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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Create composite image by blending images using a transparency mask. :param image1: The first image. :param image2: The second image. Must have the same mode and size as the first image. :param mask: A mask image. This image can have mode "1", "L", or "RGBA", and must have the same size as the other two images.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Applies the function (which should take one argument) to each pixel in the given image. If the image has more than one band, the same function is applied to each band. Note that the function is evaluated once for each possible pixel value, so you cannot use random components or other generators. :param image: The input...
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Register an image file plugin. This function should not be used in application code. :param id: An image format identifier. :param factory: An image file factory method. :param accept: An optional function that can be used to quickly reject images having another format.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers an image MIME type. This function should not be used in application code. :param id: An image format identifier. :param mimetype: The image MIME type for this format.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers an image save function. This function should not be used in application code. :param id: An image format identifier. :param driver: A function to save images in this format.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers an image function to save all the frames of a multiframe format. This function should not be used in application code. :param id: An image format identifier. :param driver: A function to save images in this format.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers image extensions. This function should not be used in application code. :param id: An image format identifier. :param extensions: A list of extensions used for this format.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Returns a dictionary containing all file extensions belonging to registered plugins
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers an image decoder. This function should not be used in application code. :param name: The name of the decoder :param decoder: A callable(mode, args) that returns an ImageFile.PyDecoder object .. versionadded:: 4.1.0
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Registers an image encoder. This function should not be used in application code. :param name: The name of the encoder :param encoder: A callable(mode, args) that returns an ImageFile.PyEncoder object .. versionadded:: 4.1.0
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Generate a Mandelbrot set covering the given extent. :param size: The requested size in pixels, as a 2-tuple: (width, height). :param extent: The extent to cover, as a 4-tuple: (x0, y0, x1, y2). :param quality: Quality.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Generate Gaussian noise centered around 128. :param size: The requested size in pixels, as a 2-tuple: (width, height). :param sigma: Standard deviation of noise.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Generate 256x256 linear gradient from black to white, top to bottom. :param mode: Input mode.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
Generate 256x256 radial gradient from black to white, centre to edge. :param mode: Input mode.
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import atexit import builtins import io import logging import math import numbers import os import re import struct import sys import tempfile import warnings from collections.abc import Callable, MutableMapping from pathlib import Path from . import ImageMode, TiffTags, UnidentifiedImageError, __version__, _plugins fr...
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import olefile from . import Image, ImageFile from ._binary import i32le as i32 def _accept(prefix): return prefix[:8] == olefile.MAGIC
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import collections import os import sys import warnings import PIL from . import Image def get_supported_modules(): """ :returns: A list of all supported modules. """ return [f for f in modules if check_module(f)] def get_supported_codecs(): """ :returns: A list of all supported codecs. """ ...
:returns: A list of all supported modules, features, and codecs.
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import collections import os import sys import warnings import PIL from . import Image features = { "webp_anim": ("PIL._webp", "HAVE_WEBPANIM", None), "webp_mux": ("PIL._webp", "HAVE_WEBPMUX", None), "transp_webp": ("PIL._webp", "HAVE_TRANSPARENCY", None), "raqm": ("PIL._imagingft", "HAVE_RAQM", "raqm_v...
Prints information about this installation of Pillow. This function can be called with ``python3 -m PIL``. :param out: The output stream to print to. Defaults to ``sys.stdout`` if ``None``. :param supported_formats: If ``True``, a list of all supported image file formats will be printed.
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from inspect import signature from typing import List, Type from beanie.migrations.controllers.base import BaseMigrationController from beanie.odm.documents import Document class BaseMigrationController(ABC): def __init__(self, function): self.function = function async def run(self, session): ...
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import asyncio from inspect import isclass, signature from typing import List, Optional, Type, Union from beanie.migrations.controllers.base import BaseMigrationController from beanie.migrations.utils import update_dict from beanie.odm.documents import Document from beanie.odm.utils.pydantic import parse_model class Du...
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import pydantic def is_second_version() -> bool: return int(pydantic.VERSION.split(".")[0]) >= 2
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import asyncio import logging import os import shutil from datetime import datetime from pathlib import Path from typing import Any import click import toml from beanie.migrations import template from beanie.migrations.database import DBHandler from beanie.migrations.models import RunningDirections, RunningMode from be...
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import asyncio import logging import os import shutil from datetime import datetime from pathlib import Path from typing import Any import click import toml from beanie.migrations import template from beanie.migrations.database import DBHandler from beanie.migrations.models import RunningDirections, RunningMode from be...
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import asyncio import logging import os import shutil from datetime import datetime from pathlib import Path from typing import Any import click import toml from beanie.migrations import template from beanie.migrations.database import DBHandler from beanie.migrations.models import RunningDirections, RunningMode from be...
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import asyncio import inspect from enum import Enum from functools import wraps from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union, ) class EventTypes(str, Enum): INSERT = "INSERT" REPLACE = "REPLACE" SAVE = "SAVE" SAVE_CHANGES...
Decorator. It adds action, which should run before mentioned one or many events happen :param args: Union[List[EventTypes], EventTypes] - event types :return: None
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import asyncio import inspect from enum import Enum from functools import wraps from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union, ) class EventTypes(str, Enum): INSERT = "INSERT" REPLACE = "REPLACE" SAVE = "SAVE" SAVE_CHANGES...
Decorator. It adds action, which should run after mentioned one or many events happen :param args: Union[List[EventTypes], EventTypes] - event types :return: None
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import asyncio import inspect from enum import Enum from functools import wraps from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union, ) class EventTypes(str, Enum): INSERT = "INSERT" REPLACE = "REPLACE" SAVE = "SAVE" SAVE_CHANGES...
Helper function to wrap Document methods with before and after event listeners :param event_type: EventTypes - event types :return: None
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from typing import Any import bson import pydantic from typing_extensions import Annotated from beanie.odm.utils.pydantic import IS_PYDANTIC_V2 def _to_bson_binary(value: Any) -> bson.Binary: return value if isinstance(value, bson.Binary) else bson.Binary(value)
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import asyncio import warnings from enum import Enum from typing import ( Any, ClassVar, Dict, Iterable, List, Mapping, Optional, Type, TypeVar, Union, ) from uuid import UUID, uuid4 from bson import DBRef, ObjectId from lazy_model import LazyModel from pydantic import ( Conf...
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import asyncio import warnings from enum import Enum from typing import ( Any, ClassVar, Dict, Iterable, List, Mapping, Optional, Type, TypeVar, Union, ) from uuid import UUID, uuid4 from bson import DBRef, ObjectId from lazy_model import LazyModel from pydantic import ( Conf...
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import asyncio import sys from collections import OrderedDict from dataclasses import dataclass from enum import Enum from typing import ( TYPE_CHECKING, Any, Dict, Generic, List, Optional, Type, TypeVar, Union, ) from typing import OrderedDict as OrderedDictType from typing import T...
If `typ` is defined, returns a subclass of `typ` with an extra attribute `_indexed` as a tuple: - Index 0: `index_type` such as `pymongo.ASCENDING` - Index 1: `kwargs` passed to `IndexModel` When instantiated the type of the result will actually be `typ`. When `typ` is not defined, returns an `IndexedAnnotation` instan...
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type from beanie.odm.fields import LinkInfo, LinkTypes def construct_query( link_info: LinkInfo, queries: List, database_major_version: int, current_depth: Optional[int] = None, ): if link_info.is_fetchable is False or ( cur...
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type from beanie.odm.fields import LinkInfo, LinkTypes The provided code snippet includes necessary dependencies for implementing the `split_text_query` function. Write a Python function `def split_text_query( query: Dict[str, Any] ) -> Tuple[List...
Divide query into text and non-text matches :param query: Dict[str, Any] - query dict :return: Tuple[Dict[str, Any], Dict[str, Any]] - text and non-text queries, respectively
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import asyncio import sys from beanie.odm.utils.pydantic import ( IS_PYDANTIC_V2, get_extra_field_info, get_model_fields, parse_model, ) from beanie.odm.utils.typing import get_index_attributes import importlib import inspect from typing import ( # type: ignore List, Optional, Type, Uni...
Beanie initialization :param database: AsyncIOMotorDatabase - motor database instance :param connection_string: str - MongoDB connection string :param document_models: List[Union[Type[DocType], str]] - model classes or strings with dot separated paths :param allow_index_dropping: bool - if index dropping is allowed. De...
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import inspect import sys from typing import Any, Dict, Optional, Tuple, Type from beanie.odm.fields import IndexedAnnotation from .pydantic import IS_PYDANTIC_V2, get_field_type def extract_id_class(annotation) -> Type[Any]: if get_origin(annotation) is not None: try: annotation = next( ...
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import inspect import sys from typing import Any, Dict, Optional, Tuple, Type from beanie.odm.fields import IndexedAnnotation from .pydantic import IS_PYDANTIC_V2, get_field_type class IndexedAnnotation: _indexed: Tuple[int, Dict[str, Any]] IS_PYDANTIC_V2 = int(pydantic.VERSION.split(".")[0]) >= 2 if IS_PYDANTIC...
Gets the index attributes from the field, if it is indexed. :param field: The field to get the index attributes from. :return: The index attributes, if the field is indexed. Otherwise, None.
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from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Dict from typing import Mapping as MappingType from beanie.odm.fields import ( ExpressionField, ) class ExpressionField(str): def __getitem__(self, item): def __getattr__(self, item): def __hash__(self): def __eq__(self,...
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from typing import Any, Type import pydantic from pydantic import BaseModel IS_PYDANTIC_V2 = int(pydantic.VERSION.split(".")[0]) >= 2 if IS_PYDANTIC_V2: from pydantic import TypeAdapter else: from pydantic import parse_obj_as def parse_object_as(object_type: Type, data: Any): if IS_PYDANTIC_V2: ret...
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from typing import Any, Type import pydantic from pydantic import BaseModel IS_PYDANTIC_V2 = int(pydantic.VERSION.split(".")[0]) >= 2 if IS_PYDANTIC_V2: from pydantic import TypeAdapter else: from pydantic import parse_obj_as def get_extra_field_info(field, parameter: str): if IS_PYDANTIC_V2: if fi...
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from typing import Any, Type import pydantic from pydantic import BaseModel IS_PYDANTIC_V2 = int(pydantic.VERSION.split(".")[0]) >= 2 if IS_PYDANTIC_V2: from pydantic import TypeAdapter else: from pydantic import parse_obj_as def get_model_dump(model, *args, **kwargs): if IS_PYDANTIC_V2: return mod...
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from typing import TYPE_CHECKING, Any, Dict, Type, Union from pydantic import BaseModel from beanie.exceptions import ( ApplyChangesException, DocWasNotRegisteredInUnionClass, UnionHasNoRegisteredDocs, ) from beanie.odm.interfaces.detector import ModelType from beanie.odm.utils.pydantic import get_config_va...
Merge two models :param left: left model :param right: right model :return: None
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from typing import TYPE_CHECKING, Any, Dict, Type, Union from pydantic import BaseModel from beanie.exceptions import ( ApplyChangesException, DocWasNotRegisteredInUnionClass, UnionHasNoRegisteredDocs, ) from beanie.odm.interfaces.detector import ModelType from beanie.odm.utils.pydantic import get_config_va...
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from typing import TYPE_CHECKING, Any, Dict, Type, Union from pydantic import BaseModel from beanie.exceptions import ( ApplyChangesException, DocWasNotRegisteredInUnionClass, UnionHasNoRegisteredDocs, ) from beanie.odm.interfaces.detector import ModelType from beanie.odm.utils.pydantic import get_config_va...
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from typing import TYPE_CHECKING, Optional, Set from beanie.odm.utils.encoder import Encoder def get_dict( document: "Document", to_db: bool = False, exclude: Optional[Set[str]] = None, keep_nulls: bool = True, ): if exclude is None: exclude = set() if document.id is None: exclud...
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from typing import TYPE_CHECKING, Optional, Set from beanie.odm.utils.encoder import Encoder def get_dict( document: "Document", to_db: bool = False, exclude: Optional[Set[str]] = None, keep_nulls: bool = True, ): if exclude is None: exclude = set() if document.id is None: exclud...
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from typing import Dict, Optional, Type, TypeVar from pydantic import BaseModel from beanie.odm.interfaces.detector import ModelType from beanie.odm.utils.pydantic import get_config_value, get_model_fields ProjectionModelType = TypeVar("ProjectionModelType", bound=BaseModel) class ModelType(str, Enum): def get_model_...
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from functools import wraps from typing import TYPE_CHECKING, Callable def validate_self_before(f: Callable): @wraps(f) async def wrapper(self: "DocType", *args, **kwargs): await self.validate_self(*args, **kwargs) return await f(self, *args, **kwargs) return wrapper
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import inspect from functools import wraps from typing import TYPE_CHECKING, Callable from beanie.exceptions import StateManagementIsTurnedOff, StateNotSaved def check_if_state_saved(self: "DocType"): if not self.use_state_management(): raise StateManagementIsTurnedOff( "State management is turn...
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import inspect from functools import wraps from typing import TYPE_CHECKING, Callable from beanie.exceptions import StateManagementIsTurnedOff, StateNotSaved def check_if_previous_state_saved(self: "DocType"): if not self.use_state_management(): raise StateManagementIsTurnedOff( "State managemen...
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import inspect from functools import wraps from typing import TYPE_CHECKING, Callable from beanie.exceptions import StateManagementIsTurnedOff, StateNotSaved def save_state_after(f: Callable): @wraps(f) async def wrapper(self: "DocType", *args, **kwargs): result = await f(self, *args, **kwargs) ...
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import dataclasses as dc import datetime import decimal import enum import ipaddress import operator import pathlib import re import uuid from enum import Enum from typing import ( Any, Callable, Container, Iterable, Mapping, MutableMapping, Optional, Tuple, ) import bson import pydantic...
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from typing import Optional from fastapi import Depends, HTTPException, Path, Query from starlette import status from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.db.errors import EntityDoesNotExist from app.db.repositories.arti...
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from typing import Optional from fastapi import Depends, HTTPException, Path from starlette import status from app.api.dependencies import articles, authentication, database from app.db.errors import EntityDoesNotExist from app.db.repositories.comments import CommentsRepository from app.models.domain.articles import Ar...
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from fastapi import APIRouter, Depends from app.api.dependencies.database import get_repository from app.db.repositories.tags import TagsRepository from app.models.schemas.tags import TagsInList def get_repository( repo_type: Type[BaseRepository], ) -> Callable[[Connection], BaseRepository]: def _get_repo( ...
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from fastapi import APIRouter, Body, Depends, HTTPException from starlette.status import HTTP_400_BAD_REQUEST from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.core.config import get_app_settings from app.core.settings.app impor...
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from fastapi import APIRouter, Body, Depends, HTTPException from starlette.status import HTTP_400_BAD_REQUEST from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.core.config import get_app_settings from app.core.settings.app impor...
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from fastapi import APIRouter, Depends, HTTPException from starlette.status import HTTP_400_BAD_REQUEST from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.api.dependencies.profiles import get_profile_by_username_from_path from ap...
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from fastapi import APIRouter, Depends, HTTPException from starlette.status import HTTP_400_BAD_REQUEST from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.api.dependencies.profiles import get_profile_by_username_from_path from ap...
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from fastapi import APIRouter, Depends, HTTPException from starlette.status import HTTP_400_BAD_REQUEST from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.api.dependencies.profiles import get_profile_by_username_from_path from ap...
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from fastapi import APIRouter, Body, Depends, HTTPException from starlette.status import HTTP_201_CREATED, HTTP_400_BAD_REQUEST from app.api.dependencies.database import get_repository from app.core.config import get_app_settings from app.core.settings.app import AppSettings from app.db.errors import EntityDoesNotExist...
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from fastapi import APIRouter, Body, Depends, HTTPException from starlette.status import HTTP_201_CREATED, HTTP_400_BAD_REQUEST from app.api.dependencies.database import get_repository from app.core.config import get_app_settings from app.core.settings.app import AppSettings from app.db.errors import EntityDoesNotExist...
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from typing import Optional from fastapi import APIRouter, Body, Depends, HTTPException, Response from starlette import status from app.api.dependencies.articles import ( check_article_modification_permissions, get_article_by_slug_from_path, get_articles_filters, ) from app.api.dependencies.authentication i...
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from typing import Optional from fastapi import APIRouter, Body, Depends, HTTPException, Response from starlette import status from app.api.dependencies.articles import ( check_article_modification_permissions, get_article_by_slug_from_path, get_articles_filters, ) from app.api.dependencies.authentication i...
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from typing import Optional from fastapi import APIRouter, Body, Depends, HTTPException, Response from starlette import status from app.api.dependencies.articles import ( check_article_modification_permissions, get_article_by_slug_from_path, get_articles_filters, ) from app.api.dependencies.authentication i...
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from typing import Optional from fastapi import APIRouter, Body, Depends, HTTPException, Response from starlette import status from app.api.dependencies.articles import ( check_article_modification_permissions, get_article_by_slug_from_path, get_articles_filters, ) from app.api.dependencies.authentication i...
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from typing import Optional from fastapi import APIRouter, Body, Depends, HTTPException, Response from starlette import status from app.api.dependencies.articles import ( check_article_modification_permissions, get_article_by_slug_from_path, get_articles_filters, ) from app.api.dependencies.authentication i...
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from fastapi import APIRouter, Depends, HTTPException, Query from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.db.repositories....
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from fastapi import APIRouter, Depends, HTTPException, Query from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.db.repositories....
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from fastapi import APIRouter, Depends, HTTPException, Query from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.database import get_repository from app.db.repositories....
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from typing import Optional from fastapi import APIRouter, Body, Depends, Response from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.comments import ( check_commen...
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from typing import Optional from fastapi import APIRouter, Body, Depends, Response from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.comments import ( check_commen...
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from typing import Optional from fastapi import APIRouter, Body, Depends, Response from starlette import status from app.api.dependencies.articles import get_article_by_slug_from_path from app.api.dependencies.authentication import get_current_user_authorizer from app.api.dependencies.comments import ( check_commen...
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from fastapi import FastAPI from fastapi.exceptions import RequestValidationError from starlette.exceptions import HTTPException from starlette.middleware.cors import CORSMiddleware from app.api.errors.http_error import http_error_handler from app.api.errors.validation_error import http422_error_handler from app.api.ro...
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import bcrypt from passlib.context import CryptContext def generate_salt() -> str: return bcrypt.gensalt().decode()
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import bcrypt from passlib.context import CryptContext pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") def verify_password(plain_password: str, hashed_password: str) -> bool: return pwd_context.verify(plain_password, hashed_password)
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import bcrypt from passlib.context import CryptContext pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") def get_password_hash(password: str) -> str: return pwd_context.hash(password)
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from typing import Tuple import sqlalchemy as sa from alembic import op from sqlalchemy import func def create_updated_at_trigger() -> None: op.execute( """ CREATE FUNCTION update_updated_at_column() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = now(); RETURN NEW; END;...
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from typing import Tuple import sqlalchemy as sa from alembic import op from sqlalchemy import func def downgrade() -> None: op.drop_table("commentaries") op.drop_table("favorites") op.drop_table("articles_to_tags") op.drop_table("tags") op.drop_table("articles") op.drop_table("followers_to_fol...
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import pathlib import sys from logging.config import fileConfig from alembic import context from sqlalchemy import engine_from_config, pool from app.core.config import get_app_settings config = context.config target_metadata = None config.set_main_option("sqlalchemy.url", str(DATABASE_URL)) def run_migrations_online(...
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import datetime from pydantic import BaseConfig, BaseModel def convert_datetime_to_realworld(dt: datetime.datetime) -> str: return dt.replace(tzinfo=datetime.timezone.utc).isoformat().replace("+00:00", "Z")
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import datetime from pydantic import BaseConfig, BaseModel def convert_field_to_camel_case(string: str) -> str: return "".join( word if index == 0 else word.capitalize() for index, word in enumerate(string.split("_")) )
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import os, sys os.environ["TOKENIZERS_PARALLELISM"] = "false" import logging import click import numpy as np from functools import partial from pathlib import Path from typing import Any, Dict, List, Tuple, Union from datetime import datetime from datasets import Dataset, load_dataset, load_from_disk from transformers ...
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import os os.environ["TOKENIZERS_PARALLELISM"] = "false" import logging, torch import click import numpy as np from functools import partial from pathlib import Path from typing import Any, Dict, List, Tuple, Union from datetime import datetime from datasets import Dataset, load_dataset, load_from_disk from transformer...
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import os, sys os.environ["TOKENIZERS_PARALLELISM"] = "false" import logging import click import numpy as np from functools import partial from pathlib import Path from typing import Any, Dict, List, Tuple, Union from datetime import datetime from datasets import Dataset, load_dataset, load_from_disk from transformers ...
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import os, json from argparse import ArgumentParser from tqdm import tqdm The provided code snippet includes necessary dependencies for implementing the `convert_inst_to_chat` function. Write a Python function `def convert_inst_to_chat(path)` to solve the following problem: Usage: 将 `指令数据格式` 转换为 `对话数据格式`. Here is the...
Usage: 将 `指令数据格式` 转换为 `对话数据格式`.
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import os, json from argparse import ArgumentParser from tqdm import tqdm The provided code snippet includes necessary dependencies for implementing the `convert_chat_to_inst` function. Write a Python function `def convert_chat_to_inst(path)` to solve the following problem: Usage: 将 `对话数据格式` 转换为 `指令数据格式` 忽略多轮数据. Here...
Usage: 将 `对话数据格式` 转换为 `指令数据格式` 忽略多轮数据.
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import os, json from argparse import ArgumentParser from tqdm import tqdm The provided code snippet includes necessary dependencies for implementing the `merge_multi_chat_files` function. Write a Python function `def merge_multi_chat_files(path)` to solve the following problem: Usage: 合并多个 `对话数据格式` 文件. Here is the fu...
Usage: 合并多个 `对话数据格式` 文件.
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from setuptools import find_packages, setup import os import subprocess import time version_file = 'pyiqa/version.py' def get_hash(): def write_version_py(): content = """# GENERATED VERSION FILE # TIME: {} __version__ = '{}' __gitsha__ = '{}' version_info = ({}) """ sha = get_hash() with open('VERSION', '...
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from setuptools import find_packages, setup import os import subprocess import time version_file = 'pyiqa/version.py' def get_version(): with open(version_file, 'r') as f: exec(compile(f.read(), version_file, 'exec')) return locals()['__version__']
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import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss def l1_loss(pred, target): return F.l1_loss(pred, target, reduction='none')
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import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss def mse_loss(pred, target): return F.mse_loss(pred, target, reduction='none')
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import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss def cross_entropy(pred, target): return F.cross_entropy(pred, target, reduction='none')
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import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss def nll_loss(pred, target): return F.nll_loss(pred, target, reduction='none')
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import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss def charbonnier_loss(pred, target, eps=1e-12): return torch.sqrt((pred - target)**2 + eps)
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from cv2 import reduce import torch import numpy as np from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss The provided code snippet includes necessary dependencies for implementing the `emd_loss` function. Write a Python f...
Args: pred (Tensor): of shape (N, C). Predicted tensor. target (Tensor): of shape (N, C). Ground truth tensor. r (float): norm level, default l2 norm.
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from cv2 import reduce import torch import numpy as np from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss The provided code snippet includes necessary dependencies for implementing the `plcc_loss` function. Write a Python ...
Args: pred (Tensor): of shape (N, 1). Predicted tensor. target (Tensor): of shape (N, 1). Ground truth tensor.
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from cv2 import reduce import torch import numpy as np from torch import nn as nn from torch.nn import functional as F from pyiqa.utils.registry import LOSS_REGISTRY from .loss_util import weighted_loss The provided code snippet includes necessary dependencies for implementing the `norm_loss_with_normalization` functi...
Args: pred (Tensor): of shape (N, 1). Predicted tensor. target (Tensor): of shape (N, 1). Ground truth tensor.
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import math import collections.abc from itertools import repeat import numpy as np from typing import Tuple import torch from torch import nn as nn from torch.nn import functional as F from torch.nn import init as init def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): r...
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import math import collections.abc from itertools import repeat import numpy as np from typing import Tuple import torch from torch import nn as nn from torch.nn import functional as F from torch.nn import init as init to_2tuple = _ntuple(2) def symm_pad(im: torch.Tensor, padding: Tuple[int, int, int, int]): """Sym...
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