id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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6,731 | import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
# type: (Tensor, Optional[Li... | null |
6,732 | import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
def pad(image, target, padding):
# assumes that we only pad on the bottom right corners
padded_image = F.pad(image... | null |
6,733 | import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from panopticapi.utils import rgb2id
from util.box_ops import masks_to_boxes
from .coco import make_coco_transforms
class CocoPanoptic:
def __init__(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True)... | null |
6,734 | from pathlib import Path
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
import datasets.transforms as T
def convert_coco_poly_to_mask(segmentations, height, width):
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height... | null |
6,735 | from pathlib import Path
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
import datasets.transforms as T
class CocoDetection(torchvision.datasets.CocoDetection):
def __init__(self, img_folder, ann_file, transforms, return_masks):
super(CocoDetection, self)._... | null |
6,736 | import copy
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch import nn, Tensor
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | null |
6,737 | import copy
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch import nn, Tensor
The provided code snippet includes necessary dependencies for implementing the `_get_activation_fn` function. Write a Python function `def _get_activation_fn(activation)` to solve the following probl... | Return an activation function given a string |
6,738 | import torch
import torch.nn.functional as F
from torch import nn
from util import box_ops
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, interpolate,
is_dist_avail_and_initialized)
from .backbone import build_backbone
from .m... | null |
6,739 | import io
from collections import defaultdict
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from PIL import Image
import util.box_ops as box_ops
from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
def _expand(te... | null |
6,740 | import io
from collections import defaultdict
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from PIL import Image
import util.box_ops as box_ops
from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
The provided c... | Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). |
6,741 | import io
from collections import defaultdict
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from PIL import Image
import util.box_ops as box_ops
from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
The provided c... | Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for th... |
6,742 | import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1) | null |
6,743 | import torch
from torchvision.ops.boxes import box_area
def box_xyxy_to_cxcywh(x):
x0, y0, x1, y1 = x.unbind(-1)
b = [(x0 + x1) / 2, (y0 + y1) / 2,
(x1 - x0), (y1 - y0)]
return torch.stack(b, dim=-1) | null |
6,744 | import torch
from torchvision.ops.boxes import box_area
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
wh = (rb - lt).clamp(min=0) # [N,M,2]
... | Generalized IoU from https://giou.stanford.edu/ The boxes should be in [x0, y0, x1, y1] format Returns a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) |
6,745 | import torch
from torchvision.ops.boxes import box_area
The provided code snippet includes necessary dependencies for implementing the `masks_to_boxes` function. Write a Python function `def masks_to_boxes(masks)` to solve the following problem:
Compute the bounding boxes around the provided masks The masks should be ... | Compute the bounding boxes around the provided masks The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. Returns a [N, 4] tensors, with the boxes in xyxy format |
6,746 | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
def get_sha():
cwd = os.path.dirname(os.path.abs... | null |
6,747 | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
def nested_tensor_from_tensor_list(tensor_list: List[... | null |
6,748 | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
def is_main_process():
return get_rank() == 0
de... | null |
6,749 | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
def setup_for_distributed(is_master):
"""
Thi... | null |
6,750 | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
The provided code snippet includes necessary depende... | Computes the precision@k for the specified values of k |
6,751 | import torch
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path, PurePath
The provided code snippet includes necessary dependencies for implementing the `plot_logs` function. Write a Python function `def plot_logs(logs, fields=('class_error', 'loss_bbo... | Function to plot specific fields from training log(s). Plots both training and test results. :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file - fields = which results to plot from each log file - plots both training and test for each field. - ewm_col = optional, which col... |
6,752 | import torch
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path, PurePath
def plot_precision_recall(files, naming_scheme='iter'):
if naming_scheme == 'exp_id':
# name becomes exp_id
names = [f.parts[-3] for f in files]
elif nami... | null |
6,753 | import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimi... | null |
6,754 | import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
class CocoEvaluator(object):
def __init__(self, coco_gt, iou_types):
assert isinstance(iou_types, (list, tupl... | null |
6,755 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def forge_clip_encode(clip, text):
if text is None:
return None
tokens = clip.tokenize(text, return_word_ids=True)
cond, pooled = clip.encode_from_tokens(toke... | null |
6,756 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def rgba2rgbfp32(x):
rgb = x[..., :3].astype(np.float32) / 255.0
a = x[..., 3:4].astype(np.float32) / 255.0
return 0.5 + (rgb - 0.5) * a | null |
6,757 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def to255unit8(x):
return (x * 255.0).clip(0, 255).astype(np.uint8) | null |
6,758 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def safe_numpy(x):
def high_quality_resize(x, size):
class ResizeMode(Enum):
def int_value(self):
def crop_and_resize_image(detected_map, resize_mode, h, w):
if resize_m... | null |
6,759 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def pytorch_to_numpy(x):
return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x] | null |
6,760 | import numpy as np
from lib_layerdiffusion.enums import ResizeMode
from ldm_patched.modules import model_management
import cv2
import torch
def numpy_to_pytorch(x):
y = x.astype(np.float32) / 255.0
y = y[None]
y = np.ascontiguousarray(y.copy())
y = torch.from_numpy(y).float()
return y | null |
6,761 | import torch.nn as nn
import torch
import cv2
import numpy as np
from tqdm import tqdm
from typing import Optional, Tuple
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_... | Zero out the parameters of a module and return it. |
6,762 | import torch.nn as nn
import torch
import cv2
import numpy as np
from tqdm import tqdm
from typing import Optional, Tuple
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_... | null |
6,763 | import gradio as gr
import os
import functools
import torch
import numpy as np
import copy
from modules import scripts
from modules.processing import StableDiffusionProcessing
from lib_layerdiffusion.enums import ResizeMode
from lib_layerdiffusion.utils import rgba2rgbfp32, to255unit8, crop_and_resize_image, forge_clip... | null |
6,764 | import gradio as gr
import os
import functools
import torch
import numpy as np
import copy
from modules import scripts
from modules.processing import StableDiffusionProcessing
from lib_layerdiffusion.enums import ResizeMode
from lib_layerdiffusion.utils import rgba2rgbfp32, to255unit8, crop_and_resize_image, forge_clip... | null |
6,765 | import asyncio
import re
from fastapi import FastAPI
from starlette.middleware.cors import CORSMiddleware
from starlette.responses import HTMLResponse, FileResponse
from tortoise.contrib.fastapi import register_tortoise
from apps.base.views import share_api
from apps.admin.views import admin_api
from core.settings impo... | null |
6,766 | import asyncio
import re
from fastapi import FastAPI
from starlette.middleware.cors import CORSMiddleware
from starlette.responses import HTMLResponse, FileResponse
from tortoise.contrib.fastapi import register_tortoise
from apps.base.views import share_api
from apps.admin.views import admin_api
from core.settings impo... | null |
6,767 | import asyncio
import re
from fastapi import FastAPI
from starlette.middleware.cors import CORSMiddleware
from starlette.responses import HTMLResponse, FileResponse
from tortoise.contrib.fastapi import register_tortoise
from apps.base.views import share_api
from apps.admin.views import admin_api
from core.settings impo... | null |
6,768 | from typing import Union
from fastapi import Header, HTTPException
from fastapi.requests import Request
from core.settings import settings
settings = Settings()
async def admin_required(authorization: Union[str, None] = Header(default=None), request: Request = None):
is_admin = authorization == str(settings.admin... | null |
6,769 | import math
from fastapi import APIRouter, Depends
from apps.admin.depends import admin_required
from apps.admin.pydantics import IDData
from apps.base.models import FileCodes
from core.response import APIResponse
from core.settings import settings
from core.storage import file_storage
class APIResponse(GenericModel, ... | null |
6,770 | import math
from fastapi import APIRouter, Depends
from apps.admin.depends import admin_required
from apps.admin.pydantics import IDData
from apps.base.models import FileCodes
from core.response import APIResponse
from core.settings import settings
from core.storage import file_storage
class IDData(BaseModel):
id:... | null |
6,771 | import math
from fastapi import APIRouter, Depends
from apps.admin.depends import admin_required
from apps.admin.pydantics import IDData
from apps.base.models import FileCodes
from core.response import APIResponse
from core.settings import settings
from core.storage import file_storage
class FileCodes(Model):
id: ... | null |
6,772 | import math
from fastapi import APIRouter, Depends
from apps.admin.depends import admin_required
from apps.admin.pydantics import IDData
from apps.base.models import FileCodes
from core.response import APIResponse
from core.settings import settings
from core.storage import file_storage
class APIResponse(GenericModel, ... | null |
6,773 | import math
from fastapi import APIRouter, Depends
from apps.admin.depends import admin_required
from apps.admin.pydantics import IDData
from apps.base.models import FileCodes
from core.response import APIResponse
from core.settings import settings
from core.storage import file_storage
class APIResponse(GenericModel, ... | null |
6,774 | from apps.admin.depends import admin_required
from apps.base.models import FileCodes
from apps.base.pydantics import SelectFileModel
from apps.base.utils import get_expire_info, get_file_path_name, error_ip_limit, upload_ip_limit
from core.response import APIResponse
from core.settings import settings
from core.storage... | null |
6,775 | from apps.admin.depends import admin_required
from apps.base.models import FileCodes
from apps.base.pydantics import SelectFileModel
from apps.base.utils import get_expire_info, get_file_path_name, error_ip_limit, upload_ip_limit
from core.response import APIResponse
from core.settings import settings
from core.storage... | null |
6,776 | from apps.admin.depends import admin_required
from apps.base.models import FileCodes
from apps.base.pydantics import SelectFileModel
from apps.base.utils import get_expire_info, get_file_path_name, error_ip_limit, upload_ip_limit
from core.response import APIResponse
from core.settings import settings
from core.storage... | null |
6,777 | from apps.admin.depends import admin_required
from apps.base.models import FileCodes
from apps.base.pydantics import SelectFileModel
from apps.base.utils import get_expire_info, get_file_path_name, error_ip_limit, upload_ip_limit
from core.response import APIResponse
from core.settings import settings
from core.storage... | null |
6,778 | from apps.admin.depends import admin_required
from apps.base.models import FileCodes
from apps.base.pydantics import SelectFileModel
from apps.base.utils import get_expire_info, get_file_path_name, error_ip_limit, upload_ip_limit
from core.response import APIResponse
from core.settings import settings
from core.storage... | null |
6,779 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING
from docutils import nodes
from docutils.parsers.rst import Directive, directives
The provided code snippet includes necessary dependencies for implementing the `get_option` function. Write a Python function `def get_option(op... | Get an option. |
6,780 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING
from docutils import nodes
from docutils.parsers.rst import Directive, directives
class video(nodes.General, nodes.Element):
"""A video node."""
class Video(Directive):
"""A docutils video directive."""
has_content ... | Register this extensions with sphinx. |
6,781 | from __future__ import annotations
from prompt_toolkit.filters import Condition
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
session = _current_app_session.get()
if isinstance(session.app, Ba... | Determine if there is a currently focused notebook. |
6,782 | from __future__ import annotations
from prompt_toolkit.filters import Condition
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
session = _current_app_session.get()
if isinstance(session.app, Ba... | Determine if there ares cell in the undo buffer. |
6,783 | from __future__ import annotations
from prompt_toolkit.filters import Condition
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
session = _current_app_session.get()
if isinstance(session.app, Ba... | Determine if a code cell is selected. |
6,784 | from __future__ import annotations
from prompt_toolkit.filters import Condition
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
session = _current_app_session.get()
if isinstance(session.app, Ba... | Determine if there is a currently focused cell. |
6,785 | from __future__ import annotations
from prompt_toolkit.filters import Condition
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
session = _current_app_session.get()
if isinstance(session.app, Ba... | Determine if there is a currently focused notebook. |
6,786 | from __future__ import annotations
import io
import logging
import os
import sys
from functools import partial
from typing import TYPE_CHECKING, cast
from prompt_toolkit.layout.containers import DynamicContainer, FloatContainer, Window
from prompt_toolkit.output.defaults import create_output
from prompt_toolkit.output.... | Get the current application. |
6,787 | from __future__ import annotations
import contextvars
from itertools import chain
from threading import Thread
from typing import TYPE_CHECKING, Sequence, TypeVar, overload
from prompt_toolkit.mouse_events import MouseButton, MouseEventType
The provided code snippet includes necessary dependencies for implementing the... | Return a mouse handler which call a given function on click. |
6,788 | from __future__ import annotations
import contextvars
from itertools import chain
from threading import Thread
from typing import TYPE_CHECKING, Sequence, TypeVar, overload
from prompt_toolkit.mouse_events import MouseButton, MouseEventType
The provided code snippet includes necessary dependencies for implementing the... | Run a function in an thread, but make sure it uses the same contextvars. This is required so that the function will see the right application. |
6,789 | from __future__ import annotations
import logging
import mimetypes
from functools import lru_cache
from typing import TYPE_CHECKING
from upath import UPath
from upath._stat import UPathStatResult
from upath.implementations.http import HTTPPath
MIME_FORMATS = {
"image/svg+xml": "svg",
"image/png": "png",
"im... | Attempt to guess the format of a path. |
6,790 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, NamedTuple
from prompt_toolkit.cache import FastDictCache, SimpleCache
from prompt_toolkit.filters import to_filter
class Converter(NamedTuple):
"""Hold a conversion function and its weight."""
func: Callable
filter_: Filter... | Add a converter to the centralized format conversion system. |
6,791 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
from euporie.core.filters import have_modules
_HTML2TEXT_TABLE_RE = r"(?:(?:.*\|)+[^|]*?(?:\n|$))+"
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and convertin... | Convert HTML to markdown tables using :py:mod:`html2text`. |
6,792 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
from euporie.core.filters import have_modules
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
_pixel_size: tuple[int | None,... | Convert HTML tables to markdown tables using :py:mod:`mtable`. |
6,793 | from __future__ import annotations
import base64
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
import base64
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
_pixel_size: tuple[int | None, int | None]
_hash: str
... | Convert bytes to base64 encoded data. |
6,794 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert HTML text to formatted ANSI using :command:`w3m`. |
6,795 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert HTML text to formatted ANSI using :command:`elinks`. |
6,796 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert HTML text to formatted ANSI using :command:`lynx`. |
6,797 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert HTML text to formatted ANSI using :command:`links`. |
6,798 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert HTML tables to ANSI text using :py:mod:`HTMLParser`. |
6,799 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert LaTeX to ANSI using :py:mod:`flatlatex`. |
6,800 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert LaTeX to ANSI using :py:mod:`pylatexenc`. |
6,801 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert LaTeX to ANSI using :py:mod:`sympy`. |
6,802 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert a PIL image to ANSI text using :py:mod:`timg`. |
6,803 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert a PIL image to ANSI text using :py:mod:`img2unicode`. |
6,804 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`timg`. |
6,805 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`catimg`. |
6,806 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`icat`. |
6,807 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`tiv`. |
6,808 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`viu`. |
6,809 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert image data to ANSI text using :command:`jp2a`. |
6,810 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert PNG data to ANSI text using :command:`img2txt`. |
6,811 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Draw placeholder ANSI text. |
6,812 | from __future__ import annotations
import logging
from functools import partial
from math import ceil
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import chafa_convert_cmd, chafa_convert_py
from euporie.core.convert.formats.pil import set_background
from euporie.core.convert.registry import... | Convert rich objects to formatted ANSI text. |
6,813 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from markdown_it import MarkdownIt
from mdit_py_plugins.amsmath import amsmath_plugin
from mdit_py_plugins.dollarmath.index import dollarmath_plugin
from mdit_py_plugins.texmath.index import texmath_plugin
from pygments import highlight
... | Convert markdown to HTML using :py:mod:`markdownit_py`. |
6,814 | from __future__ import annotations
import asyncio
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import base64_to_bytes_py, imagemagick_convert
from euporie.core.convert.registry import register
from euporie.core.filters import command_exists, have_modules
class... | Render LaTeX as a png image using :command:`dvipng`. Borrowed from IPython. |
6,815 | from __future__ import annotations
import asyncio
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import base64_to_bytes_py, imagemagick_convert
from euporie.core.convert.registry import register
from euporie.core.filters import command_exists, have_modules
class... | Render LaTeX as a png image using :py:module:`matplotlib`. Borrowed from IPython. |
6,816 | from __future__ import annotations
import asyncio
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import base64_to_bytes_py, imagemagick_convert
from euporie.core.convert.registry import register
from euporie.core.filters import command_exists, have_modules
class... | Convert a pillow image to sixels :py:mod:`teimpy`. |
6,817 | from __future__ import annotations
import asyncio
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import base64_to_bytes_py, imagemagick_convert
from euporie.core.convert.registry import register
from euporie.core.filters import command_exists, have_modules
class... | Convert SVG to PNG using :py:mod:`cairosvg`. |
6,818 | from __future__ import annotations
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
from euporie.core.filters import have_modules
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
_pixel_size: tuple[int | None, int | None]
... | Convert LaTeX to SVG using :py:mod:`ziamath`. |
6,819 | from __future__ import annotations
import logging
from functools import partial
from typing import TYPE_CHECKING
from prompt_toolkit.cache import SimpleCache
from prompt_toolkit.formatted_text import to_formatted_text
from euporie.core.convert.registry import register
from euporie.core.ft.ansi import ANSI
from euporie.... | Convert HTML to formatted text. |
6,820 | from __future__ import annotations
import logging
from functools import partial
from typing import TYPE_CHECKING
from prompt_toolkit.cache import SimpleCache
from prompt_toolkit.formatted_text import to_formatted_text
from euporie.core.convert.registry import register
from euporie.core.ft.ansi import ANSI
from euporie.... | Convert ANSI text to formatted text, lexing & formatting automatically. |
6,821 | from __future__ import annotations
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
from euporie.core.filters import have_modules
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
_pixel_size: tuple[int | None, int | None]
... | Convert base64 encoded data to bytes. |
6,822 | from __future__ import annotations
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import (
chafa_convert_cmd,
chafa_convert_py,
imagemagick_convert,
)
from euporie.core.convert.registry import register
from euporie.core.convert.utils import call_subpr... | Convert PNG data to sixels :command:`img2sixel`. |
6,823 | from __future__ import annotations
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import (
chafa_convert_cmd,
chafa_convert_py,
imagemagick_convert,
)
from euporie.core.convert.registry import register
from euporie.core.convert.utils import call_subpr... | Convert a pillow image to sixels :py:mod:`timg`. |
6,824 | from __future__ import annotations
from functools import partial
from typing import TYPE_CHECKING
from euporie.core.convert.formats.common import (
chafa_convert_cmd,
chafa_convert_py,
imagemagick_convert,
)
from euporie.core.convert.registry import register
from euporie.core.convert.utils import call_subpr... | Convert a pillow image to sixels :py:mod:`teimpy`. |
6,825 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.registry import register
from euporie.core.filters import have_modules
log = logging.getLogger(__name__)
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
... | Convert PNG to a pillow image using :py:mod:`PIL`. |
6,826 | from __future__ import annotations
import base64
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.utils import call_subproc
from euporie.core.current import get_app
import base64
class Datum(Generic[T], metaclass=_MetaDatum):
"""Class for storing and converting display data."""
_pixe... | Convert base64 encoded data to bytes. |
6,827 | from __future__ import annotations
import base64
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.utils import call_subproc
from euporie.core.current import get_app
async def call_subproc(
data: str | bytes,
cmd: list[Any],
use_tempfile: bool = False,
suffix: str = "",
) -> byt... | Convert image data to PNG bytes using ``imagemagick``. |
6,828 | from __future__ import annotations
import base64
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.utils import call_subproc
from euporie.core.current import get_app
async def call_subproc(
data: str | bytes,
cmd: list[Any],
use_tempfile: bool = False,
suffix: str = "",
) -> byt... | Convert image data to ANSI text using :command:`chafa`. |
6,829 | from __future__ import annotations
import base64
import logging
from typing import TYPE_CHECKING
from euporie.core.convert.utils import call_subproc
from euporie.core.current import get_app
def get_app() -> BaseApp:
"""Get the current active (running) Application."""
from euporie.core.app import BaseApp
s... | Convert image data to ANSI text using ::`chafa.py`. |
6,830 | from __future__ import annotations
import asyncio
import hashlib
import inspect
import io
import logging
import threading
from typing import TYPE_CHECKING, Generic, TypeVar
from weakref import ReferenceType, WeakValueDictionary, finalize, ref
import imagesize
from PIL.Image import Image as PilImage
from prompt_toolkit.... | Create or return the conversion IO loop. The loop will be running on a separate thread. |
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