id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
7,882 | import argparse
import asyncio
import json
import time
import threading
import uuid
from PIL import Image
from io import BytesIO
import base64
from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests
from transformers import TextIteratorStreamer
import torch
... | null |
7,883 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,884 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,885 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,886 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,887 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,888 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,889 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,890 | import argparse
import dataclasses
from enum import Enum, auto
import json
import time
from typing import List
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from pipeline.constants import CONTROLLER_HEART_BEAT_EXPI... | null |
7,891 | import argparse
from collections import defaultdict
import mimetypes
import datetime
import json
import os
import time
import uuid
import gradio as gr
import requests
from typing import Union
from PIL import Image
import cv2
import re
from pipeline.serve.conversation import (
default_conversation,
conv_template... | null |
7,892 | import argparse
from collections import defaultdict
import datetime
import json
import os
import time
import uuid
import gradio as gr
import requests
import re
from pipeline.serve.conversation import (
default_conversation,
conv_templates,
SeparatorStyle,
)
from pipeline.constants import LOGDIR
from pipelin... | null |
7,893 | import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `unwrap_model` function. Write a Python function `def unwrap_model(model)` to solve the following problem:
Unwrap a model from a DataParallel or DistributedDataParallel wrapper.
Here is the function:
def unwrap_model... | Unwrap a model from a DataParallel or DistributedDataParallel wrapper. |
7,894 | from pipeline.benchmarks.public_datasets_suite.eval_model import BaseEvalModel
import io
import torch
from typing import List
from transformers import IdeficsForVisionText2Text, AutoProcessor
from PIL import Image
from pipeline.train.train_utils import find_and_remove_tokens, get_image_attention_mask
from pipeline.benc... | null |
7,895 | from pipeline.benchmarks.public_datasets_suite.eval_model import BaseEvalModel
import io
import torch
from typing import List
from transformers import IdeficsForVisionText2Text, AutoProcessor
from PIL import Image
from pipeline.train.train_utils import find_and_remove_tokens, get_image_attention_mask
from pipeline.benc... | null |
7,896 | from typing import List
from PIL import Image
import torch
import transformers
from pipeline.benchmarks.public_datasets_suite.eval_model import BaseEvalModel
from contextlib import suppress
from pipeline.benchmarks.public_datasets_suite.models.utils import unwrap_model
from otter_ai import OtterForConditionalGeneration... | null |
7,897 | from typing import List
from PIL import Image
import torch
import transformers
from pipeline.benchmarks.public_datasets_suite.eval_model import BaseEvalModel
from contextlib import suppress
from pipeline.benchmarks.public_datasets_suite.models.utils import unwrap_model
from otter_ai import OtterForConditionalGeneration... | null |
7,898 | import argparse
import importlib
import json
import os
import random
import uuid
from collections import defaultdict
from einops import repeat
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
from .coco_metric import compute_cider, postprocess_captioning_generation
from .eval_datasets import (
... | null |
7,899 | import argparse
import importlib
import json
import os
import random
import uuid
from collections import defaultdict
from einops import repeat
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
from .coco_metric import compute_cider, postprocess_captioning_generation
from .eval_datasets import (
... | Evaluate a model on COCO dataset. Args: args (argparse.Namespace): arguments eval_model (BaseEvalModel): model to evaluate seed (int, optional): seed for random number generator. Defaults to 42. max_generation_length (int, optional): maximum length of the generated caption. Defaults to 20. num_beams (int, optional): nu... |
7,900 | import argparse
import importlib
import json
import os
import random
import uuid
from collections import defaultdict
from einops import repeat
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
from .coco_metric import compute_cider, postprocess_captioning_generation
from .eval_datasets import (
... | Evaluate a model on VQA datasets. Currently supports VQA v2.0, OK-VQA, VizWiz and TextVQA. Args: args (argparse.Namespace): arguments eval_model (BaseEvalModel): model to evaluate seed (int, optional): random seed. Defaults to 42. max_generation_length (int, optional): max generation length. Defaults to 5. num_beams (i... |
7,901 | import argparse
import importlib
import json
import os
import random
import uuid
from collections import defaultdict
from einops import repeat
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
from .coco_metric import compute_cider, postprocess_captioning_generation
from .eval_datasets import (
... | Evaluate a model on classification dataset. Args: eval_model (BaseEvalModel): model to evaluate imagenet_root (str): path to imagenet root for the specified split. seed (int, optional): random seed. Defaults to 42. num_shots (int, optional): number of shots to use. Defaults to 8. dataset_name (str, optional): dataset n... |
7,902 | import base64
import io
from PIL import Image
import json
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
import os
import numpy as np
from datasets import load_dataset
from typing import Union
from .base_eval_dataset import BaseEvalDataset
from tqdm import tqdm
import dateti... | null |
7,903 | import base64
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from datasets import load_dataset
from .base_eval_dataset import BaseEvalDataset
import json
from io import BytesIO
import pytz
import datetime
import openai
import time
import re
import io
from Levenshtein import distance
demo_prom... | null |
7,904 | import base64
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from datasets import load_dataset
from .base_eval_dataset import BaseEvalDataset
import json
from io import BytesIO
import pytz
import datetime
import openai
import time
import re
import io
from Levenshtein import distance
import ti... | null |
7,905 | import base64
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from datasets import load_dataset
from .base_eval_dataset import BaseEvalDataset
import json
from io import BytesIO
import pytz
import datetime
import openai
import time
import re
import io
from Levenshtein import distance
import ti... | Normalize the extracted answer to match the answer type |
7,906 | import base64
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from datasets import load_dataset
from .base_eval_dataset import BaseEvalDataset
import json
from io import BytesIO
import pytz
import datetime
import openai
import time
import re
import io
from Levenshtein import distance
import ti... | null |
7,907 | import base64
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from datasets import load_dataset
from .base_eval_dataset import BaseEvalDataset
import json
from io import BytesIO
import pytz
import datetime
import openai
import time
import re
import io
from Levenshtein import distance
import ti... | Check if the prediction is equal to the answer, even if they are of different types |
7,908 | from typing import List
from transformers import AutoTokenizer, FuyuImageProcessor
from transformers import FuyuForCausalLM
from src.otter_ai.models.fuyu.processing_fuyu import FuyuProcessor
from PIL import Image
from .base_model import BaseModel
import torch
import numpy as np
import warnings
import io
import base64
i... | null |
7,909 | import io
import torch
from typing import List
from transformers import IdeficsForVisionText2Text, AutoProcessor
from PIL import Image
from .base_model import BaseModel
from pipeline.train.train_utils import find_and_remove_tokens, get_image_attention_mask
import base64
import numpy as np
def get_single_formatted_promp... | null |
7,910 | from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
from PIL import Image
from .base_model import BaseModel
import torch
import numpy as np
import warnings
import io
import base64
def get_pil_image(raw_image_data) -> Image.Image:
if isinstance(raw_image_data, Image.Image):
... | null |
7,911 | import requests
import base64
from .base_model import BaseModel
from PIL import Image
import io
import time
def get_pil_image(raw_image_data) -> Image.Image:
if isinstance(raw_image_data, Image.Image):
return raw_image_data
elif isinstance(raw_image_data, dict) and "bytes" in raw_image_data:
r... | null |
7,912 | from transformers import FuyuForCausalLM, AutoTokenizer, FuyuImageProcessor, FuyuProcessor
from PIL import Image
from .base_model import BaseModel
import torch
import numpy as np
import warnings
import io
import base64
import math
def get_pil_image(raw_image_data) -> Image.Image:
if isinstance(raw_image_data, Imag... | null |
7,913 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from otter_ai import OtterForConditionalGeneration
from .base_model import BaseModel
def get_pil_image(raw_image_data) -> Image.Image:
if isinstance(raw_image... | null |
7,914 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from otter_ai import OtterForConditionalGeneration
from .base_model import BaseModel
def get_formatted_prompt(prompt: str) -> str:
return f"<image>User: {prom... | null |
7,915 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from otter_ai import OtterForConditionalGeneration
from .base_model import BaseModel
def get_formatted_forward_prompt(question: str, answer: str) -> str:
retu... | null |
7,916 | import sys
import argparse
import os
import yaml
import contextlib
from .models.base_model import load_model
from .datasets.base_eval_dataset import load_dataset
def get_info(info):
if "name" not in info:
raise ValueError("Model name is not specified.")
name = info["name"]
# info.pop("name")
ret... | null |
7,917 | import sys
import argparse
import os
import yaml
import contextlib
from .models.base_model import load_model
from .datasets.base_eval_dataset import load_dataset
def get_info(info):
if "name" not in info:
raise ValueError("Model name is not specified.")
name = info["name"]
# info.pop("name")
ret... | null |
7,918 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator, load_checkpoint_and_dispatch
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_sched... | null |
7,919 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator, load_checkpoint_and_dispatch
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_sched... | null |
7,920 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator, load_checkpoint_and_dispatch
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_sched... | null |
7,921 | import os
import torch
def is_global_master(args):
return args.rank == 0
def is_local_master(args):
return args.local_rank == 0
def is_master(args, local=False):
return is_local_master(args) if local else is_global_master(args) | null |
7,922 | import os
import torch
def is_using_distributed():
if "WORLD_SIZE" in os.environ:
return int(os.environ["WORLD_SIZE"]) > 1
if "SLURM_NTASKS" in os.environ:
return int(os.environ["SLURM_NTASKS"]) > 1
return False
def world_info_from_env():
local_rank = 0
for v in (
"LOCAL_RANK... | null |
7,923 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_linea... | null |
7,924 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_linea... | null |
7,925 | import argparse
import glob
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn
from accelerate import Accelerator
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_linea... | null |
7,926 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def truncate_text(path, keep_start=10, keep_end=10, truncate_to="..."):
if len(path) <= (keep_start + kee... | null |
7,927 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
rand... | null |
7,928 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def get_cast_dtype(precision: str):
cast_dtype = None
if precision == "bf16":
cast_dtype = to... | null |
7,929 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def get_autocast(precision):
if precision == "amp":
return torch.cuda.amp.autocast
elif preci... | null |
7,930 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def get_checkpoint_deepspeed_zero3(args, model):
state_dict = {}
for name, p in model.named_paramete... | null |
7,931 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def verify_yaml(args):
if args.rank != 0:
return
# Run pytest with the necessary arguments.
... | null |
7,932 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def get_grouped_params(model, wd):
params_with_wd, params_without_wd = [], []
def apply_decay(x):
... | null |
7,933 | import os
import random
import subprocess
import sys
from contextlib import suppress
import numpy as np
import torch
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
def get_checkpoint(model):
state_dict = model.state_dict()
for name, p in model.named_parameters():
... | Save final weights of the model. |
7,934 | import argparse
import gc
import glob
import os
import sys
import time
from itertools import cycle
import deepspeed
import numpy as np
import torch
import torch.nn
import torch.nn.functional as F
from accelerate import Accelerator
from tqdm import tqdm
from transformers import (
CLIPImageProcessor,
get_constant... | null |
7,935 | import argparse
import os
from pipeline.train.distributed import world_info_from_env
def parse_tuple(string):
try:
x, y = map(int, string.split(","))
return (x, y)
except:
raise argparse.ArgumentTypeError("Invalid tuple format. Expected 'x,y'")
def world_info_from_env():
local_rank ... | Parse the command line arguments and perform the initial setup. :return: Parsed arguments |
7,936 | import argparse
import datetime
import json
import sys
import requests
import yaml
from .demo_models import TestIdefics, TestOtter, TestOtterHD
from .demo_utils import get_image, print_colored
import pytz
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default... | null |
7,937 | import argparse
import datetime
import json
import sys
import requests
import yaml
from .demo_models import TestIdefics, TestOtter, TestOtterHD
from .demo_utils import get_image, print_colored
import pytz
utc_plus_8_time = utc_now.astimezone(utc_plus_8)
def print_colored(text, color_code):
end_code = "\033[0m" # ... | null |
7,938 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor
from tqdm import tqdm
import sys
from otter_ai import OtterForConditionalGeneration
requests.packages.... | null |
7,939 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor
from tqdm import tqdm
import sys
from otter_ai import OtterForConditionalGeneration
def get_formatted_... | null |
7,940 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor
from tqdm import tqdm
import sys
from otter_ai import OtterForConditionalGeneration
requests.packages.... | null |
7,941 | import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor
from tqdm import tqdm
import sys
from otter_ai import OtterForConditionalGeneration
def get_formatted_... | null |
7,942 | import mimetypes
import os
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
import sys
from otter_ai import OtterForConditionalGeneration
requests.packages.urllib3.disable_warnings()
def get_content_type(file_path):
content_type, _ = mimetypes.guess_type(fil... | null |
7,943 | import mimetypes
import os
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
import sys
from otter_ai import OtterForConditionalGeneration
def get_formatted_prompt(prompt: str) -> str:
return f"<image>User: {prompt} GPT:<answer>"
def get_response(input_data,... | null |
7,944 | import sys
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer, FuyuImageProcessor, CLIPImageProcessor, IdeficsForVisionText2Text, FuyuImageProcessor
from transformers import FuyuForCausalLM
from src.otter_ai.models.fuyu.processing_fuyu import FuyuProcessor
from otte... | null |
7,945 | import argparse
import json
import os
import uuid
import webdataset as wds
from typing import List
import logging
import gc
import os.path as op
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
def generate_lineidx(filein: str, idxout: str) -> None:
idxout_tmp = idxout + ".tmp"
with open... | null |
7,946 | import argparse
import json
import os
import uuid
import webdataset as wds
from typing import List
import logging
import gc
import os.path as op
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
def read_to_character(fp, c):
result = []
while True:
s = fp.read(32)
assert s... | null |
7,947 | import argparse
import json
import os
import uuid
import webdataset as wds
from typing import List
import logging
import gc
import os.path as op
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
class TSVFile(object):
def __init__(
self,
tsv_root: str,
tsv_... | null |
7,948 | import pandas as pd
import os
import time
import json
from tqdm import tqdm
import argparse
import orjson
import dask.dataframe as dd
from concurrent.futures import ThreadPoolExecutor, as_completed
def process_images(base64_str, resize_res=-1):
import base64
from PIL import Image
from io import BytesIO
... | null |
7,949 | import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
def apply_delta(base_model_path, target_model_path, delta_path):
print("Loading base model")
base = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=... | null |
7,950 | import base64
import json
import datasketch
import datasketches
def active(func):
def wrapper(self, *args, **kwargs):
assert self.active, "Sketchpad is not active, cannot add a row"
return func(self, *args, **kwargs)
return wrapper | null |
7,951 | import ast
import base64
import importlib
import inspect
import json
import logging
import os
import uuid
import datasketches
import numpy as np
import pandas as pd
import requests
from IPython.display import HTML, display
import lambdaprompt
import sketch
def retrieve_name(var):
callers_local_vars = inspect.curren... | null |
7,952 | import ast
import base64
import importlib
import inspect
import json
import logging
import os
import uuid
import datasketches
import numpy as np
import pandas as pd
import requests
from IPython.display import HTML, display
import lambdaprompt
import sketch
def get_description_from_parts(
column_names, data_types, e... | null |
7,953 | import ast
import base64
import importlib
import inspect
import json
import logging
import os
import uuid
import datasketches
import numpy as np
import pandas as pd
import requests
from IPython.display import HTML, display
import lambdaprompt
import sketch
def get_description_from_parts(
column_names, data_types, e... | null |
7,954 | import ast
import base64
import importlib
import inspect
import json
import logging
import os
import uuid
import datasketches
import numpy as np
import pandas as pd
import requests
from IPython.display import HTML, display
import lambdaprompt
import sketch
def get_import_modules_from_codestring(code):
"""
Given... | null |
7,955 | import hashlib
import json
import os
from typing import Dict
def get_id_for_object(obj):
serialized = json.dumps(obj, sort_keys=True)
return hashlib.sha256(serialized.encode("utf-8")).hexdigest() | null |
7,956 | import datasketches
import numpy as np
def strings_from_sketchpad_sketches(sketchpad):
# FI and VO are the two
output = ""
ds = sketchpad.get_sketchdata_by_name("DS_FI")
# consider showing the counts of frequent items?? Might be useful information.
output += " ".join(
[
x[0]
... | null |
7,957 | import datasketches
import numpy as np
def unary_metrics(sketchpad):
# get metrics for a single sketchpad
# return a vector of metrics
metrics = {}
metrics["rows"] = sketchpad.get_sketchdata_by_name("Rows")
metrics["count"] = sketchpad.get_sketchdata_by_name("Count")
ds = sketchpad.get_sketch... | null |
7,958 | import datasketches
import numpy as np
def max_delta(x1, y1, x2, y2):
f1 = np.interp(np.concatenate([x1, x2]), x2, y2)
f2 = np.interp(np.concatenate([x1, x2]), x1, y1)
return np.max(np.abs(f1 - f2))
def get_CDF(s, N=100):
yvals = [x / N for x in range(N + 1)]
xvals = s.get_quantiles(yvals)
retur... | null |
7,959 | import datasketches
import numpy as np
def binary_metrics(sketchpad1, sketchpad2):
metrics = {}
ds1 = sketchpad1.get_sketchdata_by_name("DS_THETA")
ds2 = sketchpad2.get_sketchdata_by_name("DS_THETA")
lower, estimate, upper = datasketches.theta_jaccard_similarity.jaccard(ds1, ds2)
metrics["theta_j... | null |
7,960 | import os
import argparse
import gradio as gr
from main import load_models, cache_path
from PIL import Image
from os import path
canvas_size = 512
def process_image(p, im, steps, cfg, image_strength, seed):
if not im:
return Image.new("RGB", (canvas_size, canvas_size))
... | null |
7,961 | import os
import argparse
import gradio as gr
from main import load_models, cache_path
from PIL import Image
from os import path
def load_models(model_id="Lykon/dreamshaper-7"):
from diffusers import AutoPipelineForImage2Image, LCMScheduler
from diffusers.utils import load_image
if not is_mac:
tor... | null |
7,962 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
def build_prompt(history):
prompt = "欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
for query, response in history:
prompt += f"\n\n用户：{query}"
prompt += f"\n\nChatGLM-6B：{response}"... | null |
7,963 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
stop_stream = False
def signal_handler(signal, frame):
global stop_stream
stop_stream = True | null |
7,964 | from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
async def create_it... | null |
7,965 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
# transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到... | null |
7,966 | import logging
import os
import sys
import json
import numpy as np
from datasets import load_dataset
import jieba
from rouge_chinese import Rouge
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
import torch
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTok... | null |
7,967 | import os, sys
import gradio as gr
import mdtex2html
import torch
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainingArguments,
set_seed,
)
from arguments import ModelArguments, Data... | null |
7,968 | import os, sys
import gradio as gr
import mdtex2html
import torch
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainingArguments,
set_seed,
)
from arguments import ModelArguments, Data... | null |
7,969 | import os, sys
import gradio as gr
import mdtex2html
import torch
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainingArguments,
set_seed,
)
from arguments import ModelArguments, Data... | null |
7,970 | import os, sys
import gradio as gr
import mdtex2html
import torch
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainingArguments,
set_seed,
)
from arguments import ModelArguments, Data... | null |
7,971 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is ... | null |
7,972 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
def parse_text(text):
"""... | null |
7,973 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
def parse_text(text):
"""... | null |
7,974 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
gr.Chatbot.postprocess = postprocess
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">VisualGLM</h1>""")
image_path = gr.Image(type="filepath", label="Image Prompt", value=None)
chatbot = gr.Chatbot()
with gr.... | null |
7,975 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
def reset_state():
return None, [], [] | null |
7,976 | from transformers import AutoModel, AutoTokenizer
import streamlit as st
from streamlit_chat import message
st.set_page_config(
page_title="ChatGLM-6b 演示",
page_icon=":robot:"
)
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_... | null |
7,977 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
MAX_BOXES = MAX_TURNS * 2
with gr.Blocks() as demo:... | null |
7,978 | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
def build_prompt(history, prefix):
prompt = prefix
for query, response in history:
prompt += f"\n\n用户：{query}"
prompt += f"\n\nChatGLM-6B：{response}"
return prompt | null |
7,979 | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
stop_stream = False
def signal_handler(signal, frame):
global stop_stream
stop_stream = True | null |
7,981 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
def parse_text(text):
"""copy... | null |
7,982 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
gr.Chatbot.postprocess = postprocess
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">ChatGLM</h1>""")
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
... | null |
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