](https://fastapi.tiangolo.com/tutorial/) for more details on how to get setup
with FastAPI.

## Congratulations 🎉

You've successfully created your first motormongo Object Document Mapper class. 🥳

The subsequent sections detail the datatype fields provided by motormongo, as well as the CRUD
operations available on the classmethods and object instance methods of a motormongo document.

If you wish to get straight into how to integrate motormongo with your `FastAPI` application, skip ahead to the
[FastAPI Integration](#fastapi-integration) section.

## motormongo Fields

`motormongo` supports a variety of field types to accurately define the schema of your MongoDB documents. Each field
type is designed to handle specific data types and validations:

- [BinaryField](#binaryfield): Stores binary data, useful for storing encoded or hashed data like passwords.
- [BooleanField](#booleanfield): Stores boolean values (`True` or `False`).
- [DateTimeField](#datetimefield): Manages date and time, with options for automatically setting current date/time on
  creation or update.
- [EmbeddedDocumentField](#embeddeddocumentfield): For fields that should contain values from a predefined enumeration.
- [EnumField](#enumfield): For fields that should contain values from a predefined enumeration.
- [FloatField](#floatfield): Handles floating-point numbers, with options to specify minimum and maximum values.
- [GeoJSONField](#geojsonfield): Manages geographical data in GeoJSON format, with an option to return data as JSON.
- [IntegerField](#integerfield): Manages integer data, allowing specifications for minimum and maximum values.
- [ListField](#listfield): Handles lists of items, which can be of any type.
- [ReferenceField](#referencefield): Creates a reference to another document.
- [StringField](#stringfield): Handles string data with options for minimum and maximum length, and regex validation.

### BinaryField

The `BinaryField` is used for storing binary data in the database, with support for encoding, hashing, and decoding.

**Parameters:**

- `hash_function`: (Optional) A callable for hashing input strings to bytes.
- `return_decoded`: (Optional) If `True`, decodes binary data when retrieved.
- `encode`: (Optional) Function to encode a string to bytes. Defaults to UTF-8 encoding.
- `decode`: (Optional) Function to decode bytes back to a string. Defaults to UTF-8 decoding.

**Example Usage:**

```python
from motormongo import Document, BinaryField, StringField
import bcrypt


def hash_password(password: str) -> bytes:
    return bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt())


class User(Document):
    username = StringField(min_length=3, max_length=50)
    password = BinaryField(hash_function=hash_password, return_decoded=False)
    
    def verify_password(self, password: str) -> bool:
        return bcrypt.checkpw(password.encode("utf-8"), self.password)


# Create a user with a hashed password
user = User(username="johndoe", password="secret")
inserted_user = await user.save()
is_authenticated = inserted_user.verify_password("wrongpassword") # Will return False
is_authenticated = inserted_user.verify_password("secret") # Will return True
```

### BooleanField

The `BooleanField` is used for storing boolean values (True or False). It ensures that the data stored in this field is
strictly boolean.

**Parameters:**

- There are no specific parameters unique to BooleanField other than those inherited from the base Field class.

```python
from motormongo import Document, BooleanField, StringField


class Product(Document):
    name = StringField(min_length=1, max_length=100)
    is_available = BooleanField(default=False)


# Create a product indicating its availability
product = Product(name="Gadget", is_available=True)
await product.save()
```

### DateTimeField

The `DateTimeField` handles date and time values, with options to automatically update these values on document creation
or modification.

**Parameters:**

- `auto_now`: Automatically update the field to the current datetime when the document is saved.
- `auto_now_add`: Automatically set the field to the current datetime when the document is created.
- `datetime_formats`: List of string formats to parse datetime strings.

**Example Usage:**

```python
from motormongo import Document, DateTimeField


class Event(Document):
    start_time = DateTimeField(auto_now_add=True)


# Create an event with the current start time
event = Event()
await event.save()
```

### EmbeddedDocumentField

The `EmbeddedDocumentField` is used for embedding documents within a document, supporting nested document structures.
This field allows you to include complex data structures as part of your document.

**Parameters:**

- `document_type`: The class of the embedded document, which must be a subclass of `EmbeddedDocument`, `BaseModel` from
  Pydantic, or `dict` representation of the `EmbeddedDocument`.

**Example Usage:**

```python
from motormongo import Document, EmbeddedDocument, EmbeddedDocumentField, StringField
from pydantic import BaseModel

class Address(EmbeddedDocument):
    street = StringField()
    city = StringField()


class User(Document):
    name = StringField()
    address = EmbeddedDocumentField(document_type=Address)

class PydanticAddress(BaseModel):
    street: str 
    city: str

# Create a user with an embedded address document
user = User(name="John Doe", address={"street": "123 Elm St", "city": "Springfield"})
# user = User(name="John Doe", address=Address(street="123 Elm St", city="Springfield")) # Also works
# user = User(name="John Doe", address=PydanticAddress(street="123 Elm St", city="Springfield")) # Also works
await user.save()
```

### EnumField

The `EnumField` is designed to store enumerated values, allowing for validation against a predefined set of options.

**Parameters:**

- `enum`: The enumeration class that defines valid values for the field.

**Example Usage:**

```python
import enum
from motormongo import Document, EnumField


class UserStatus(enum.Enum):
    ACTIVE = 'active'
    INACTIVE = 'inactive'
    BANNED = 'banned'


class User(Document):
    status = EnumField(enum=UserStatus)


# Create a user and set their status using the EnumField
user = User(status=UserStatus.ACTIVE)
# user = User(status="active") # Also works
await user.save()
```

### FloatField

The `FloatField` handles floating-point numbers, with options to specify minimum and maximum values.

**Parameters:**

- `min_value`: (Optional) The minimum allowable value.
- `max_value`: (Optional) The maximum allowable value.

**Example Usage:**

```python
from motormongo import Document, FloatField


class Measurement(Document):
    temperature = FloatField(min_value=-273.15)  # Absolute zero constraint


# Record a temperature measurement
measurement = Measurement(temperature=25.5)
await measurement.save()
```

### GeoJSONField

The `GeoJSONField` is designed for storing geographical coordinates in GeoJSON format.

**Parameters:**

- `return_as_list`: (Optional) If `True`, returns the coordinates as a [longitude, latitude] list instead of a GeoJSON
  object.

**Example Usage:**

```python
from motormongo import Document, GeoJSONField


class Location(Document):
    point = GeoJSONField()


# Create a location point
location = Location(point={"type": "Point", "coordinates": [-73.856077, 40.848447]})  # Could also use 
# location = Location(point=[-73.856077, 40.848447]) # This would also work
await location.save()
```

### IntegerField

The `IntegerField` is used for storing integer values, with optional validation for minimum and maximum values.

**Parameters:**

- `min_value`: (Optional) The minimum allowable value.
- `max_value`: (Optional) The maximum allowable value.

**Example Usage:**

```python
from motormongo import Document, IntegerField


class Product(Document):
    quantity = IntegerField(min_value=0)


# Create a product with quantity validation
product = Product(quantity=10)
await product.save()
```

### ListField

The `ListField` is used for storing a list of items, optionally validating the type of items in the list.

**Parameters:**

- `field`: (Optional) A `Field` instance specifying the type of items in the list.

**Example Usage:**

```python
from motormongo import Document, ListField, StringField


class ShoppingList(Document):
    items = ListField(field=StringField())


# Create a shopping list with string items
shopping_list = ShoppingList(items=["Milk", "Eggs", "Bread"])
await shopping_list.save()
```

### ReferenceField

The `ReferenceField` is used to create a reference to another document, typically for creating relationships between
collections.

**Parameters:**

- `document_class`: The class of the document to which the field references.

**Example Usage:**

```python
from motormongo import Document, ReferenceField
from bson import ObjectId


class User(Document):
    pass


class Post(Document):
    author = ReferenceField(document_class=User)


# Create a user and a post referencing the user
user = User(_id=ObjectId())
post = Post(author=user)

# When accessing `post.author`, it fetches the User instance it references
```

To fetch the referenced document, you must await the coroutine returned by accessing the reference field. This operation
asynchronously retrieves the related document instance from the database.

```python
# Assuming `post` is an instance of the Post document with a reference to a User
# Fetch the user referenced by the post's author field
referenced_user = await post.author()

if referenced_user:
    print("Referenced User:", referenced_user.to_dict())
else:
    print("User not found or failed to fetch.")
```

This example demonstrates how to access and asynchronously fetch the document referenced by a `ReferenceField`. The await
keyword is crucial because the operation is asynchronous, involving a database query to retrieve the referenced
document.

**Note:** Ensure that the fetching operation is performed within an asynchronous context, such as an async function. The
ReferenceField provides a powerful way to manage relationships between documents, enabling complex data models with
interconnected documents.

### StringField

The `StringField` is used for storing string data in a document. It supports validation for minimum and maximum length
and can enforce a specific regex pattern.

**Parameters:**

- `min_length`: (Optional) The minimum length of the string.
- `max_length`: (Optional) The maximum length of the string.
- `regex`: (Optional) A regex pattern that the string must match.

**Example Usage:**

```python
from motormongo import Document, StringField


class UserProfile(Document):
    username = StringField(min_length=3, max_length=50)
    bio = StringField(max_length=200, regex=r'^[A-Za-z0-9 ]*$')  # Alphanumeric and space only


# Create a user profile with validation
profile = UserProfile(username="user123", bio="I love coding.")
await profile.save()
```

## Class methods

## Operations

The following class methods are supported by motormongo's `Document` class:

| CRUD Type | Operation                                                                                                                          |
|-----------|------------------------------------------------------------------------------------------------------------------------------------|
| Create    | [`insert_one(document: dict, **kwargs) -> Document`](#insert_one)                                                                  |
| Create    | [`insert_many(documents: List[dict]) -> Tuple[List[Document], Any]`](#insert_many)                                                 |
| Read      | [`find_one(query: dict, **kwargs) -> Document`](#find_one)                                                                         |
| Read      | [`find_many(query: dict, limit: int, **kwargs) -> List[Document]`](#find_many)                                                     |
| Update    | [`update_one(query: dict, update_fields: dict) -> Document`](#update_one)                                                          |
| Update    | [`update_many(query: dict, update_fields: dict) -> Tuple[List[Document], int]`](#update_many)                                      |
| Delete    | [`delete_one(query: dict, **kwargs) -> bool`](#delete_one)                                                                         |
| Delete    | [`delete_many(query: dict, **kwargs) -> int`](#delete_many)                                                                        |
| Mixed     | [`find_one_or_create(query: dict, defaults: dict) -> Tuple[Document, bool]`](#find_one_or_create)                                  |
| Mixed     | [`find_one_and_replace(query: dict, replacement: dict) -> Document`](#find_one_and_replace)                                        |
| Mixed     | [`find_one_and_delete(query: dict) -> Document`](#find_one_and_delete)                                                             |
| Mixed     | [`find_one_and_update_empty_fields(query: dict, update_fields: dict) -> Tuple[Document, bool]`](#find_one_and_update_empty_fields) |

All examples below assume `User` is a subclass of motormongo provided Document class.

### Create

#### <a name="insert_one"></a> `insert_one(document: dict, **kwargs) -> Document`

Inserts a single document into the database.

```python
user = await User.insert_one({
    "name": "John",
    "age": 24,
    "alive": True
})
```

Alternatively, using `**kwargs`:

```python
user = await User.insert_one(
    name="John",
    age=24,
    alive=True)
```

And similarly, with a dictionary:

```python
user_document = {
    "name": "John",
    "age": 24,
    "alive": True
}
user = await User.insert_one(**user_document)
```

#### <a name="insert_many"></a> `insert_many(List[document]) -> tuple[List['Document'], Any]`

```python
users, user_ids = await User.insert_many(
    [
        {
            "name": "John",
            "age": 24,
            "alive": True
        },
        {
            "name": "Mary",
            "age": 2,
            "alive": False
        }
    ]
)
```

or

```python
docs_to_insert = [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]
inserted_docs, inserted_ids = await User.insert_many(docs_to_insert)
```

### Read

#### <a name="find_one"></a> `find_one(query, **kwargs) -> Document`

```python
user = await User.find_one(
    {
        "_id": "655fc281c440f677fa1e117e"
    }
)
```

Alternatively, using `**kwargs`:

```python
user = await User.find_one(_id="655fc281c440f677fa1e117e")
```

**Note:** The `_id` string datatype here is automatically converted to a BSON ObjectID, however, motormongo handles the
scenario when a
BSON ObjectId is passed as the `_id` datatype:

```python
from bson import ObjectId

user = await User.find_one(
    {
        "_id": ObjectId("655fc281c440f677fa1e117e")
    }
)
```

#### <a name="find_many"></a> `find_many(query, limit, **kwargs) -> List[Document]`

```python
users = await User.find_many(age={"$gt": 40}, alive=False, limit=20)
```

or

```python
query = {"age": {"$gt": 40}, "alive": False}
users = await User.find_many(**query, limit=20)
```

### Update

#### <a name="update_one"></a> `update_one(query, updated_fields) -> Document`

```python
updated_user = await User.update_one(
    {
        "_id": "655fc281c440f677fa1e117e"
    },
    {
        "name": "new_name",
        "age": 30
    }
)
```

or

```python
query_criteria = {"name": "old_name"}
update_data = {"name": "updated_name"}
updated_user = await User.update_one(query_criteria, update_data)
```

#### <a name="update_many"></a> `update_many(qeury, fields) -> Tuple[List[Any], int]`

```python
updated_users, modified_count = await User.update_many({'age': {'$gt': 40}}, {'category': 'senior'})
```

another example:

```python
updated_users, modified_count = await User.update_many({'name': 'John Doe'}, {'$inc': {'age': 1}})
```

### Delete

#### <a name="delete_one"></a> `delete_one(query, **kwargs) -> bool`

```python
deleted = await User.delete_one({'_id': '507f191e810c19729de860ea'})
```

Alternatively, using `**kwargs`:

```python
deleted = await User.delete_one(name='John Doe')
```

#### <a name="delete_many"></a> `delete_many(query, **kwargs) -> int`

```python
deleted_count = await User.delete_many({'age': {'$gt': 40}})
```

Another example:

```python
# Delete all users with a specific status
deleted_count = await User.delete_many({'status': 'inactive'})
```

Alternatively, using `**kwargs`:

```python
deleted_count = await User.delete_many(status='inactive')
```

### Mixed

#### <a name="find_one_or_create"></a> `find_one_or_create(query, defaults) -> Tuple['Document', bool]`

```python
user, created = await User.find_one_or_create({'username': 'johndoe'}, defaults={'age': 30})
```

#### <a name="find_one_and_replace"></a> `find_one_and_replace(query, replacement) -> Document`

```python
replaced_user = await User.find_one_and_replace({'username': 'johndoe'}, {'username': 'johndoe', 'age': 35})
```

#### <a name="find_one_and_delete"></a> `find_one_and_delete(query) -> Document`

```python
deleted_user = await User.find_one_and_delete({'username': 'johndoe'})
```

#### <a name="find_one_and_update_empty_fields"></a> `find_one_and_update_empty_fields(query, update_fields) -> Tuple['Document', bool]`

```python
updated_user, updated = await User.find_one_and_update_empty_fields(
    {'username': 'johndoe'},
    {'email': 'johndoe@example.com', 'age': 30}
)
```

## Instance methods

motormongo also supports the manimulation of fields on the [object instance](). This allows
users to programmatically achieve the same operations listed above through the object instance
itself.

### Operations

The following are object instance methods are supported by motormongo's `Document` class:

| CRUD Type | Operation                     |
|-----------|-------------------------------|
| Create    | [`save() -> None`](#save)     |
| Delete    | [`delete() -> None`](#delete) |

**Note:** All update operations can be manipulated on the fields in the Document class object itself.

#### <a name="save"></a> `user.save() -> None`

```python
# Find user by MongoDB _id
user = await User.find_one(
    {
        "_id": "655fc281c440f677fa1e117e"
    }
)
# If there age is greater than 80, make them dead
if user.age > 80:
    user.alive = False
# Persist update on User instance in MongoDB mongo
user.save()
```

In this example, `User.find_one()` returns an instance of `User`. If the age field
is greater than 80, the alive field is set to false. The instance of the document in the MongoDB
database is then updated by calling the `.save()` method on the `User` object instance.

### Delete

#### <a name="delete"></a> `user.delete() -> None`

```python
# Find all users where the user is not alive
users = await User.find_many(
    {
        "alive": False
    }
)
# Recursively delete all User instances in the users list who are not alive
for user in users:
    user.delete()
```

### Aggregation

The `aggregate` class method is designed to perform aggregation operations on the documents within the collection. It allows the execution of a sequence of data aggregation operations defined by the `pipeline` parameter. This method can return the results either as a list of documents or as a cursor, based on the `return_as_list` flag.

**Parameters:**

- `pipeline`: A list of dictionaries defining the aggregation operations to be performed on the collection.
- `return_as_list` (optional): A boolean flag that determines the format of the returned results. If set to `True`, the method returns a list of documents. If `False` (default), it returns a cursor.

**Returns:**

- If `return_as_list` is `True`, returns a list of documents resulting from the aggregation pipeline.
- If `return_as_list` is `False`, returns a Cursor to iterate over the results.

**Raises:**

- `ValueError`: If an error occurs during the execution of the pipeline.

**Example Usage:**

```python
from yourmodule import YourDocumentClass

# Connect to the database (Assuming the database connection is already set up)
# Define an aggregation pipeline
pipeline = [
    {"$match": {"status": "active"}},
    {"$project": {"_id": 0, "username": 1, "status": 1}},
    {"$sort": {"username": 1}}
]

# Execute the aggregation without returning a list
cursor = await YourDocumentClass.aggregate(pipeline)
async for doc in cursor:
    print(doc)

# Execute the aggregation and return results as a list
docs_list = await YourDocumentClass.aggregate(pipeline, return_as_list=True)
print(docs_list)
```


## FastAPI integration

motormongo can be easily integrated in FastAPI APIs to leverage the asynchronous ability of
FastAPI. To leverage motormongo's ease-of-use, Pydantic model's should be created to represent the MongoDB
motormongo Document as a Pydantic model. Below is a light-weight CRUD FastAPI application using motormongo:

```python
import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from motormongo import DataBase, Document, BinaryField, StringField
import re
import bcrypt


def hash_password(password) -> bytes:
    # Example hashing function
    return bcrypt.hashpw(password.encode('utf-8'), salt=bcrypt.gensalt())


class User(Document):
    username = StringField(help_text="The username for the user", min_length=3, max_length=50)
    email = StringField(help_text="The email for the user", regex=re.compile(r'^\S+@\S+\.\S+$'))  # Simple email regex
    password = BinaryField(help_text="The hashed password for the user", hash_function=hash_password)

    def verify_password(self, password: str) -> bool:
        return bcrypt.checkpw(password.encode("utf-8"), self.password)

    class Meta:
        collection = "users"  # < If not provided, will default to class name (ex. User->user, UserDetails->user_details)
        created_at_timestamp = True  # < Provide a DateTimeField for document creation
        updated_at_timestamp = True  # < Provide a DateTimeField for document updates


class UserModelRequest(BaseModel):
    username: str = Field(example="johndoe")
    email: str = Field(example="johndoe@coldmail.com")
    password: str = Field(example="password123")


app = FastAPI()


@app.on_event("startup")
async def startup_db_client():
    await DataBase.connect(uri=os.getenv("MONGODB_URL"), db=os.getenv("MONGODB_DB"))


@app.on_event("shutdown")
async def shutdown_db_client():
    await DataBase.close()


@app.post("/users/", status_code=201)
async def create_user(user: UserModelRequest):
    new_user = await User.insert_one(**user.dict())
    return new_user.to_dict()


@app.post("/user/auth", status_code=201)
async def is_authenticated(username: str, password: str):
    user = await User.find_one({"username": username})
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user.verify_password(password)


@app.get("/users")
async def get_users():
    users = await User.find_many()
    if not users:
        raise HTTPException(status_code=404, detail="User not found")
    return [user.to_dict() for user in users]


@app.get("/users/{user_id}")
async def get_user(user_id: str):
    user = await User.find_one({"_id": user_id})
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user.to_dict()


@app.put("/users/{user_id}", status_code=200)
async def update_user(user_id: str, user_data: UserModelRequest):
    updated_user = await User.update_one({"_id": user_id}, user_data.dict())
    if not updated_user:
        raise HTTPException(status_code=404, detail="User not found")
    return updated_user.to_dict()


@app.delete("/users/{user_id}", status_code=204)
async def delete_user(user_id: str):
    user = await User.find_one({"_id": user_id})
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    await user.delete()
    return {"status": "User deleted successfully"}
```

## License

This project is licensed under the MIT License.