id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
167,062 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,063 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,064 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | Creates a relative symlink to a target from a link location, ensuring parent directories exist. The target can be either a file or a directory. Parameters: - target: The path to the target file or directory. This can be an absolute or a relative path. - link_name: The path where the symlink will be created. This should... |
167,065 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,066 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,067 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,068 | import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.fut... | null |
167,074 | import os
from functools import wraps
import psutil
def rlimitproc(pp, rlim):
limit_nofile = 131071
def psfunc(func, *args, **kwargs):
def get_file_limit(pid=None):
if pid is None:
pid = os.getpid()
ps = psfunc(psutil.Process, pid)
if ps is not None:
nofile = rlimitproc(ps, psutil.RLIMIT_NO... | null |
167,076 | import os
import numpy as np
from scipy.stats import mode
from src.utils import have_cv2, have_pillow
def align_image(img_file):
import cv2
from imutils.perspective import four_point_transform
try:
# Load the image
# img_file = '/home/jon/Downloads/fastfood.jpg'
# img_file = "/home/j... | null |
167,077 | import os
import numpy as np
from scipy.stats import mode
from src.utils import have_cv2, have_pillow
def get_image_types():
if have_pillow:
from PIL import Image
exts = Image.registered_extensions()
image_types0 = {ex for ex, f in exts.items() if f in Image.OPEN}
image_types0 = sort... | null |
167,079 | import textwrap
import re
from src.utils import flatten_list, have_emoji, have_langid
def init_sentence_state():
sentence_state = dict(sentence_list=[], index=0)
return sentence_state | null |
167,080 | import textwrap
import re
from src.utils import flatten_list, have_emoji, have_langid
def unpack_state(sentence_state):
def pack_state(sentence_state, *args):
def _get_sentences(response, verbose=False, min_start=15, max_length=250):
def clean_sentence(sentence, verbose=False):
def get_sentence(response, sentence_stat... | null |
167,081 | import textwrap
import re
from src.utils import flatten_list, have_emoji, have_langid
def detect_language(prompt, supported_languages, verbose=False):
if not have_langid:
# if no package, just return english
return "en"
import langid
# Fast language autodetection
if len(prompt) > 15:
... | null |
167,084 | import copy
import json
import os
import types
import uuid
from typing import Any, Dict, List, Union, Optional, Tuple, Mapping
import time
import queue
import pathlib
from datetime import datetime
from langchain.schema import BasePromptTemplate
from langchain.chains import LLMChain
from langchain.chains import MapReduc... | null |
167,085 | import copy
import json
import os
import types
import uuid
from typing import Any, Dict, List, Union, Optional, Tuple, Mapping
import time
import queue
import pathlib
from datetime import datetime
from langchain.schema import BasePromptTemplate
from langchain.chains import LLMChain
from langchain.chains import MapReduc... | null |
167,086 | import copy
import json
import os
import types
import uuid
from typing import Any, Dict, List, Union, Optional, Tuple, Mapping
import time
import queue
import pathlib
from datetime import datetime
from langchain.schema import BasePromptTemplate
from langchain.chains import LLMChain
from langchain.chains import MapReduc... | null |
167,087 | import copy
import json
import os
import types
import uuid
from typing import Any, Dict, List, Union, Optional, Tuple, Mapping
import time
import queue
import pathlib
from datetime import datetime
from langchain.schema import BasePromptTemplate
from langchain.chains import LLMChain
from langchain.chains import MapReduc... | null |
167,088 | import copy
import json
import os
import types
import uuid
from typing import Any, Dict, List, Union, Optional, Tuple, Mapping
import time
import queue
import pathlib
from datetime import datetime
from langchain.schema import BasePromptTemplate
from langchain.chains import LLMChain
from langchain.chains import MapReduc... | Load summarizing chain. Args: llm: Language Model to use in the chain. chain_type: Type of document combining chain to use. Should be one of "stuff", "map_reduce", and "refine". verbose: Whether chains should be run in verbose mode or not. Note that this applies to all chains that make up the final chain. Returns: A ch... |
167,089 | from io import IOBase
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from langchain._api import warn_deprecated
from langchain.agents import AgentExecutor, BaseSingleActionAgent
from langchain_experimental.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF,
FUNCTIONS_WITH_MULTI... | Create csv agent by loading to a dataframe and using pandas agent. |
167,091 | from enum import Enum
openai_supports_functiontools = ["gpt-4-0613", "gpt-4-32k-0613", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",
"gpt-4-1106-preview", "gpt-35-turbo-1106"]
def does_support_functiontools(inference_server, model_name):
if any([inference_server.startswith(x) for... | null |
167,092 | from enum import Enum
openai_supports_json_mode = ["gpt-4-1106-preview", "gpt-35-turbo-1106"]
def does_support_json_mode(inference_server, model_name):
if any([inference_server.startswith(x) for x in ['openai_azure', 'openai_azure_chat']]):
return model_name.lower() in openai_supports_json_mode
elif an... | null |
167,093 | from enum import Enum
def t5_type(model_name):
return 't5' == model_name.lower() or \
't5-' in model_name.lower() or \
'flan-' in model_name.lower() or \
'fastchat-t5' in model_name.lower() | null |
167,094 | from enum import Enum
def get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt,
model_name, inference_server, model_path_llama,
doc_json_mode,
prompt_query_type='simple'):
if prompt_qu... | null |
167,095 | from enum import Enum
def gr_to_lg(image_audio_loaders,
pdf_loaders,
url_loaders,
use_pymupdf=None,
use_unstructured_pdf=None,
use_pypdf=None,
enable_pdf_ocr=None,
enable_pdf_doctr=None,
try_pdf_as_html=None,
... | null |
167,100 | import uuid
from enums import LangChainMode
def length_db1():
# For MyData:
# 0: db
# 1: userid and dbid
# 2: username
# For others:
# 0: db
# 1: dbid
# 2: None
return 3
class LangChainMode(Enum):
"""LangChain mode"""
DISABLED = "Disabled"
LLM = "LLM"
WIKI = "wiki"
... | null |
167,101 | import uuid
from enums import LangChainMode
class LangChainMode(Enum):
"""LangChain mode"""
DISABLED = "Disabled"
LLM = "LLM"
WIKI = "wiki"
WIKI_FULL = "wiki_full"
USER_DATA = "UserData"
MY_DATA = "MyData"
GITHUB_H2OGPT = "github h2oGPT"
H2O_DAI_DOCS = "DriverlessAI docs"
def set_... | null |
167,102 | import uuid
from enums import LangChainMode
class LangChainMode(Enum):
"""LangChain mode"""
DISABLED = "Disabled"
LLM = "LLM"
WIKI = "wiki"
WIKI_FULL = "wiki_full"
USER_DATA = "UserData"
MY_DATA = "MyData"
GITHUB_H2OGPT = "github h2oGPT"
H2O_DAI_DOCS = "DriverlessAI docs"
def get_... | null |
167,103 | import uuid
from enums import LangChainMode
class LangChainMode(Enum):
"""LangChain mode"""
DISABLED = "Disabled"
LLM = "LLM"
WIKI = "wiki"
WIKI_FULL = "wiki_full"
USER_DATA = "UserData"
MY_DATA = "MyData"
GITHUB_H2OGPT = "github h2oGPT"
H2O_DAI_DOCS = "DriverlessAI docs"
def get_... | null |
167,104 | import uuid
from enums import LangChainMode
def get_dbid(db1):
return db1[1] | null |
167,105 | import uuid
from enums import LangChainMode
def length_db1():
# For MyData:
# 0: db
# 1: userid and dbid
# 2: username
# For others:
# 0: db
# 1: dbid
# 2: None
return 3
def set_dbid(db1):
# can only call this after function called so for specific user, not in gr.State() that oc... | null |
167,109 | import ast
import glob
import pickle
import uuid
from typing import List, Optional
import os
import bz2
import csv
import numpy as np
import pandas as pd
import pytest
from matplotlib import pyplot as plt
from langchain.docstore.document import Document
from langchain.document_loaders import MWDumpLoader
def unescape(x... | null |
167,111 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,112 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,113 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,114 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,115 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,116 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,118 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,119 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,120 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,121 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,122 | import ast
import copy
import functools
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import uuid
import warnings
from datetime import datetime
import httpx
import requests
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutErr... | null |
167,124 | from __future__ import annotations
from typing import Iterable
from gradio.themes.soft import Soft
from gradio.themes import Color, Size
from gradio.themes.utils import colors, sizes, fonts
h2o_logo = '<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="100%" height="100%"' \
' vi... | null |
167,125 | from __future__ import annotations
from typing import Iterable
from gradio.themes.soft import Soft
from gradio.themes import Color, Size
from gradio.themes.utils import colors, sizes, fonts
def get_simple_title(title, description):
return f"""{description}<h1 align="center"> {title}</h1>""" | null |
167,126 | from __future__ import annotations
from typing import Iterable
from gradio.themes.soft import Soft
from gradio.themes import Color, Size
from gradio.themes.utils import colors, sizes, fonts
def get_dark_js() -> str:
return """
if (document.querySelectorAll('.dark').length) {
document.querySelec... | null |
167,127 | from __future__ import annotations
from typing import Iterable
from gradio.themes.soft import Soft
from gradio.themes import Color, Size
from gradio.themes.utils import colors, sizes, fonts
def get_heap_js(heapAppId: str) -> str:
return (
"""globalThis.window.heap=window.heap||[],heap.load=function(e,t){wi... | null |
167,128 | from __future__ import annotations
from typing import Iterable
from gradio.themes.soft import Soft
from gradio.themes import Color, Size
from gradio.themes.utils import colors, sizes, fonts
The provided code snippet includes necessary dependencies for implementing the `wrap_js_to_lambda` function. Write a Python funct... | Generates a JS code representing JS lambda that wraps all given '*args' code strings. The lambda function has number of parameters based on 'num_params' and returns them without modification in an array. Lambda with zero parameters returns an empty array. |
167,129 | import ast
import time
from enums import PromptType, gpt_token_mapping, \
anthropic_mapping, google_mapping, mistralai_mapping
def is_vision_model(base_model):
return base_model.startswith('llava-') or \
base_model.startswith('liuhaotian/llava-') or \
base_model.startswith('Qwen-VL') or \
... | null |
167,130 | import ast
import time
from enums import PromptType, gpt_token_mapping, \
anthropic_mapping, google_mapping, mistralai_mapping
prompt_types = []
def get_prompt(prompt_type, prompt_dict, context, reduced, making_context, return_dict=False,
system_prompt=None, histi=-1):
prompt_dict_error = ''
... | null |
167,133 | import ast
import time
from enums import PromptType, gpt_token_mapping, \
anthropic_mapping, google_mapping, mistralai_mapping
def get_vllm_extra_dict(tokenizer, stop_sequences=[], repetition_penalty=None):
stop_token_ids = [tokenizer.added_tokens_encoder[x] for x in stop_sequences if
has... | null |
167,134 | import ast
import time
from enums import PromptType, gpt_token_mapping, \
anthropic_mapping, google_mapping, mistralai_mapping
def step_forward_prompts(which):
if which == 1:
return """Let’s think step by step."""
elif which == 2:
return """Take a deep breath and work on this problem step-by... | null |
167,135 | import ast
import time
from enums import PromptType, gpt_token_mapping, \
anthropic_mapping, google_mapping, mistralai_mapping
def get_llava_prompts():
return [('None', ''),
('Auto', 'auto'),
('Generic', "Describe the image and what does the image say?"),
('OCR', "Read all t... | null |
167,142 | import os
import filelock
from diffusers import DiffusionPipeline
import torch
from src.utils import makedirs
from src.vision.sdxl import get_device
def get_pipe_make_image(gpu_id, refine=True):
device = get_device(gpu_id)
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1... | null |
167,143 | import os
import filelock
import torch
from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image
from diffusers.utils import load_image
from src.utils import cuda_vis_check, makedirs
def get_pipe_make_image(gpu_id='auto'):
# https://huggingface.co/stabilityai/sdxl-turbo
device = get_device(gpu... | null |
167,145 | import os
import uuid
from src.utils import makedirs, sanitize_filename, get_gradio_tmp
def sanitize_filename(name, file_length_limit=250):
"""
Sanitize file *base* names.
:param name: name to sanitize
:param file_length_limit: bit smaller than 256 for safety
:return:
"""
bad_chars = ['[', ... | null |
167,146 | import base64
import os
import time
import uuid
from io import BytesIO
import numpy as np
def img_to_base64(image_file):
# assert image_file.lower().endswith('jpg') or image_file.lower().endswith('jpeg')
from PIL import Image
EXTENSIONS = {'.png': 'PNG', '.apng': 'PNG', '.blp': 'BLP', '.bmp': 'BMP', '.dib... | null |
167,147 | import base64
import os
import time
import uuid
from io import BytesIO
import numpy as np
def base64_to_img(img_str, output_path):
# Split the string on "," to separate the metadata from the base64 data
meta, base64_data = img_str.split(",", 1)
# Extract the format from the metadata
img_format = meta.s... | null |
167,148 | import base64
import os
import time
import uuid
from io import BytesIO
import numpy as np
def fix_llava_prompt(file, prompt, allow_prompt_auto=True):
if prompt in ['auto', None] and allow_prompt_auto:
prompt = "Describe the image and what does the image say?"
# prompt = "According to the image, desc... | null |
167,149 | import os
import filelock
from diffusers import DiffusionPipeline
import torch
from src.utils import makedirs
from src.vision.sdxl import get_device
def get_pipe_make_image(gpu_id):
device = get_device(gpu_id)
pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2-1024px-aesthetic",
... | null |
167,150 | import io
import numpy as np
import pydub
from src.utils import have_pyrubberband
def get_wave_header(frame_input=b"", channels=1, sample_width=2, sample_rate=24000):
# This will create a wave header then append the frame input
# It should be first on a streaming wav file
# Other frames better should not ha... | null |
167,151 | import io
import numpy as np
import pydub
from src.utils import have_pyrubberband
def get_no_audio(return_as_byte=True, return_nonbyte_as_file=False, sr=None):
if return_as_byte:
return b""
else:
if return_nonbyte_as_file:
return None
else:
assert sr is not None
... | null |
167,152 | import io
import numpy as np
import pydub
from src.utils import have_pyrubberband
def pydub_to_np(audio: pydub.AudioSegment) -> (np.ndarray, int):
"""
Converts pydub audio segment into np.int16 of shape [duration_in_seconds*sample_rate, channels],
"""
return np.array(audio.get_array_of_samples(), dtype=... | null |
167,154 | from __future__ import annotations
import base64
from pkg_resources import resource_filename
import os
import time
from io import BytesIO
import numpy as np
import scipy
import wavio
import soundfile as sf
import torch
import librosa
from src.tts_sentence_parsing import init_sentence_state, get_sentence
from src.tts_ut... | null |
167,158 | from __future__ import annotations
import base64
from pkg_resources import resource_filename
import os
import time
from io import BytesIO
import numpy as np
import scipy
import wavio
import soundfile as sf
import torch
import librosa
from src.tts_sentence_parsing import init_sentence_state, get_sentence
from src.tts_ut... | null |
167,160 | from __future__ import annotations
import functools
import io
import os
import tempfile
import filelock
import numpy as np
import uuid
import subprocess
import time
from src.enums import coqui_lock_name
from src.tts_sentence_parsing import init_sentence_state, get_sentence, clean_sentence, detect_language
from src.tts_... | null |
167,161 | from __future__ import annotations
import functools
import io
import os
import tempfile
import filelock
import numpy as np
import uuid
import subprocess
import time
from src.enums import coqui_lock_name
from src.tts_sentence_parsing import init_sentence_state, get_sentence, clean_sentence, detect_language
from src.tts_... | null |
167,164 | from __future__ import annotations
import functools
import io
import os
import tempfile
import filelock
import numpy as np
import uuid
import subprocess
import time
from src.enums import coqui_lock_name
from src.tts_sentence_parsing import init_sentence_state, get_sentence, clean_sentence, detect_language
from src.tts_... | null |
167,165 | import functools
import json
from src.enums import t5_type
from src.utils import have_optimum
def t5_type(model_name):
return 't5' == model_name.lower() or \
't5-' in model_name.lower() or \
'flan-' in model_name.lower() or \
'fastchat-t5' in model_name.lower()
class H2OExLlamaTokenizer(Ex... | null |
167,166 | import functools
import json
from src.enums import t5_type
from src.utils import have_optimum
def get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token):
tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
... | null |
167,167 | import torch
from transformers import StoppingCriteria, StoppingCriteriaList
from enums import PromptType, t5_type
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], stop_words=[], encounters=[], device="cuda", model_max_length=None, tokenizer=None,
truncation_generation=Fals... | null |
167,168 | import functools
import os
import math
import csv
import datetime
import filelock
import gradio as gr
from src.enums import no_server_str
from src.utils import is_gradio_version4
def get_chatbot_name(base_model, model_path_llama, inference_server='', prompt_type='', model_label_prefix='', debug=False):
#have_infere... | null |
167,169 | def make_css_base() -> str:
return """
#col_container {margin-left: auto; margin-right: auto; text-align: left;}
body.dark{#warning {background-color: #555555};}
#sidebar {
order: 1;
order: 2;
}
}
#col-tabs {
order: 2;
order: 1;
}
}
#sm... | null |
167,171 | import gradio as gr
import torch
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
from h2oai_pipeline import H2OTextGenerationPipeline
def generate(query):
return generate_text(query, max_new_tokens=150)[0]['generated_text']
def process_example(args):
for x in generate(args):
pass... | null |
167,172 | import os
import sys
from functools import partial
from typing import List, Union
import numpy as np
from src.loaders import get_loaders, get_tokenizer
from src.prompter import generate_prompt, prompt_types, PromptType
from src.utils import get_githash, copy_code, H2O_Fire
import torch
def train(
save_code: boo... | null |
167,173 | import os
import sys
from functools import partial
from typing import List, Union
import numpy as np
if os.path.dirname(os.path.abspath(__file__)) not in sys.path:
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
if os.path.dirname('src') not in sys.path:
sys.path.append('src')
from src.loaders impor... | null |
167,174 | import shutil
import pandas as pd
import os
import huggingface_hub
import pytest
from datasets import load_dataset
def test_create_data_cards(dataset_name, link_to_source):
if dataset_name != "openassistant_oasst1_h2ogpt_llama2_chat":
return
#
assert os.path.exists("README-template.md"), "must be r... | null |
167,175 | import os
import sys
from src.utils_sys import protect_stdout_stderr
from src.gen import main
from src.utils import H2O_Fire
def main(
load_8bit: bool = False,
load_4bit: bool = False,
low_bit_mode: int = 1,
load_half: bool = None,
use_flash_attention_2=False,
load_gptq:... | null |
167,182 | import abc
import ast
import collections
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,
List,
Optional,
OrderedDict,
Union,
)
from h2ogpt_client._gradio_client import GradioClientWrapper
from h2ogpt_client._h2ogpt_enums import (
DocumentSubset,
LangChainAction,
La... | Convert given params to the order of params in h2oGPT. |
167,183 | import json
import platform
import re
import time
import traceback
from pathlib import Path
from time import sleep
from urllib import parse
import requests
import uigf_converter
from config import Config, version
from gacha_metadata import (
WEB_CACHE_PATH,
WEB_CACHE_PATH_GLOB,
gacha_query_type_ids,
gac... | null |
167,184 | import json
import platform
import re
import time
import traceback
from pathlib import Path
from time import sleep
from urllib import parse
import requests
import uigf_converter
from config import Config, version
from gacha_metadata import (
WEB_CACHE_PATH,
WEB_CACHE_PATH_GLOB,
gacha_query_type_ids,
gac... | null |
167,185 | import json
import platform
import re
import time
import traceback
from pathlib import Path
from time import sleep
from urllib import parse
import requests
import uigf_converter
from config import Config, version
from gacha_metadata import (
WEB_CACHE_PATH,
WEB_CACHE_PATH_GLOB,
gacha_query_type_ids,
gac... | null |
167,186 | import json
import platform
import re
import time
import traceback
from pathlib import Path
from time import sleep
from urllib import parse
import requests
import uigf_converter
from config import Config, version
from gacha_metadata import (
WEB_CACHE_PATH,
WEB_CACHE_PATH_GLOB,
gacha_query_type_ids,
gac... | null |
167,187 | import json
import platform
import re
import time
import traceback
from pathlib import Path
from time import sleep
from urllib import parse
import requests
import uigf_converter
from config import Config, version
from gacha_metadata import (
WEB_CACHE_PATH,
WEB_CACHE_PATH_GLOB,
gacha_query_type_ids,
gac... | null |
167,188 | import pathlib
import os
import sys
import time
from gacha_metadata import (
gacha_query_type_ids,
gacha_query_type_names,
gacha_query_type_dict,
gacha_type_dict,
)
from utils import logger
from config import version
def id_generator():
id = 1000000000000000000
while True:
id = id + 1
... | null |
167,189 | import json
import os
import pathlib
from typing import Union
from utils import logger
version = "v4.2.0.11162254"
The provided code snippet includes necessary dependencies for implementing the `get_version` function. Write a Python function `def get_version()` to solve the following problem:
从PC启动器api获取游戏版本号
Here is... | 从PC启动器api获取游戏版本号 |
167,190 | import json
import os
import gacha_metadata
import uigf_converter
from utils import logger, gen_path
from gacha_metadata import (
gacha_query_type_ids,
gacha_query_type_names,
gacha_query_type_dict,
gacha_type_dict,
)
def write_logs(uid, gacha_log):
import xlsxwriter
import time
t = time.str... | null |
167,191 | from clipboard_utils import get_url_from_clipboard
from utils import logger
import subprocess
def get_url_from_clipboard():
text = get_clipboad_text_or_html()
logger.debug(f"get_clipboad_text_or_html {text}")
url = get_url_from_string(text)
logger.debug(f"get_url_from_string {url}")
return url
def... | null |
167,192 | import re
import os
import sympy
import pandas as pd
from tot.tasks.base import Task, DATA_PATH
from tot.prompts.game24 import *
def get_current_numbers(y: str) -> str:
last_line = y.strip().split('\n')[-1]
return last_line.split('left: ')[-1].split(')')[0] | null |
167,193 | import os
import json
import argparse
from tot.tasks import get_task
from tot.methods.bfs import solve, naive_solve
from tot.models import gpt_usage
def get_task(name):
def solve(args, task, idx, to_print=True):
def naive_solve(args, task, idx, to_print=True):
def gpt_usage(backend="gpt-4"):
def run(args):
tas... | null |
167,194 | import os
import json
import argparse
from tot.tasks import get_task
from tot.methods.bfs import solve, naive_solve
from tot.models import gpt_usage
def parse_args():
args = argparse.ArgumentParser()
args.add_argument('--backend', type=str, choices=['gpt-4', 'gpt-3.5-turbo'], default='gpt-4')
args.add_argu... | null |
167,195 | import argparse
import os, sys
import uuid
from pathlib import Path
import main as detection
import submitit
def parse_args():
detection_parser = detection.get_args_parser()
parser = argparse.ArgumentParser("Submitit for detection", parents=[detection_parser])
parser.add_argument("--ngpus", default=8, type... | null |
167,196 | import argparse
import os, sys
import uuid
from pathlib import Path
import main as detection
import submitit
def get_shared_folder() -> Path:
user = os.getenv("USER")
if Path("/comp_robot").is_dir():
p = Path(f"/comp_robot/{user}/experiments")
p.mkdir(exist_ok=True)
return p
raise Ru... | null |
167,197 | import argparse
import datetime
import json
import random
import time
from pathlib import Path
import os, sys
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
from util.get_param_dicts import get_param_dict
from util.logger import setup_logger
from util.slconfig import DictAct... | null |
167,200 | import os
import contextlib
import copy
import numpy as np
import torch
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from util.misc import all_gather
The provided code snippet includes necessary dependencies for implementing the `evaluate` function. W... | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None |
167,203 | 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 |
167,205 | import PIL
from PIL import Image
import torch
import os
import torchvision.transforms.functional as F
import numpy as np
import random
from .random_crop import random_crop
from util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
The provided code snippet includes necessary dependencies for implementing the `lig... | color channel swap in image image: A PIL image |
167,206 | import PIL
from PIL import Image
import torch
import os
import torchvision.transforms.functional as F
import numpy as np
import random
from .random_crop import random_crop
from util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
The provided code snippet includes necessary dependencies for implementing the `rot... | Rotate image and bounding box image: A Pil image (w, h) boxes: A tensors of dimensions (#objects, 4) Out: rotated image (w, h), rotated boxes |
167,207 | 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 |
167,208 | import json
from pathlib import Path
import random
import os
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from datasets.data_util import preparing_dataset
import datasets.transforms as T
from util.box_ops import box_cxcywh_to_xyxy, box_iou
The provided code snippet ... | label: Tensor(K) |
167,209 | import json
from pathlib import Path
import random
import os
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from datasets.data_util import preparing_dataset
import datasets.transforms as T
from util.box_ops import box_cxcywh_to_xyxy, box_iou
def convert_coco_poly_to_m... | null |
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