id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
33,737 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def multiclass_accuracy(y_true, y_pred):
correct = 0
total = len(y_true)
for label, pred in zip(y_true, y_pred):
correct += label == pred
return correct/total | null |
33,738 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def accuracy_cm(cm):
return np.trace(cm)/np.sum(cm) | null |
33,739 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def balanced_accuracy_cm(cm):
correctly_classified = np.diagonal(cm)
rows_sum = np.sum(cm, axis=1)
indices = np.nonzero(rows_sum)[0]
if rows_sum.shape[0] != indices.shape[0]:
warnings.warn("y_... | null |
33,740 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def precision(y_true, y_pred):
"""
Fraction of True Positive Elements divided by total number of positive predicted units
How I view it: Assuming we say someone has cancer: how often are we correct?
It... | null |
33,741 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def confusion_matrix(y_true, y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
assert y_true.shape == y_pred.shape
unique_classes = np.unique(np.concatenate([y_true, y_pred], axis=0)).shape[... | null |
33,742 | import numpy as np
from scipy.integrate import simpson
import matplotlib.pyplot as plt
import warnings
def precision(y_true, y_pred):
"""
Fraction of True Positive Elements divided by total number of positive predicted units
How I view it: Assuming we say someone has cancer: how often are we correct?
It... | null |
33,743 | import pandas as pd
import numpy as np
import torch
def get_predictions(loader, model, device):
model.eval()
saved_preds = []
true_labels = []
with torch.no_grad():
for x,y in loader:
x = x.to(device)
y = y.to(device)
scores = model(x)
saved_pred... | null |
33,744 | import pandas as pd
import numpy as np
import torch
def get_submission(model, loader, test_ids, device):
all_preds = []
model.eval()
with torch.no_grad():
for x,y in loader:
print(x.shape)
x = x.to(device)
score = model(x)
prediction = score.float()
... | null |
33,745 | import pandas as pd
import torch
from torch.utils.data import TensorDataset
from torch.utils.data.dataset import random_split
from math import ceil
def get_data():
train_data = pd.read_csv("new_shiny_train.csv")
y = train_data["target"]
X = train_data.drop(["ID_code", "target"], axis=1)
X_tensor = torc... | null |
33,746 | import torch
from torch import nn, optim
import os
import config
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn.metrics import cohen_kappa_score
from efficientnet_pytorch import EfficientNet
from dataset import DRDataset
from torchvision.utils import save_image
from utils import (
load_c... | null |
33,747 | import torch
from tqdm import tqdm
import numpy as np
from torch import nn
from torch import optim
from torch.utils.data import DataLoader, Dataset
from utils import save_checkpoint, load_checkpoint, check_accuracy
from sklearn.metrics import cohen_kappa_score
import config
import os
import pandas as pd
def make_predi... | null |
33,748 | import torch
import pandas as pd
import numpy as np
import config
from tqdm import tqdm
import warnings
import torch.nn.functional as F
def make_prediction(model, loader, output_csv="submission.csv"):
preds = []
filenames = []
model.eval()
for x, y, files in tqdm(loader):
x = x.to(config.DEVIC... | null |
33,749 | import torch
import pandas as pd
import numpy as np
import config
from tqdm import tqdm
import warnings
import torch.nn.functional as F
def check_accuracy(loader, model, device="cuda"):
model.eval()
all_preds, all_labels = [], []
num_correct = 0
num_samples = 0
for x, y, filename in tqdm(loader):
... | null |
33,750 | import torch
import pandas as pd
import numpy as np
import config
from tqdm import tqdm
import warnings
import torch.nn.functional as F
def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
print("=> Saving checkpoint")
torch.save(state, filename) | null |
33,751 | import torch
import pandas as pd
import numpy as np
import config
from tqdm import tqdm
import warnings
import torch.nn.functional as F
def load_checkpoint(checkpoint, model, optimizer, lr):
print("=> Loading checkpoint")
model.load_state_dict(checkpoint["state_dict"])
#optimizer.load_state_dict(checkpoint... | null |
33,752 | import torch
import pandas as pd
import numpy as np
import config
from tqdm import tqdm
import warnings
import torch.nn.functional as F
def get_csv_for_blend(loader, model, output_csv_file):
warnings.warn("Important to have shuffle=False (and to ensure batch size is even size) when running get_csv_for_blend also s... | null |
33,753 | import os
import numpy as np
from PIL import Image
import warnings
from multiprocessing import Pool
from tqdm import tqdm
import cv2
def save_single(args):
img_file, input_path_folder, output_path_folder, output_size = args
image_original = Image.open(os.path.join(input_path_folder, img_file))
image = trim(... | Uses multiprocessing to make it fast |
33,754 | import torch
from dataset import FacialKeypointDataset
from torch import nn, optim
import os
import config
from torch.utils.data import DataLoader
from tqdm import tqdm
from efficientnet_pytorch import EfficientNet
from utils import (
load_checkpoint,
save_checkpoint,
get_rmse,
get_submission
)
def tra... | null |
33,755 | import torch
import numpy as np
import config
import pandas as pd
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `get_submission` function. Write a Python function `def get_submission(loader, dataset, model_15, model_4)` to solve the following problem:
This can be ... | This can be done a lot faster.. but it didn't take too much time to do it in this inefficient way |
33,756 | import torch
import numpy as np
import config
import pandas as pd
from tqdm import tqdm
def get_rmse(loader, model, loss_fn, device):
model.eval()
num_examples = 0
losses = []
for batch_idx, (data, targets) in enumerate(loader):
data = data.to(device=device)
targets = targets.to(device=... | null |
33,757 | import torch
import numpy as np
import config
import pandas as pd
from tqdm import tqdm
def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
print("=> Saving checkpoint")
torch.save(state, filename) | null |
33,758 | import torch
import numpy as np
import config
import pandas as pd
from tqdm import tqdm
def load_checkpoint(checkpoint, model, optimizer, lr):
print("=> Loading checkpoint")
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
# If we don't do this then it... | null |
33,759 | import numpy as np
import pandas as pd
import os
from PIL import Image
def extract_images_from_csv(csv, column, save_folder, resize=(96, 96)):
if not os.path.exists(save_folder):
os.makedirs(save_folder)
for idx, image in enumerate(csv[column]):
image = np.array(image.split()).astype(np.uint8)... | null |
33,760 | import os
import torch
import torch.nn.functional as F
import numpy as np
import config
from torch import nn, optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from dataset import CatDog
from efficientnet_pytorch import EfficientNet
from utils import check_accuracy, load_checkpoint, save_checkpoint
d... | null |
33,761 | import os
import torch
import torch.nn.functional as F
import numpy as np
import config
from torch import nn, optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from dataset import CatDog
from efficientnet_pytorch import EfficientNet
from utils import check_accuracy, load_checkpoint, save_checkpoint
d... | null |
33,762 | import torch
import os
import pandas as pd
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
from tqdm import tqdm
from dataset import CatDog
from torch.utils.data import DataLoader
from sklearn.metrics import log_loss
The provided code snippet includes necessary... | Check accuracy of model on data from loader |
33,763 | import torch
import os
import pandas as pd
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
from tqdm import tqdm
from dataset import CatDog
from torch.utils.data import DataLoader
from sklearn.metrics import log_loss
def save_checkpoint(state, filename="my_chec... | null |
33,764 | import torch
import os
import pandas as pd
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
from tqdm import tqdm
from dataset import CatDog
from torch.utils.data import DataLoader
from sklearn.metrics import log_loss
def load_checkpoint(checkpoint, model):
... | null |
33,765 | import torch
import os
import pandas as pd
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
from tqdm import tqdm
from dataset import CatDog
from torch.utils.data import DataLoader
from sklearn.metrics import log_loss
def create_submission(model, model_name, fil... | null |
33,766 | import torch
import os
import pandas as pd
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
from tqdm import tqdm
from dataset import CatDog
from torch.utils.data import DataLoader
from sklearn.metrics import log_loss
def blending_ensemble_data():
pred_csvs ... | null |
33,767 | import locale
import os
import re
import shutil
import subprocess
import sys
import sysconfig
from typing import List
from pathlib import Path
from typing import Optional
import pkg_resources
from mikazuki.log import log
python_bin = sys.executable
def run(command,
desc: Optional[str] = None,
errdesc: O... | null |
33,768 | import subprocess
import sys
import os
import threading
import uuid
from enum import Enum
from typing import Dict, List
from subprocess import Popen, PIPE, TimeoutExpired, CalledProcessError, CompletedProcess
import psutil
from mikazuki.log import log
def kill_proc_tree(pid, including_parent=True):
parent = psutil... | null |
33,769 | import cv2
import numpy as np
from PIL import Image
def smart_imread(img, flag=cv2.IMREAD_UNCHANGED):
if img.endswith(".gif"):
img = Image.open(img)
img = img.convert("RGB")
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
else:
img = cv2.imread(img, flag)
return img | null |
33,770 | import cv2
import numpy as np
from PIL import Image
def smart_24bit(img):
if img.dtype is np.dtype(np.uint16):
img = (img / 257).astype(np.uint8)
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif img.shape[2] == 4:
trans_mask = img[:, :, 3] == 0
img[t... | null |
33,771 | import cv2
import numpy as np
from PIL import Image
def make_square(img, target_size):
old_size = img.shape[:2]
desired_size = max(old_size)
desired_size = max(desired_size, target_size)
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta... | null |
33,772 | import cv2
import numpy as np
from PIL import Image
def smart_resize(img, size):
# Assumes the image has already gone through make_square
if img.shape[0] > size:
img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
elif img.shape[0] < size:
img = cv2.resize(img, (size, size), i... | null |
33,773 | import re
import hashlib
from typing import Dict, Callable, NamedTuple
from pathlib import Path
class Info(NamedTuple):
path: Path
output_ext: str
def hash(i: Info, algo='sha1') -> str:
try:
hash = hashlib.new(algo)
except ImportError:
raise ValueError(f"'{algo}' is invalid hash algorit... | null |
33,774 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,775 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,776 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,777 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,778 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,779 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,780 | import asyncio
import json
import os
from datetime import datetime
from pathlib import Path
import toml
from fastapi import APIRouter, BackgroundTasks, Request
from starlette.requests import Request
import mikazuki.process as process
from mikazuki import launch_utils
from mikazuki.app.models import (APIResponse, APIRes... | null |
33,781 | import asyncio
import os
import httpx
import starlette
import websockets
from fastapi import APIRouter, Request, WebSocket
from httpx import ConnectError
from starlette.background import BackgroundTask
from starlette.requests import Request
from starlette.responses import PlainTextResponse, StreamingResponse
from mikaz... | null |
33,782 | import asyncio
import os
import httpx
import starlette
import websockets
from fastapi import APIRouter, Request, WebSocket
from httpx import ConnectError
from starlette.background import BackgroundTask
from starlette.requests import Request
from starlette.responses import PlainTextResponse, StreamingResponse
from mikaz... | null |
33,783 | import asyncio
import mimetypes
import os
import webbrowser
import sys
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from mikazuki.utils.devices import check... | null |
33,784 | import asyncio
import mimetypes
import os
import webbrowser
import sys
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from mikazuki.utils.devices import check... | null |
33,785 | import asyncio
import mimetypes
import os
import webbrowser
import sys
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from mikazuki.utils.devices import check... | null |
33,786 | import sys
def check_torch_gpu():
try:
import torch
print(f'Torch {torch.__version__}')
if torch.cuda.is_available():
if torch.version.cuda:
print(
f'Torch backend: nVidia CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version() if torc... | null |
33,787 | import glob
import os
import re
import shutil
import sys
from mikazuki.log import log
def check_training_params(data):
potential_path = [
"train_data_dir", "reg_data_dir", "output_dir"
]
file_paths = [
"sample_prompts"
]
for p in potential_path:
if p in data and not os.path.... | null |
33,788 | import argparse
import locale
import os
import platform
import subprocess
import sys
import webbrowser
from mikazuki.launch_utils import prepare_environment, base_dir_path
from mikazuki.log import log
def run_tensorboard():
log.info("Starting tensorboard...")
subprocess.Popen([sys.executable, "-m", "tensorboard... | null |
33,789 | from collections import namedtuple
import rrc_evaluation_funcs
import importlib
The provided code snippet includes necessary dependencies for implementing the `default_evaluation_params` function. Write a Python function `def default_evaluation_params()` to solve the following problem:
default_evaluation_params: Defau... | default_evaluation_params: Default parameters to use for the validation and evaluation. |
33,790 | from collections import namedtuple
import rrc_evaluation_funcs
import importlib
The provided code snippet includes necessary dependencies for implementing the `validate_data` function. Write a Python function `def validate_data(gtFilePath, submFilePath, evaluationParams)` to solve the following problem:
Method validat... | Method validate_data: validates that all files in the results folder are correct (have the correct name contents). Validates also that there are no missing files in the folder. If some error detected, the method raises the error |
33,791 | from collections import namedtuple
import rrc_evaluation_funcs
import importlib
def evaluation_imports():
"""
evaluation_imports: Dictionary ( key = module name , value = alias ) with python modules used in the evaluation.
"""
return {
'Polygon':'plg',
'numpy':'np'
... | Method evaluate_method: evaluate method and returns the results Results. Dictionary with the following values: - method (required) Global method metrics. Ex: { 'Precision':0.8,'Recall':0.9 } - samples (optional) Per sample metrics. Ex: {'sample1' : { 'Precision':0.8,'Recall':0.9 } , 'sample2' : { 'Precision':0.8,'Recal... |
33,792 | import json
import sy
import zipfile
import re
import sys
import os
import codecs
import importlib
from StringIO import StringIO
The provided code snippet includes necessary dependencies for implementing the `load_zip_file_keys` function. Write a Python function `def load_zip_file_keys(file,fileNameRegExp='')` to solv... | Returns an array with the entries of the ZIP file that match with the regular expression. The key's are the names or the file or the capturing group definied in the fileNameRegExp |
33,793 | import json
import sysys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
from StringIO import StringIO
def print_help():
sys.stdout.write('Usage: python %s.py -g=<gtFile> -s=<submFile> [-o=<outputFolder> -p=<jsonParams>]' %sys.argv[0])
sys.exit(2)
The provided cod... | This process validates a method, evaluates it and if it succed generates a ZIP file with a JSON entry for each sample. Params: p: Dictionary of parmeters with the GT/submission locations. If None is passed, the parameters send by the system are used. default_evaluation_params_fn: points to a function that returns a dic... |
33,794 | import json
import sysys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
from StringIO import StringIO
The provided code snippet includes necessary dependencies for implementing the `main_validation` function. Write a Python function `def main_validation(default_evaluatio... | This process validates a method Params: default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation validate_data_fn: points to a method that validates the corrct format of the submission |
33,795 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,796 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,797 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,798 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,799 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,800 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,801 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,802 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,803 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,804 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,805 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,806 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,807 | import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential,... | null |
33,808 | import argparse
import dataclasses
import json
import os
from azure.identity import DefaultAzureCredential
from azure.core.credentials import AzureKeyCredential
from azure.keyvault.secrets import SecretClient
from azure.ai.formrecognizer import DocumentAnalysisClient
from data_utils import chunk_directory
def get_docu... | null |
33,809 | import ast
import html
import json
import os
import re
import requests
from openai import AzureOpenAI
import re
import tempfile
import time
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from dataclasses import dataclass
from functools import partial
from typing import Callable, ... | Cleans up the given content using regexes Args: content (str): The content to clean up. Returns: str: The cleaned up content. |
33,810 | import argparse
import json
import os
import time
import uuid
import pinecone
import requests
from data_utils import Document
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from azure.identity import AzureCliCredential
from typing import List
from data_u... | null |
33,811 | import argparse
import json
import os
import uuid
import requests
from data_utils import Document
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from azure.identity import AzureCliCredential
from pymongo.mongo_client import MongoClient
from typing import... | null |
33,812 | import argparse
import json
import os
import uuid
import requests
from data_utils import Document
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from azure.identity import AzureCliCredential
from pymongo.mongo_client import MongoClient
from typing import... | null |
33,813 | import argparse
from azure.identity import AzureDeveloperCliCredential
import urllib3
def update_redirect_uris(credential, app_id, uri):
urllib3.request(
"PATCH",
f"https://graph.microsoft.com/v1.0/applications/{app_id}",
headers={
"Authorization": "Bearer "
+ creden... | null |
33,814 | import argparse
import dataclasses
import time
from tqdm import tqdm
from azure.identity import AzureDeveloperCliCredential
from azure.core.credentials import AzureKeyCredential
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
SearchableField,
Sear... | null |
33,815 | import argparse
import subprocess
from azure.identity import AzureDeveloperCliCredential
import urllib3
def get_auth_headers(credential):
return {
"Authorization": "Bearer "
+ credential.get_token("https://graph.microsoft.com/.default").token
}
def check_for_application(credential, app_id):
... | null |
33,816 | import argparse
import subprocess
from azure.identity import AzureDeveloperCliCredential
import urllib3
def get_auth_headers(credential):
return {
"Authorization": "Bearer "
+ credential.get_token("https://graph.microsoft.com/.default").token
}
def create_application(credential):
resp = url... | null |
33,817 | import argparse
import subprocess
from azure.identity import AzureDeveloperCliCredential
import urllib3
def get_auth_headers(credential):
return {
"Authorization": "Bearer "
+ credential.get_token("https://graph.microsoft.com/.default").token
}
def add_client_secret(credential, app_id):
res... | null |
33,818 | import argparse
import subprocess
from azure.identity import AzureDeveloperCliCredential
import urllib3
def update_azd_env(name, val):
subprocess.run(f"azd env set {name} {val}", shell=True) | null |
33,819 | import argparse
import dataclasses
import json
import os
import subprocess
import requests
import time
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from azure.identity import AzureCliCredential
from azure.search.documents import SearchClient
from tqdm ... | null |
33,820 | import argparse
import dataclasses
import json
import os
import subprocess
import requests
import time
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from azure.identity import AzureCliCredential
from azure.search.documents import SearchClient
from tqdm ... | null |
33,821 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | null |
33,822 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | null |
33,823 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | null |
33,824 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | null |
33,825 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Calculate the estimated resolution for resizing an image while preserving aspect ratio. The function first calculates scaling factors for height and width of the image based on the target height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger scaling factor to estimate the ne... |
33,826 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Convert a base64 image into the image type the extension uses |
33,827 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Fetch ControlNet processing units from a StableDiffusionProcessing. |
33,828 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Fetch a single ControlNet processing unit from ControlNet script arguments. The list must not contain script positional arguments. It must only contain processing units. |
33,829 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Fetch the maximum number of allowed ControlNet models. |
33,830 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Update the arguments of the ControlNet script in `p.script_args` in place, reading from `cn_units`. `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want. Does not update `p.script_args` if any of the folling is true: - ControlNet is not present in `p.scripts` - ... |
33,831 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | @Deprecated(Raises assertion error if script_args passed in is Tuple) Update the arguments of the ControlNet script in `script_args` in place, reading from `cn_units`. `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want. Does not update `script_args` if any of ... |
33,832 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | Fetch the list of available models. Each value is a valid candidate of `ControlNetUnit.model`. Keyword arguments: update -- Whether to refresh the list from disk. (default False) |
33,833 | from dataclasses import dataclass
from enum import Enum
from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
from modules import scripts, processing, shared
from scripts import global_state
from scripts.processor import preprocessor_sliders_config, model_free_preprocessors... | get the detail of all preprocessors including sliders: the slider config in Auto1111 webUI Keyword arguments: alias_names -- Whether to get the module detail with alias names instead of internal keys |
33,834 | import os
import io
import cv2
import base64
import requests
def generate(url: str, payload: dict, file_suffix: str = ""):
response = requests.post(url=url, json=payload).json()
if "images" not in response:
print(response)
else:
for i, base64image in enumerate(response["images"]):
... | null |
33,835 | import os
import io
import cv2
import base64
import requests
def read_image(img_path: str) -> str:
img = cv2.imread(img_path)
_, bytes = cv2.imencode(".png", img)
encoded_image = base64.b64encode(bytes).decode("utf-8")
return encoded_image | null |
33,836 | import os
import io
import cv2
import base64
import requests
def generate(url: str, payload: dict):
response = requests.post(url=url, json=payload).json()
if "images" not in response:
print(response)
else:
for i, base64image in enumerate(response["images"]):
with open(f"{os.path... | null |
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