id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
144,194 | from typing import Any, Dict, List, Optional, Tuple, Union
def safe_get(
container: Union[str, List[Any], Dict[Any, Any], Tuple],
key: Any,
default: Optional[Any] = None,
) -> Any:
if isinstance(container, dict):
return container.get(key, default)
else:
return safe_get_with_brackets(... | null |
144,195 | from abc import ABC, abstractmethod
from pathlib import Path
from typing import List, Optional
class SQLDriver(ABC):
"""Abstract class for SQL drivers.
The expose common functionality for validating SQL queries.
"""
def validate_sql(self, query: str) -> List[str]:
...
def get_schema(self) ->... | null |
144,196 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, Optional, Type, Union, get_args, get_origin
from pydantic import BaseModel, validator
from pydantic.fields import ModelField
from guardrails.datatypes import Boolean as B... | null |
144,197 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, Optional, Type, Union, get_args, get_origin
from pydantic import BaseModel, validator
from pydantic.fields import ModelField
from guardrails.datatypes import Boolean as B... | Convert a Pydantic BaseModel to an OpenAI function. Args: model: The Pydantic BaseModel to convert. Returns: OpenAI function paramters. |
144,198 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, Optional, Type, Union, get_args, get_origin
from pydantic import BaseModel, validator
from pydantic.fields import ModelField
from guardrails.datatypes import Boolean as B... | Create an Object from a Pydantic model. |
144,199 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union, get_args
from pydantic import BaseModel, ConfigDict, field_validator
from pydantic.fields import FieldInfo
from guardrails.datatypes... | null |
144,200 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union, get_args
from pydantic import BaseModel, ConfigDict, field_validator
from pydantic.fields import FieldInfo
from guardrails.datatypes... | Convert a Pydantic BaseModel to an OpenAI function. Args: model: The Pydantic BaseModel to convert. Returns: OpenAI function paramters. |
144,201 | import typing
import warnings
from copy import deepcopy
from datetime import date, time
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union, get_args
from pydantic import BaseModel, ConfigDict, field_validator
from pydantic.fields import FieldInfo
from guardrails.datatypes... | Create an Object from a Pydantic model. |
144,202 | import sys
from functools import wraps
from operator import attrgetter
from typing import Any, List, Optional, Union
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import StatusCode, Tracer
from guardrails.stores.context import get_tracer as get_context_tracer
from ... | null |
144,203 | import sys
from functools import wraps
from operator import attrgetter
from typing import Any, List, Optional, Union
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import StatusCode, Tracer
from guardrails.stores.context import get_tracer as get_context_tracer
from ... | null |
144,204 | import sys
from functools import wraps
from operator import attrgetter
from typing import Any, List, Optional, Union
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import StatusCode, Tracer
from guardrails.stores.context import get_tracer as get_context_tracer
from ... | null |
144,205 | import sys
from functools import wraps
from operator import attrgetter
from typing import Any, List, Optional, Union
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import StatusCode, Tracer
from guardrails.stores.context import get_tracer as get_context_tracer
from ... | This is the standard otel tracer set to talk to a grpc open telemetry collector running on port 4317. |
144,206 | import sys
from functools import wraps
from operator import attrgetter
from typing import Any, List, Optional, Union
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import StatusCode, Tracer
from guardrails.stores.context import get_tracer as get_context_tracer
from ... | This tracer will emit spans directly to an otlp endpoint, configured by the following environment variables: OTEL_EXPORTER_OTLP_PROTOCOL OTEL_EXPORTER_OTLP_TRACES_ENDPOINT OTEL_EXPORTER_OTLP_HEADERS We recommend using Grafana to collect your metrics. A full example of how to do that is in our (docs)[https://docs.guards... |
144,207 | import os
import random
from typing import List
from lxml.etree import Element as E
from rich.pretty import pretty_repr
from guardrails import datatypes as dt
from guardrails.classes.history.call import Call
from guardrails.utils.reask_utils import gather_reasks
class Call(ArbitraryModel):
iterations: Stack[Iterat... | Generate artifacts for testing. Artifacts include: rail_spec, compiled_prompt, llm_output, validated_response. The artifacts are saved by on_fail_type. Check out tests/integration_tests/test_assets/entity_extraction/ for examples. This function is only intended to be used to create artifacts for integration tests once ... |
144,208 | import os
import random
from typing import List
from lxml.etree import Element as E
from rich.pretty import pretty_repr
from guardrails import datatypes as dt
from guardrails.classes.history.call import Call
from guardrails.utils.reask_utils import gather_reasks
The provided code snippet includes necessary dependencie... | Generate random schemas that represent a valid schema. Args: n: The number of schemas to generate. depth: The depth of nesting |
144,209 | from typing import Any, Dict, List, Optional, Tuple, Type, Union
from guardrails.utils.safe_get import safe_get
from guardrails.validator_base import Validator
def safe_get(
container: Union[str, List[Any], Dict[Any, Any], Tuple],
key: Any,
default: Optional[Any] = None,
) -> Any:
if isinstance(contain... | null |
144,210 | from typing import Union
The provided code snippet includes necessary dependencies for implementing the `cast_xml_to_string` function. Write a Python function `def cast_xml_to_string(xml_value: Union[memoryview, bytes, bytearray, str]) -> str` to solve the following problem:
Cast XML value to a string. Args: xml_value... | Cast XML value to a string. Args: xml_value (Union[memoryview, bytes, bytearray, str]): The XML value to cast. Returns: str: The XML value as a string. |
144,211 | from typing import Dict, List
import tiktoken
The provided code snippet includes necessary dependencies for implementing the `num_tokens_from_string` function. Write a Python function `def num_tokens_from_string(text: str, model_name: str) -> int` to solve the following problem:
Returns the number of tokens in a text ... | Returns the number of tokens in a text string. Supported for OpenAI models only. This is a helper function that is required when OpenAI's `stream` parameter is set to `True`, because OpenAI does not return the number of tokens in that case. Requires the `tiktoken` package to be installed. Args: text (str): The text str... |
144,212 | from typing import Dict, List
import tiktoken
The provided code snippet includes necessary dependencies for implementing the `num_tokens_from_messages` function. Write a Python function `def num_tokens_from_messages( messages: List[Dict[str, str]], model: str = "gpt-3.5-turbo-0613" ) -> int` to solve the following... | Return the number of tokens used by a list of messages. |
144,213 | from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
import openai.error
from tenacity import retry, retry_if_exception_type, wait_exponential_jitter
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import (
BaseAsyncOpenAIClient,
BaseSyncO... | null |
144,214 | from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
import openai.error
from tenacity import retry, retry_if_exception_type, wait_exponential_jitter
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import (
BaseAsyncOpenAIClient,
BaseSyncO... | null |
144,215 | from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
import openai.error
from tenacity import retry, retry_if_exception_type, wait_exponential_jitter
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import (
BaseAsyncOpenAIClient,
BaseSyncO... | null |
144,216 | from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
import openai.error
from tenacity import retry, retry_if_exception_type, wait_exponential_jitter
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import (
BaseAsyncOpenAIClient,
BaseSyncO... | null |
144,217 | import os
from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import BaseOpenAIClient
from guardrails.utils.openai_utils.streaming_utils import (
num_tokens_from_messages,
num_tokens_from_st... | null |
144,218 | import os
from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import BaseOpenAIClient
from guardrails.utils.openai_utils.streaming_utils import (
num_tokens_from_messages,
num_tokens_from_st... | null |
144,219 | import os
from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import BaseOpenAIClient
from guardrails.utils.openai_utils.streaming_utils import (
num_tokens_from_messages,
num_tokens_from_st... | null |
144,220 | import os
from typing import Any, AsyncIterable, Dict, Iterable, List, cast
import openai
from guardrails.utils.llm_response import LLMResponse
from guardrails.utils.openai_utils.base import BaseOpenAIClient
from guardrails.utils.openai_utils.streaming_utils import (
num_tokens_from_messages,
num_tokens_from_st... | null |
144,221 | from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List, Optional
from guardrails.utils.pydantic_utils import ArbitraryModel
from guardrails.utils.reask_utils import FieldReAsk, ReAsk, prune_obj_for_reasking
from guardrails.validator_base import ValidationResult
def update_response_by... | Merge the reask output into the original output. Args: prev_logs: validation output object from the previous iteration. current_logs: validation output object from the current iteration. Returns: The merged output. |
144,222 | import argparse, os
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
from ... | null |
144,223 | import argparse, os
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
from ... | null |
144,224 | import argparse, os
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
from ... | null |
144,225 | import argparse, os
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
from pytorch_lightning import seed_everything
from torch import autocast
fr... | null |
144,226 | import argparse, os
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
from pytorch_lightning import seed_everything
from torch import autocast
fr... | null |
144,227 | import argparse, os
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
from pytorch_lightning import seed_everything
from torch import autocast
fr... | null |
144,228 | import sys
import torch
import numpy as np
import streamlit as st
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.util import instantiate_f... | null |
144,229 | import sys
import torch
import numpy as np
import streamlit as st
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.models.diffusion.ddim imp... | null |
144,230 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,231 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,232 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,233 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,234 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,235 | import importlib
import streamlit as st
import torch
import cv2
import numpy as np
import PIL
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
import io, os
from torch import autocast
from einops import rearrange, repeat
from torchvision.utils import make_grid
from pytorch_lightning import ... | null |
144,236 | import sys
import cv2
import torch
import numpy as np
import streamlit as st
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat
from streamlit_drawable_canvas import st_canvas
from imwatermark import WatermarkEncoder
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ins... | null |
144,237 | import sys
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.util import instantiate_from... | null |
144,238 | import sys
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.util import instantiate_from... | null |
144,239 | import sys
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.models.diffusion.ddim import... | null |
144,240 | import sys
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.models.diffusion.ddim import... | null |
144,241 | import sys
import cv2
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat
from imwatermark import WatermarkEncoder
from pathlib import Path
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
torch... | null |
144,242 | import sys
import cv2
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat
from imwatermark import WatermarkEncoder
from pathlib import Path
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
def i... | null |
144,243 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .nn import timestep_embedding
The provided code snippet includes necessary dependencies for implementing the `convert_module_to_f16` function. Write a Python function `def convert_module_to_f16(param)` to solve the following prob... | Convert primitive modules to float16. |
144,244 | import enum
import math
import numpy as np
import torch as th
def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
"""
This is the deprecated API for creating beta schedules.
See get_named_beta_schedule() for the new library of schedules.
"""
if beta_schedule == "q... | Get a pre-defined beta schedule for the given name. The beta schedule library consists of beta schedules which remain similar in the limit of num_diffusion_timesteps. Beta schedules may be added, but should not be removed or changed once they are committed to maintain backwards compatibility. |
144,245 | import enum
import math
import numpy as np
import torch as th
The provided code snippet includes necessary dependencies for implementing the `_extract_into_tensor` function. Write a Python function `def _extract_into_tensor(arr, timesteps, broadcast_shape)` to solve the following problem:
Extract values from a 1-D num... | Extract values from a 1-D numpy array for a batch of indices. :param arr: the 1-D numpy array. :param timesteps: a tensor of indices into the array to extract. :param broadcast_shape: a larger shape of K dimensions with the batch dimension equal to the length of timesteps. :return: a tensor of shape [batch_size, 1, ...... |
144,246 | import torch as th
from .gaussian_diffusion import GaussianDiffusion
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use ... | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100... |
144,247 | from abc import abstractmethod
import torch as th
class UniformSampler(ScheduleSampler):
def __init__(self, diffusion):
super(UniformSampler, self).__init__()
self.diffusion = diffusion
self.register_buffer(
"_weights", th.ones([diffusion.num_timesteps]), persistent=False
... | Create a ScheduleSampler from a library of pre-defined samplers. :param name: the name of the sampler. :param diffusion: the diffusion object to sample for. |
144,248 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `conv_nd` function. Write a Python function `def conv_nd(dims, *args, **kwargs)` to solve the following problem:
Create a 1D, 2D, or 3D convolution module.... | Create a 1D, 2D, or 3D convolution module. |
144,249 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `linear` function. Write a Python function `def linear(*args, **kwargs)` to solve the following problem:
Create a linear module.
Here is the function:
de... | Create a linear module. |
144,250 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `avg_pool_nd` function. Write a Python function `def avg_pool_nd(dims, *args, **kwargs)` to solve the following problem:
Create a 1D, 2D, or 3D average poo... | Create a 1D, 2D, or 3D average pooling module. |
144,251 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `zero_module` function. Write a Python function `def zero_module(module)` to solve the following problem:
Zero out the parameters of a module and return it... | Zero out the parameters of a module and return it. |
144,252 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `scale_module` function. Write a Python function `def scale_module(module, scale)` to solve the following problem:
Scale the parameters of a module and ret... | Scale the parameters of a module and return it. |
144,253 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-5):
super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
self.swish = swish
def forward(self, x)... | Make a standard normalization layer, with an optional swish activation. :param channels: number of input channels. :return: an nn.Module for normalization. |
144,254 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `timestep_embedding` function. Write a Python function `def timestep_embedding(timesteps, dim, max_period=10000)` to solve the following problem:
Create si... | Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. |
144,255 | import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `mean_flat` function. Write a Python function `def mean_flat(tensor)` to solve the following problem:
Take the mean over all non-batch dimensions.
Here is... | Take the mean over all non-batch dimensions. |
144,256 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which de... | null |
144,352 | import torch
import torch.nn as nn
import kornia
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
import open_clip
from ldm.util import default, count_params, autocast
from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseA... | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
144,358 | import torch
import torch.nn.functional as F
import math
from tqdm import tqdm
def expand_dims(v, dims):
"""
Expand the tensor `v` to the dim `dims`.
Args:
`v`: a PyTorch tensor with shape [N].
`dim`: a `int`.
Returns:
a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total d... | Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. We support four types of the diffusion m... |
144,364 | from os import path
from crosslinked.logger import Log
def delimiter2list(value, delim=","):
return value.split(delim) if value else [] | null |
144,365 | from os import path
from crosslinked.logger import Log
def delimiter2dict(value, delim_one=";", delim_two=":"):
x = {}
for item in value.split(delim_one):
if item:
sp = item.split(delim_two)
x[sp[0].strip()] = delim_two.join(sp[1:]).strip()
return x | null |
144,366 | from os import path
from crosslinked.logger import Log
class Log:
# Quick log class for CLI output
def info(msg):
print(' '.join([highlight('[*]', 'bold', 'blue'), msg]))
def success(msg):
print(' '.join([highlight('[+]', 'bold', 'green'), msg]))
def warn(msg):
print(' '.join(... | null |
144,367 | import logging
import requests
import threading
from time import sleep
from random import choice
from bs4 import BeautifulSoup
from unidecode import unidecode
from urllib.parse import urlparse
from crosslinked.logger import Log
from datetime import datetime, timedelta
from urllib3 import disable_warnings, exceptions
d... | null |
144,368 | import logging
import requests
import threading
from time import sleep
from random import choice
from bs4 import BeautifulSoup
from unidecode import unidecode
from urllib.parse import urlparse
from crosslinked.logger import Log
from datetime import datetime, timedelta
from urllib3 import disable_warnings, exceptions
de... | null |
144,369 | import logging
import requests
import threading
from time import sleep
from random import choice
from bs4 import BeautifulSoup
from unidecode import unidecode
from urllib.parse import urlparse
from crosslinked.logger import Log
from datetime import datetime, timedelta
from urllib3 import disable_warnings, exceptions
d... | null |
144,370 | import logging
import requests
import threading
from time import sleep
from random import choice
from bs4 import BeautifulSoup
from unidecode import unidecode
from urllib.parse import urlparse
from crosslinked.logger import Log
from datetime import datetime, timedelta
from urllib3 import disable_warnings, exceptions
d... | null |
144,371 | import os
import sys
import logging
def debug_args(args):
for k in args.__dict__:
logging.debug('{:20} => {}'.format(k, args.__dict__[k])) | null |
144,372 | import os
import sys
import logging
def highlight(data, style='bold', fg='blue'):
return code_gen(data, style, fg, windows=True if os.name == 'nt' else False)
def setup_debug_logger():
debug_output_string = "{} %(message)s".format(highlight('DEBUG', fg='purple'))
formatter = logging.Formatter(debug_output_... | null |
144,373 | import os
import sys
import logging
def first_run(logger):
# init headings in CSV log file
logger.info('Datetime,Search,Name,Title,URL,rawText')
def setup_file_logger(file_name, log_name='cLinked_file', file_mode='w'):
formatter = logging.Formatter('%(message)s')
fileHandler = logging.FileHandler(file_... | null |
144,374 | import os
import sys
import logging
def setup_cli_logger(log_level=logging.INFO, logger_name='cLinked'):
formatter = logging.Formatter('%(message)s')
StreamHandler = logging.StreamHandler(sys.stdout)
StreamHandler.setFormatter(formatter)
logger = logging.getLogger(logger_name)
logger.propagate = F... | null |
144,376 | from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'gfpgan/version.py'
def get_hash():
def write_version_py():
content = """# GENERATED VERSION FILE
# TIME: {}
__version__ = '{}'
__gitsha__ = '{}'
version_info = ({})
"""
sha = get_hash()
with open('VERSION', ... | null |
144,377 | from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'gfpgan/version.py'
def get_version():
with open(version_file, 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__'] | null |
144,380 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | null |
144,382 | import os
os.system('python setup.py develop')
os.system('pip install realesrgan')
import cv2
import shutil
import tempfile
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan import GFPGANer
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(fo... | null |
144,383 | import argparse
import math
import torch
from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
def modify_checkpoint(checkpoint_bilinear, checkpoint_clean):
for ori_k, ori_v in checkpoint_bilinear.items():
if 'stylegan_decoder' in ori_k:
if 'style_mlp' in ori_k: # style_mlp_layers
... | null |
144,384 | from ane_transformers.reference.layer_norm import LayerNormANE
import torch
import torch.nn as nn
from transformers.models.distilbert import modeling_distilbert
def correct_for_bias_scale_order_inversion(state_dict, prefix, local_metadata,
strict, missing_keys,
... | null |
144,385 | from ane_transformers.reference.layer_norm import LayerNormANE
import torch
import torch.nn as nn
from transformers.models.distilbert import modeling_distilbert
The provided code snippet includes necessary dependencies for implementing the `linear_to_conv2d_map` function. Write a Python function `def linear_to_conv2d_... | Unsqueeze twice to map nn.Linear weights to nn.Conv2d weights |
144,386 | import os
import traceback
import time
import pyuac
import asyncio
import questionary
import importlib
import tqdm
from docopt import docopt
from questionary import ValidationError
from pluggy import PluginManager
from get_width import get_width
from utils.log import log, fight_log
from utils.config import read_json_fi... | null |
144,387 | import sys
import pyuac
import traceback
import tkinter as tk
from tkinter import messagebox
try:
import time
import flet as ft
from re import sub
from cryptography.fernet import Fernet
from flet_core import MainAxisAlignment
from utils.log import log,level
from utils.map import Map as map_w... | if page.session.contains_key("updata_log"): page.session.remove("updata_log") |
144,388 | from .get_angle import get_camera_angle, get_angle
import time
import pyautogui
import win32con, win32api
import numpy as np
import sys
import cv2
print(f'前进{t}秒')
pyautogui.keyDown("w")
time.sleep(t)
pyautogui.keyUp("w")
def move(t, run=True):
print(f'前进{t}秒')
pyautogui.keyDown("w")
if run:
pyautogui.keyDow... | null |
144,389 | import time
import cv2 as cv
import numpy as np
import pygetwindow as gw
from PIL import Image, ImageGrab
from .config import _, sra_config_obj
from .log import log
def show_img(img, scale=1, title='Image'):
# cv.namedWindow('image', cv.WINDOW_NORMAL)
h, w = img.shape[:2]
img = cv.resize( img ,(int(w*scale... | null |
144,390 | import time
import cv2 as cv
import numpy as np
import pygetwindow as gw
from PIL import Image, ImageGrab
from .config import _, sra_config_obj
from .log import log
def show_imgs(imgs, title='Image'):
cv.imshow(title, np.hstack(imgs))
cv.waitKey(0)
cv.destroyAllWindows() | null |
144,391 | import time
from pynput.keyboard import Key
from utils.calculated import calculated
from utils.log import _, log
def get_percentile(rect, shape):
#获取长方形的相对坐标
x1,y1,x2,y2 = rect
w,h = shape
return [x1/w*100,y1/h*100,x2/w*100,y2/h*100] | null |
144,392 | import itertools
import os
import re
import sys
import time
import copy
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
import cv2 as cv
import numpy as np
import pywinctl as pwc n32api
import hashlib
import pprint
from cnocr import CnOcr
from P... | 说明： 将多个风格化文本序列，按行进行横向并联 参数： :param sep: 插入在文本序列间的内容，默认为空 :param prefix: 文本前缀 :param indent: 起始位置的缩进长度，默认为零 |
144,393 | import itertools
import os
import re
import sys
import time
import copy
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
import cv2 as cv
import numpy as np
import pywinctl as pwc n32api
import hashlib
import pprint
from cnocr import CnOcr
from P... | 说明： 打印风格化文本 |
144,394 | import itertools
import os
import re
import sys
import time
import copy
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
import cv2 as cv
import numpy as np
import pywinctl as pwc
import hashlib
import pprint
from cnocr import CnOcr
from PIL impo... | 说明： 求任意类型数据 (包括list和dict) 的哈希值 首先将数据规范化输出为str，再计算md5转16进制 参数: :param data: 任意类型数据 :param key_filter: 键值过滤器 :param speed_modified: 是否对速度属性进行修饰 (忽略小数位数值) |
144,395 | import itertools
import os
import re
import sys
import time
import copy
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
import cv2 as cv
import numpy as np
import pywinctl as pwc
import hashlib
import pprint
from cnocr import CnOcr
from PIL impo... | 说明： 封装str.rjust()&str.ljust()，以适应中文字符的实际宽度 |
144,396 | import gettext
import inspect
import os
import sys
import jsonschema
from pathlib import Path
from typing import Any, Union, get_type_hints
import orjson
from orjson import JSONDecodeError
from .exceptions import TypeError
from .log import log
os.makedirs(USER_DATA_PREFIX, exist_ok=True)
The provided code snippet incl... | 获取文件夹下的文件夹列表 |
144,397 | import gettext
import inspect
import os
import sys
import jsonschema
from pathlib import Path
from typing import Any, Union, get_type_hints
import orjson
from orjson import JSONDecodeError
from .exceptions import TypeError
from .log import log
The provided code snippet includes necessary dependencies for implementing ... | 说明: 在指定位置添加键值对 参数: :param dictionary 需要添加的字典 :param key: 键 :param value: 值 :param position: 需要添加的位置 返回: new_dictionary: 添加后的字典 |
144,398 | import gettext
import inspect
import os
import sys
import jsonschema
from pathlib import Path
from typing import Any, Union, get_type_hints
import orjson
from orjson import JSONDecodeError
from .exceptions import TypeError
from .log import log
The provided code snippet includes necessary dependencies for implementing ... | 说明: 将指定键值对插入指定key后面 参数: :param my_dict: 被操作的字典 :param new_key: 需要插入的key :param new_value: 需要插入的value :param insert_after_key: 插入到那个key后面 |
144,399 | import gettext
import inspect
import os
import sys
import jsonschema
from pathlib import Path
from typing import Any, Union, get_type_hints
import orjson
from orjson import JSONDecodeError
from .exceptions import TypeError
from .log import log
CONFIG_FILE_NAME = "config.json"
def read_json_file(filename: str, path=Fals... | null |
144,400 | import gettext
import inspect
import os
import sys
import jsonschema
from pathlib import Path
from typing import Any, Union, get_type_hints
import orjson
from orjson import JSONDecodeError
from .exceptions import TypeError
from .log import log
CONFIG_FILE_NAME = "config.json"
def read_json_file(filename: str, path=Fals... | 加载配置文件 |
144,401 | import time
import cv2 as cv
import numpy as np
import pywinctl as pwc import Image, ImageGrab
from .config import _, sra_config_obj
from .log import log
def show_img(img, scale=1, title='Image', not_close=False):
# cv.namedWindow('image', cv.WINDOW_NORMAL)
#h, w = img.shape[:2]
#img = cv.resize( img ,(int... | null |
144,402 | import time
import cv2 as cv
import numpy as np
import pywinctl as pwc import Image, ImageGrab
from .config import _, sra_config_obj
from .log import log
def show_imgs(imgs, title='Image'):
cv.imshow(title, np.hstack(imgs))
cv.waitKey(0)
cv.destroyAllWindows() | null |
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