id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
8,661 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Show current status of database logging |
8,662 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Turn on logging for all prompts |
8,663 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Turn off logging for all prompts |
8,664 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Show recent logged prompts and their responses |
8,665 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Manage available models |
8,666 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | List available models |
8,667 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Show or set the default model |
8,668 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Manage stored prompt templates |
8,669 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | List available prompt templates |
8,670 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | List current aliases |
8,671 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Set an alias for a model Example usage: \b $ llm aliases set turbo gpt-3.5-turbo |
8,672 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Remove an alias Example usage: \b $ llm aliases remove turbo |
8,673 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Output the path to the aliases.json file |
8,674 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | List installed plugins |
8,675 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Show the specified prompt template |
8,676 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Edit the specified prompt template using the default $EDITOR |
8,677 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Output the path to the templates directory |
8,678 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Install packages from PyPI into the same environment as LLM |
8,679 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Uninstall Python packages from the LLM environment |
8,680 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Embed text and store or return the result |
8,681 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Store embeddings for multiple strings at once Input can be CSV, TSV or a JSON list of objects. The first column is treated as an ID - all other columns are assumed to be text that should be concatenated together in order to calculate the embeddings. Input data can come from one of three sources: \b 1. A CSV, JSON, TSV ... |
8,682 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Return top N similar IDs from a collection Example usage: \b llm similar my-collection -c "I like cats" Or to find content similar to a specific stored ID: \b llm similar my-collection 1234 |
8,683 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Manage available embedding models |
8,684 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | List available embedding models |
8,685 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Show or set the default embedding model |
8,686 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | View and manage collections of embeddings |
8,687 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Output the path to the embeddings database |
8,688 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | View a list of collections |
8,689 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | Delete the specified collection Example usage: \b llm collections delete my-collection |
8,690 | import click
from click_default_group import DefaultGroup
from dataclasses import asdict
import io
import json
from llm import (
Collection,
Conversation,
Response,
Template,
UnknownModelError,
encode,
get_embedding_models_with_aliases,
get_embedding_model_aliases,
get_embedding_mode... | null |
8,691 | import click
import httpx
import json
import textwrap
from typing import List, Dict
The provided code snippet includes necessary dependencies for implementing the `remove_dict_none_values` function. Write a Python function `def remove_dict_none_values(d)` to solve the following problem:
Recursively remove keys with va... | Recursively remove keys with value of None or value of a dict that is all values of None |
8,692 | import click
import httpx
import json
import textwrap
from typing import List, Dict
class _LogTransport(httpx.BaseTransport):
def __init__(self, transport: httpx.BaseTransport):
self.transport = transport
def handle_request(self, request: httpx.Request) -> httpx.Response:
response = self.transpo... | null |
8,693 | import datetime
from typing import Callable, List
def m001_initial(db):
# Ensure the original table design exists, so other migrations can run
if db["log"].exists():
# It needs to have the chat_id column
if "chat_id" not in db["log"].columns_dict:
db["log"].add_column("chat_id")
... | null |
8,694 | import datetime
from typing import Callable, List
def m002_id_primary_key(db):
db["log"].transform(pk="id") | null |
8,695 | import datetime
from typing import Callable, List
def m003_chat_id_foreign_key(db):
db["log"].transform(types={"chat_id": int})
db["log"].add_foreign_key("chat_id", "log", "id") | null |
8,696 | import datetime
from typing import Callable, List
def m004_column_order(db):
db["log"].transform(
column_order=(
"id",
"model",
"timestamp",
"prompt",
"system",
"response",
"chat_id",
)
) | null |
8,697 | import datetime
from typing import Callable, List
def m004_drop_provider(db):
db["log"].transform(drop=("provider",)) | null |
8,698 | import datetime
from typing import Callable, List
def m005_debug(db):
db["log"].add_column("debug", str)
db["log"].add_column("duration_ms", int) | null |
8,699 | import datetime
from typing import Callable, List
def m006_new_logs_table(db):
columns = db["log"].columns_dict
for column, type in (
("options_json", str),
("prompt_json", str),
("response_json", str),
("reply_to_id", int),
):
# It's possible people running developm... | null |
8,700 | import datetime
from typing import Callable, List
def m007_finish_logs_table(db):
db["log"].transform(
drop={"debug"},
rename={"timestamp_utc": "datetime_utc"},
drop_foreign_keys=("chat_id",),
)
with db.conn:
db.execute("alter table log rename to logs") | null |
8,701 | import datetime
from typing import Callable, List
def m008_reply_to_id_foreign_key(db):
db["logs"].add_foreign_key("reply_to_id", "logs", "id") | null |
8,702 | import datetime
from typing import Callable, List
def m008_fix_column_order_in_logs(db):
# reply_to_id ended up at the end after foreign key added
db["logs"].transform(
column_order=(
"id",
"model",
"prompt",
"system",
"prompt_json",
... | null |
8,703 | import datetime
from typing import Callable, List
def m009_delete_logs_table_if_empty(db):
# We moved to a new table design, but we don't delete the table
# if someone has put data in it
if not db["logs"].count:
db["logs"].drop() | null |
8,704 | import datetime
from typing import Callable, List
def m010_create_new_log_tables(db):
db["conversations"].create(
{
"id": str,
"name": str,
"model": str,
},
pk="id",
)
db["responses"].create(
{
"id": str,
"model": s... | null |
8,705 | import datetime
from typing import Callable, List
def m011_fts_for_responses(db):
db["responses"].enable_fts(["prompt", "response"], create_triggers=True) | null |
8,706 | from dataclasses import dataclass, field
import datetime
from .errors import NeedsKeyException
from itertools import islice
import re
import time
from typing import Any, Dict, Iterable, Iterator, List, Optional, Set, Union
from abc import ABC, abstractmethod
import json
from pydantic import BaseModel
from ulid import U... | null |
8,707 | from pluggy import HookimplMarker
from pluggy import HookspecMarker
The provided code snippet includes necessary dependencies for implementing the `register_commands` function. Write a Python function `def register_commands(cli)` to solve the following problem:
Register additional CLI commands, e.g. 'llm mycommand ...... | Register additional CLI commands, e.g. 'llm mycommand ... |
8,708 | from pluggy import HookimplMarker
from pluggy import HookspecMarker
The provided code snippet includes necessary dependencies for implementing the `register_models` function. Write a Python function `def register_models(register)` to solve the following problem:
Register additional model instances representing LLM mod... | Register additional model instances representing LLM models that can be called |
8,709 | from pluggy import HookimplMarker
from pluggy import HookspecMarker
The provided code snippet includes necessary dependencies for implementing the `register_embedding_models` function. Write a Python function `def register_embedding_models(register)` to solve the following problem:
Register additional model instances ... | Register additional model instances that can be used for embedding |
8,710 | from llm import EmbeddingModel, Model, hookimpl
import llm
from llm.utils import dicts_to_table_string, remove_dict_none_values, logging_client
import click
import datetime
import httpx
import openai
import os
from typing import List, Iterable, Iterator, Optional, Union
import json
import yaml
class Chat(Model):
ne... | null |
8,711 | from llm import EmbeddingModel, Model, hookimpl
import llm
from llm.utils import dicts_to_table_string, remove_dict_none_values, logging_client
import click
import datetime
import httpx
import openai
import os
from typing import List, Iterable, Iterator, Optional, Union
import json
import yaml
class OpenAIEmbeddingMode... | null |
8,712 | from llm import EmbeddingModel, Model, hookimpl
import llm
from llm.utils import dicts_to_table_string, remove_dict_none_values, logging_client
import click
import datetime
import httpx
import openai
import os
from typing import List, Iterable, Iterator, Optional, Union
import json
import yaml
def dicts_to_table_strin... | null |
8,713 | from llm import EmbeddingModel, Model, hookimpl
import llm
from llm.utils import dicts_to_table_string, remove_dict_none_values, logging_client
import click
import datetime
import httpx
import openai
import os
from typing import List, Iterable, Iterator, Optional, Union
import json
import yaml
def not_nulls(data) -> d... | null |
8,714 | from llm import EmbeddingModel, Model, hookimpl
import llm
from llm.utils import dicts_to_table_string, remove_dict_none_values, logging_client
import click
import datetime
import httpx
import openai
import os
from typing import List, Iterable, Iterator, Optional, Union
import json
import yaml
def combine_chunks(chunk... | null |
8,715 | import re
import json
import typing
import requests
import lxml.html
from typing import Any, Sequence, Type
from pydantic import BaseModel
from .errors import PreprocessorError
from .responses import Response, ScrapeResponse
from .apicall import OpenAiCall, Postprocessor, RetryRule
from .utils import logger, _tokens, _... | Combine (possibly paginated) API responses into a single ScrapeResponse. |
8,716 | import re
import json
import typing
import requests
import lxml.html
from typing import Any, Sequence, Type
from pydantic import BaseModel
from .errors import PreprocessorError
from .responses import Response, ScrapeResponse
from .apicall import OpenAiCall, Postprocessor, RetryRule
from .utils import logger, _tokens, _... | Given URL or HTML, return lxml.html.Element |
8,717 | import re
import json
import typing
import requests
import lxml.html
from typing import Any, Sequence, Type
from pydantic import BaseModel
from .errors import PreprocessorError
from .responses import Response, ScrapeResponse
from .apicall import OpenAiCall, Postprocessor, RetryRule
from .utils import logger, _tokens, _... | Given a list of all matching HTML tags, recombine into HTML chunks that can be passed to API. |
8,718 | import re
import json
import typing
import requests
import lxml.html
from typing import Any, Sequence, Type
from pydantic import BaseModel
from .errors import PreprocessorError
from .responses import Response, ScrapeResponse
from .apicall import OpenAiCall, Postprocessor, RetryRule
from .utils import logger, _tokens, _... | Given a Pydantic model, return a simple schema that can be used by SchemaScraper. We don't use Pydantic's schema() method because the additional complexity of JSON Schema adds a lot of extra tokens and in testing did not work as well as the simplified versions. |
8,719 | import json
import pathlib
import logging
import structlog
import typer
from .scrapers import SchemaScraper
from .preprocessors import CSS, XPath
class SchemaScraper(OpenAiCall):
def __init__(
self,
schema: dict | str | list,
extra_preprocessors: list | None = None,
... | null |
8,720 | import lxml.html
import structlog
import tiktoken
from .models import _model_dict
def _tokens(model: str, html: str) -> int:
encoding = tiktoken.encoding_for_model(model)
return len(encoding.encode(html))
_model_dict = {model.name: model for model in models}
The provided code snippet includes necessary depend... | Given HTML, return cost estimate in dollars. This is a very rough estimate and not guaranteed to be accurate. |
8,721 | from __future__ import annotations
from typing import TYPE_CHECKING
import json
from pydantic import ValidationError
from .utils import logger, _tostr
from .errors import InvalidJSON, PostprocessingError
from .responses import Response, ScrapeResponse
class PostprocessingError(ScrapeghostError):
pass
The provided... | Recursively check response for data that is not in the html. |
8,722 | import sys
import json
import lxml.html
import requests
from scrapeghost import SchemaScraper, CSS
def get_urls():
# this page is currently too long for the 8k limit, even with hints
html = requests.get("https://www.ilga.gov/senate/default.asp").text
doc = lxml.html.fromstring(html)
doc.make_links_abso... | null |
8,723 | import ast
import re
from setuptools import setup, find_packages
with open('pypi_desc.md', 'r', encoding='utf-8') as f:
readme = f.read()
The provided code snippet includes necessary dependencies for implementing the `get_version_string` function. Write a Python function `def get_version_string()` to solve the fol... | Get the gne version number :return: version number :rtype: str |
8,724 | import ast
import re
from setuptools import setup, find_packages
with open('pypi_desc.md', 'r', encoding='utf-8') as f:
readme = f.read()
The provided code snippet includes necessary dependencies for implementing the `get_author_string` function. Write a Python function `def get_author_string()` to solve the follo... | Get the gne author info :return: author name :rtype: str |
8,725 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
def html2element(html):
html = re.sub('</?br... | null |
8,726 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
def normalize_node(element: HtmlElement):
def pr... | null |
8,727 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
def remove_node(node: HtmlElement):
"""
t... | null |
8,728 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
The provided code snippet includes necessary dep... | 网站上的图片可能有如下几种格式： 完整的绝对路径：https://xxx.com/1.jpg 完全不含 host 的相对路径： /1.jpg 含 host 但是不含 scheme: xxx.com/1.jpg 或者 ://xxx.com/1.jpg :param host: :param url: :return: |
8,729 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
config = read_config()
def read_config():
if... | null |
8,730 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
HIGH_WEIGHT_ARRT_KEYWORD = ['content',
... | null |
8,731 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
The provided code snippet includes necessary dep... | 获取两个字符串的最长公共子串。 构造一个矩阵，横向是字符串1，纵向是字符串2，例如： 青南是天才！？ 听0 0 0 0 00 0 说0 0 0 0 00 0 青1 0 0 0 00 0 南0 1 0 0 00 0 是0 0 1 0 00 0 天0 0 0 1 00 0 才0 0 0 0 10 0 ！0 0 0 0 01 0 显然，只要斜对角线最长的就是最长公共子串 :param str1: :param str2: :return: |
8,732 | import os
import re
import yaml
import unicodedata
from lxml.html import fromstring, HtmlElement
from lxml.html import etree
from urllib.parse import urlparse, urljoin
from .defaults import USELESS_TAG, TAGS_CAN_BE_REMOVE_IF_EMPTY, USELESS_ATTR, HIGH_WEIGHT_ARRT_KEYWORD
The provided code snippet includes necessary dep... | 使用 NFKC 对网页源代码进行归一化，把特殊符号转换为普通符号 注意，中文标点符号可能会被转换成英文标点符合。 :param html: :return: |
8,733 | import os
import re
from setuptools import find_packages, setup
deps = {b: a for a, b in (re.findall(r"^(([^!=<>~]+)(?:[!=<>~].*)?$)", x)[0] for x in _deps)}
def deps_list(*pkgs):
return [deps[pkg] for pkg in pkgs] | null |
8,734 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,735 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,736 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,737 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,738 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,739 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,740 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,741 | import gc
from collections import OrderedDict
from typing import *
import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from cuda import cudart
from PIL import Image
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.trt... | null |
8,742 | import gc
import os
from typing import *
import torch
from .models import BaseModel
from .utilities import (
build_engine,
export_onnx,
optimize_onnx,
)
def create_onnx_path(name, onnx_dir, opt=True):
return os.path.join(onnx_dir, name + (".opt" if opt else "") + ".onnx") | null |
8,743 | from typing import Literal, Optional
import fire
from packaging.version import Version
from ..pip_utils import is_installed, run_pip, version
import platform
def get_cuda_version_from_torch() -> Optional[Literal["11", "12"]]:
try:
import torch
except ImportError:
return None
return torch.ver... | null |
8,744 | import os
import sys
import threading
import time
import tkinter as tk
from multiprocessing import Process, Queue, get_context
from typing import List, Literal
import fire
from PIL import Image, ImageTk
from streamdiffusion.image_utils import postprocess_image
from utils.wrapper import StreamDiffusionWrapper
class Str... | Process for generating images based on a prompt using a specified model. Parameters ---------- queue : Queue The queue to put the generated images in. fps_queue : Queue The queue to put the calculated fps. prompt : str The prompt to generate images from. model_id_or_path : str The name of the model to use for image gen... |
8,745 | import os
import sys
import threading
import time
import tkinter as tk
from multiprocessing import Process, Queue, get_context
from typing import List, Literal
import fire
from PIL import Image, ImageTk
from streamdiffusion.image_utils import postprocess_image
from utils.wrapper import StreamDiffusionWrapper
def _recei... | Setup the Tkinter window and start the thread to receive images. Parameters ---------- queue : Queue The queue to receive images from. fps_queue : Queue The queue to put the calculated fps. |
8,746 | import os
import sys
import time
from multiprocessing import Process, Queue, get_context
from typing import Literal
import fire
from utils.viewer import receive_images
from utils.wrapper import StreamDiffusionWrapper
class StreamDiffusionWrapper:
def __init__(
self,
model_id_or_path: str,
t... | Process for generating images based on a prompt using a specified model. Parameters ---------- queue : Queue The queue to put the generated images in. fps_queue : Queue The queue to put the calculated fps. prompt : str The prompt to generate images from. model_id_or_path : str The name of the model to use for image gen... |
8,747 | import os
import sys
import time
import threading
from multiprocessing import Process, Queue, get_context
from multiprocessing.connection import Connection
from typing import List, Literal, Dict, Optional
import torch
import PIL.Image
from streamdiffusion.image_utils import pil2tensor
import mss
import fire
import tkin... | null |
8,748 | import os
import sys
import time
import threading
from multiprocessing import Process, Queue, get_context
from multiprocessing.connection import Connection
from typing import List, Literal, Dict, Optional
import torch
import PIL.Image
from streamdiffusion.image_utils import pil2tensor
import mss
import fire
import tkin... | Process for generating images based on a prompt using a specified model. Parameters ---------- queue : Queue The queue to put the generated images in. fps_queue : Queue The queue to put the calculated fps. model_id_or_path : str The name of the model to use for image generation. lora_dict : Optional[Dict[str, float]], ... |
8,749 | import io
import os
import sys
import time
from multiprocessing import Process, Queue
from typing import List, Literal, Optional, Dict
import fire
import PIL.Image
import requests
import torch
from tqdm import tqdm
from streamdiffusion.image_utils import postprocess_image
from utils.wrapper import StreamDiffusionWrappe... | Initializes the StreamDiffusionWrapper. Parameters ---------- iterations : int, optional The number of iterations to run, by default 100. model_id_or_path : str The model id or path to load. lora_dict : Optional[Dict[str, float]], optional The lora_dict to load, by default None. Keys are the LoRA names and values are t... |
8,750 | import io
import os
import sys
from typing import List, Literal, Optional, Dict
import fire
import PIL.Image
import requests
import torch
from tqdm import tqdm
from utils.wrapper import StreamDiffusionWrapper
def download_image(url: str):
response = requests.get(url)
image = PIL.Image.open(io.BytesIO(response.c... | Initializes the StreamDiffusionWrapper. Parameters ---------- iterations : int, optional The number of iterations to run, by default 100. model_id_or_path : str The model id or path to load. lora_dict : Optional[Dict[str, float]], optional The lora_dict to load, by default None. Keys are the LoRA names and values are t... |
8,751 | from importlib import import_module
from types import ModuleType
from typing import Dict, Any
from pydantic import BaseModel as PydanticBaseModel, Field
from PIL import Image
import io
def get_pipeline_class(pipeline_name: str) -> ModuleType:
try:
module = import_module(f"pipelines.{pipeline_name}")
ex... | null |
8,752 | from importlib import import_module
from types import ModuleType
from typing import Dict, Any
from pydantic import BaseModel as PydanticBaseModel, Field
from PIL import Image
import io
def bytes_to_pil(image_bytes: bytes) -> Image.Image:
image = Image.open(io.BytesIO(image_bytes))
return image | null |
8,753 | from importlib import import_module
from types import ModuleType
from typing import Dict, Any
from pydantic import BaseModel as PydanticBaseModel, Field
from PIL import Image
import io
def pil_to_frame(image: Image.Image) -> bytes:
frame_data = io.BytesIO()
image.save(frame_data, format="JPEG")
frame_data ... | null |
8,754 | from importlib import import_module
from types import ModuleType
from typing import Dict, Any
from pydantic import BaseModel as PydanticBaseModel, Field
from PIL import Image
import io
def is_firefox(user_agent: str) -> bool:
return "Firefox" in user_agent | null |
8,755 | import os
import sys
import threading
import time
import tkinter as tk
from multiprocessing import Queue
from typing import List
from PIL import Image, ImageTk
from streamdiffusion.image_utils import postprocess_image
def _receive_images(
queue: Queue, fps_queue: Queue, label: tk.Label, fps_label: tk.Label
) -> No... | Setup the Tkinter window and start the thread to receive images. Parameters ---------- queue : Queue The queue to receive images from. fps_queue : Queue The queue to put the calculated fps. |
8,756 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import time
from albert import fine_tuning_utils
from albert import modeling
from albert import squad_utils
import six
import tensorflow.compat.v1 as tf
from tensorflow.compat... | Validate the input FLAGS or throw an exception. |
8,757 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import time
from albert import fine_tuning_utils
from albert import modeling
from albert import squad_utils
import six
import tensorflow.compat.v1 as tf
from tensorflow.compat... | Builds a serving input fn for raw input. |
8,758 | from albert import modeling
from albert import tokenization
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
def _create_model_from_hub(hub_module, is_training, input_ids, input_mask,
segment_ids):
"""Creates an ALBERT model from TF-Hub."""
tags = set()
if is_training:
... | Creates an ALBERT, either from TF-Hub or from scratch. |
8,759 | from albert import modeling
from albert import tokenization
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
The provided code snippet includes necessary dependencies for implementing the `create_vocab` function. Write a Python function `def create_vocab(vocab_file, do_lower_case, spm_model_file, hub_mod... | Creates a vocab, either from vocab file or from a TF-Hub module. |
8,760 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import random
from albert import tokenization
import numpy as np
import six
from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 as tf
FLAGS = flags.FLAGS
def crea... | Create TF example files from `TrainingInstance`s. |
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