id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
144,091 | import logging
import os
import io
import torch
import glob
from fastapi import FastAPI, Response
from pydantic import BaseModel
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transfor... | null |
144,092 | import logging
import os
import io
import torch
import glob
from fastapi import FastAPI, Response
from pydantic import BaseModel
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transfor... | null |
144,093 | import logging
import os
import io
import torch
import glob
from fastapi import FastAPI, Response
from pydantic import BaseModel
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transfor... | null |
144,094 | import logging
import os
import io
import torch
import glob
from fastapi import FastAPI, Response
from pydantic import BaseModel
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transfor... | null |
144,095 | from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transformers import AutoTokenizer
import os, sys, warnings, torch, glob, argparse
import numpy as np
from models.hifigan.get_vocoder import MAX_WAV_VALUE
import soundfile as sf
from yacs import con... | null |
144,096 | import argparse
import os
import jsonlines
import json
from tqdm import tqdm
from multiprocessing.pool import ThreadPool
from functools import partial
import re
import sys
from frontend_cn import split_py, tn_chinese
from frontend_en import read_lexicon, G2p
from frontend import contains_chinese, re_digits, g2p_cn
def... | null |
144,097 | import argparse
import os
import jsonlines
import json
from tqdm import tqdm
from multiprocessing.pool import ThreadPool
from functools import partial
import re
import sys
from frontend_cn import split_py, tn_chinese
from frontend_en import read_lexicon, G2p
from frontend import contains_chinese, re_digits, g2p_cn
def... | null |
144,098 | import argparse
import os
import jsonlines
import json
from tqdm import tqdm
from multiprocessing.pool import ThreadPool
from functools import partial
import re
import sys
from frontend_en import read_lexicon, G2p
def get_phoneme(text, g2p, lexicon):
filters = {",", " ", "'"}
phones = []
words = list(filter... | null |
144,099 | import re
import argparse
from string import punctuation
import numpy as np
from g2p_en import G2p
import os
def read_lexicon(lex_path):
lexicon = {}
with open(lex_path) as f:
for line in f:
temp = re.split(r"\s+", line.strip("\n"))
word = temp[0]
phones = temp[1:]
... | null |
144,100 | import requests
from utils.AuthV3Util import addAuthParams
= ''= ''
.mp3'
def doCall(url, header, params, method):
if 'get' == method:
return requests.get(url, params)
elif 'post' == method:
return requests.post(url, params, header)
def saveFile(res):
contentType = res.headers['Content-Type... | note: 将下列变量替换为需要请求的参数 |
144,102 | from cog import BasePredictor, Input, Path
from typing import List
import numpy as np
from yacs import config as CONFIG
import torch
import re
import os, glob
import time
import subprocess
import requests
import soundfile as sf
from frontend_cn import g2p_cn
from frontend_en import preprocess_english
from config.joint.... | null |
144,103 | from cog import BasePredictor, Input, Path
from typing import List
import numpy as np
from yacs import config as CONFIG
import torch
import re
import os, glob
import time
import subprocess
import requests
import soundfile as sf
from frontend_cn import g2p_cn
from frontend_en import preprocess_english
from config.joint.... | null |
144,104 | from cog import BasePredictor, Input, Path
from typing import List
import numpy as np
from yacs import config as CONFIG
import torch
import re
import os, glob
import time
import subprocess
import requests
import soundfile as sf
from frontend_cn import g2p_cn
from frontend_en import preprocess_english
from config.joint.... | null |
144,105 | from cog import BasePredictor, Input, Path
from typing import List
import numpy as np
from yacs import config as CONFIG
import torch
import re
import os, glob
import time
import subprocess
import requests
import soundfile as sf
from frontend_cn import g2p_cn
from frontend_en import preprocess_english
from config.joint.... | Download model weights from Replicate and save to file. Weights and download locations are specified in DEFAULT_WEIGHTS |
144,106 | import os
The provided code snippet includes necessary dependencies for implementing the `get_labels_length` function. Write a Python function `def get_labels_length(file_path)` to solve the following problem:
Return labels and their count in a file. Args: file_path (str): The path to the file containing the labels. R... | Return labels and their count in a file. Args: file_path (str): The path to the file containing the labels. Returns: list: labels; int: The number of labels in the file. |
144,108 | import streamlit as st
import os, glob
import numpy as np
from yacs import config as CONFIG
import torch
import re
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from config.joint.config import Config
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import S... | null |
144,109 | import streamlit as st
import os, glob
import numpy as np
from yacs import config as CONFIG
import torch
import re
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from config.joint.config import Config
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import S... | null |
144,110 | import streamlit as st
import os, glob
import numpy as np
from yacs import config as CONFIG
import torch
import re
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from config.joint.config import Config
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import S... | null |
144,111 | import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--text', type=str, help='Path to text.txt.')
parser.add_argument('--special_tokens',
type=str,
help='Path to special_token.txt')
parser.add_argument('--output', type=s... | null |
144,112 | import argparse
import pathlib
from typing import List, Set
import os
import numpy as np
from praatio import textgrid
def readtg(tg_path):
alignment = textgrid.openTextgrid(tg_path, includeEmptyIntervals=True)
phones = []
ends = []
for interval in alignment.getTier("phones")._entries:
phone = i... | null |
144,113 | import argparse
import pathlib
from typing import List, Set
import os
import numpy as np
from praatio import textgrid
SILENCE_TOKEN = set(['sp', 'sil'])
The provided code snippet includes necessary dependencies for implementing the `insert_special_tokens` function. Write a Python function `def insert_special_tokens(se... | Inserting special tokens into MFA aligned phoneme sequence. MFA aligned phoneme sequences contains no special token but contains silence phonemes such as 'sp' and 'sil'. However, FastSpeech2 expects phoneme sequences containing special tokens. This function will insert special tokens into MFA aligned phoneme sequence. ... |
144,114 | import argparse
import pathlib
from typing import List, Set
import os
import numpy as np
from praatio import textgrid
def get_args():
parser = argparse.ArgumentParser(
description="Preprocess audio and then extract features.")
parser.add_argument("--wav", type=str, help="Path to wav.txt.")
parser.a... | null |
144,115 | import argparse
import jsonlines
import pathlib
def read_lists(list_file):
lists = []
with open(list_file, 'r', encoding='utf8') as fin:
for line in fin:
lists.append(line.strip())
return lists | null |
144,116 | import argparse
import jsonlines
import pathlib
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--wav', type=str, help='Path to wav.txt.')
parser.add_argument('--speaker', type=str, help='Path to speaker.txt.')
parser.add_argument('--text', type=str, help='Path to text.txt.')
... | null |
144,117 | import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--special_tokens',
type=str,
help='Path to special_token.txt')
return parser.parse_args() | null |
144,118 | import argparse
import collections
import pathlib
import os
from typing import Iterable
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_dir',
type=str,
help='Path to cath dataset')
parser.add_argument('-... | null |
144,119 | import argparse
import collections
import pathlib
import os
from typing import Iterable
from tqdm import tqdm
def save_scp_files(wav_scp_path: os.PathLike, speaker_scp_path: os.PathLike,
text_scp_path: os.PathLike, content: Iterable[str]):
wav_scp_path = pathlib.Path(wav_scp_path)
speaker_sc... | null |
144,120 | import argparse
import pathlib
import random, os
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--wav", type=str, help='Path to wav.txt.')
parser.add_argument("--speaker", type=str, help='Path to speaker.txt.')
parser.add_argument(
"--text",
... | null |
144,121 | import torch
def get_segments( x: torch.Tensor, start_idxs: torch.Tensor, segment_size: int):
b, c, t = x.size()
segments = x.new_zeros(b, c, segment_size)
if t < segment_size:
x = torch.nn.functional.pad(x, (0, segment_size - t), 'constant')
for i, start_idx in enumerate(start_idxs):
se... | null |
144,122 | import os, json, torch
from models.hifigan.env import AttrDict
from models.hifigan.models import Generator
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
class Generator(torch.nn.Module):
def __init__(self, h):
... | null |
144,123 | import os, json, torch
from models.hifigan.env import AttrDict
from models.hifigan.models import Generator
class Generator(torch.nn.Module):
def __init__(self, h):
super(Generator, self).__init__()
self.h = h
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h... | null |
144,124 | import os, json, torch
from models.hifigan.env import AttrDict
from models.hifigan.models import Generator
def vocoder_inference(vocoder, melspec, max_db, min_db):
with torch.no_grad():
x = melspec*(max_db-min_db)+min_db
device = torch.device('cpu')
x = torch.FloatTensor(x).to(device)
... | null |
144,125 | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import remove_weight_norm, spectral_norm
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.we... | null |
144,126 | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import remove_weight_norm, spectral_norm
def get_padding(kernel_size, dilation=1):
return int((kernel_size*dilation - dilation)/2) | null |
144,128 | import torch
import jsonlines
from transformers import AutoTokenizer
import os, sys
import numpy as np
from scipy.io.wavfile import read
from torch.nn.utils.rnn import pad_sequence
import copy
from models.prompt_tts_modified.tacotron_stft import TacotronSTFT
def get_mel(filename, stft, sampling_rate, trim=False):
... | null |
144,129 | import torch
import jsonlines
from transformers import AutoTokenizer
import os, sys
import numpy as np
from scipy.io.wavfile import read
from torch.nn.utils.rnn import pad_sequence
import copy
from models.prompt_tts_modified.tacotron_stft import TacotronSTFT
def pad_mel(data, downsample_ratio, max_len ):
batch_si... | null |
144,130 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def initialize(model: torch.nn.Module, init: str):
for p in model.parameters():
if p.dim() > 1:
if init == "xavier_uniform":
torch.nn.init.xavier_uniform_(p.data)
elif init == "xavi... | null |
144,131 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from numba import jit
from scipy.stats import betabinom
def _monotonic_alignment_search(log_p_attn):
T_mel = log_p_attn.shape[0]
T_inp = log_p_attn.shape[1]
Q = np.full((T_inp, T_mel), fill_value=-np.inf)
log_prob = lo... | null |
144,132 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from numba import jit
from scipy.stats import betabinom
def _average_by_duration(ds, xs, text_lengths, feats_lengths):
B = ds.shape[0]
xs_avg = np.zeros_like(ds)
ds = ds.astype(np.int32)
for b in range(B):
t_te... | null |
144,133 | import torch
import math
from torch import nn
import torch.nn.functional as F
class MultiSequential(torch.nn.Sequential):
def __init__(self, *args, layer_drop_rate=0.0):
super(MultiSequential, self).__init__(*args)
self.layer_drop_rate = layer_drop_rate
def forward(self, *args):
_probs =... | null |
144,134 | import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def window_sumsquare(window,
n_frames,
hop_length=200,
win_length=800,
n_fft=800,
dtype=np.float32,
... | null |
144,135 | import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def griffin_lim(magnitudes, stft_fn, n_iters=30):
angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
angles = angles.astype(np.float32)
angles = torch.autograd.Variable(torch.fro... | null |
144,136 | import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C) | null |
144,137 | import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def dynamic_range_decompression(x, C=1):
return torch.exp(x) / C | null |
144,138 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def get_mask_from_lengths(lengths, max_len=None):
batch_size = lengths.shape[0]
if max_len is None:
max_len = torch.max(lengths).item()
ids = (
torch.arange(0, max_len).unsqueeze(0).expand(batch_size, -1)... | null |
144,139 | import torch
import jsonlines
from transformers import AutoTokenizer
import os, sys
import numpy as np
from scipy.io.wavfile import read
from torch.nn.utils.rnn import pad_sequence
import copy
from models.prompt_tts_modified.simbert import StyleEncoder
from models.prompt_tts_modified.tacotron_stft import TacotronSTFT
... | null |
144,140 | import torch
import jsonlines
from transformers import AutoTokenizer
import os, sys
import numpy as np
from scipy.io.wavfile import read
from torch.nn.utils.rnn import pad_sequence
import copy
from models.prompt_tts_modified.simbert import StyleEncoder
from models.prompt_tts_modified.tacotron_stft import TacotronSTFT
... | null |
144,141 | import torch
import torch.nn as nn
from transformers import AutoModel
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `flat_accuracy` function. Write a Python function `def flat_accuracy(preds, labels)` to solve the following problem:
Function to calculate the accuracy... | Function to calculate the accuracy of our predictions vs labels |
144,142 | import librosa
import numpy as np
import pyworld
from scipy.interpolate import interp1d
from librosa.filters import mel as librosa_mel_fn
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
from librosa.util import pad_center, tiny
impo... | null |
144,143 | import librosa
import numpy as np
import pyworld
from scipy.interpolate import interp1d
from librosa.filters import mel as librosa_mel_fn
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
from librosa.util import pad_center, tiny
impo... | PARAMS ------ magnitudes: spectrogram magnitudes stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods |
144,144 | import librosa
import numpy as np
import pyworld
from scipy.interpolate import interp1d
from librosa.filters import mel as librosa_mel_fn
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
from librosa.util import pad_center, tiny
impo... | PARAMS ------ C: compression factor |
144,145 | import librosa
import numpy as np
import pyworld
from scipy.interpolate import interp1d
from librosa.filters import mel as librosa_mel_fn
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
from librosa.util import pad_center, tiny
impo... | PARAMS ------ C: compression factor used to compress |
144,146 | import os
from docspec_python import ParserOptions
from docs.pydocs.pydocs_markdown_impl import render_loader
from pydoc_markdown.contrib.loaders.python import PythonLoader
from pydoc_markdown.contrib.renderers.markdown import MarkdownRenderer
from pydoc_markdown.contrib.processors.filter import FilterProcessor
def wr... | null |
144,147 | import inspect
def module_to_string(
module,
display_string,
ignore_prefix_list=[],
include_list=[],
indents=1,
visited=set(),
ignore_attrs=False
):
if module in visited:
return ""
visited.add(module)
module_str = f"{'#'*indents} {display_string}\n"
module_docs = insp... | null |
144,148 | from pydoc_markdown.interfaces import Context
from pydoc_markdown.contrib.renderers.markdown import MarkdownRenderer, MarkdownReferenceResolver
from pydoc_markdown.contrib.processors.filter import FilterProcessor
from pydoc_markdown.contrib.processors.google import GoogleProcessor
def render_loader(loader, processor =... | null |
144,149 | import inspect
from collections import defaultdict
from copy import deepcopy
from string import Template
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
from langchain_core.messages import BaseMessage
from langchain_core.runnables im... | null |
144,150 | import inspect
from collections import defaultdict
from copy import deepcopy
from string import Template
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
from langchain_core.messages import BaseMessage
from langchain_core.runnables im... | null |
144,151 | import inspect
from collections import defaultdict
from copy import deepcopy
from string import Template
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
from langchain_core.messages import BaseMessage
from langchain_core.runnables im... | Register a validator for a data type. |
144,152 | import inspect
from collections import defaultdict
from copy import deepcopy
from string import Template
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
from langchain_core.messages import BaseMessage
from langchain_core.runnables im... | null |
144,153 | import asyncio
import itertools
import os
from concurrent.futures import ProcessPoolExecutor
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from guardrails.classes.history import Iteration
from guardrails.datatypes import FieldValidation
from guardrails.errors import ValidationError
f... | null |
144,154 | import asyncio
import itertools
import os
from concurrent.futures import ProcessPoolExecutor
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from guardrails.classes.history import Iteration
from guardrails.datatypes import FieldValidation
from guardrails.errors import ValidationError
f... | null |
144,155 | import asyncio
import itertools
import os
from concurrent.futures import ProcessPoolExecutor
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from guardrails.classes.history import Iteration
from guardrails.datatypes import FieldValidation
from guardrails.errors import ValidationError
f... | null |
144,156 | from guardrails.logger import set_config, set_level
def set_config(config=None):
def set_level(level=None):
def configure_logging(logging_config=None, log_level=None):
set_config(logging_config)
set_level(log_level) | null |
144,157 | import inspect
import json
import sys
from dataclasses import InitVar, asdict, dataclass, field, is_dataclass
from json import JSONEncoder
from typing import Any, Dict
from pydash.strings import snake_case
if sys.version_info.minor >= 10:
encoder_kwargs["kw_only"] = True
encoder_kwargs["default"] = Serializeabl... | null |
144,158 | import asyncio
from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional, Union, cast
from guard_rails_api_client.models.validate_payload_llm_api import ValidatePayloadLlmApi
from pydantic import BaseModel
from guardrails.utils.exception_utils import UserFacingException
from guardrails.utils.llm_respo... | Prepare final prompt for nonchat engine. |
144,159 | import asyncio
from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional, Union, cast
from guard_rails_api_client.models.validate_payload_llm_api import ValidatePayloadLlmApi
from pydantic import BaseModel
from guardrails.utils.exception_utils import UserFacingException
from guardrails.utils.llm_respo... | Prepare final prompt for chat engine. |
144,160 | import asyncio
from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional, Union, cast
from guard_rails_api_client.models.validate_payload_llm_api import ValidatePayloadLlmApi
from pydantic import BaseModel
from guardrails.utils.exception_utils import UserFacingException
from guardrails.utils.llm_respo... | Prepare messages for LiteLLM. |
144,161 | import asyncio
from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional, Union, cast
from guard_rails_api_client.models.validate_payload_llm_api import ValidatePayloadLlmApi
from pydantic import BaseModel
from guardrails.utils.exception_utils import UserFacingException
from guardrails.utils.llm_respo... | null |
144,162 | import asyncio
from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional, Union, cast
from guard_rails_api_client.models.validate_payload_llm_api import ValidatePayloadLlmApi
from pydantic import BaseModel
from guardrails.utils.exception_utils import UserFacingException
from guardrails.utils.llm_respo... | null |
144,163 | import copy
import json
from functools import partial
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
from pydantic import BaseModel
from guardrails.classes.history import Call, Inputs, Iteration, Outputs
from guardrails.datatypes import verify_metadata_requirements
from guardrails.errors imp... | null |
144,164 | import copy
import json
from functools import partial
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
from pydantic import BaseModel
from guardrails.classes.history import Call, Inputs, Iteration, Outputs
from guardrails.datatypes import verify_metadata_requirements
from guardrails.errors imp... | null |
144,165 | import asyncio
import json
import os
from string import Template
from typing import Callable, Dict, Optional, Type, cast
from guardrails.classes import ValidationOutcome
from guardrails.document_store import DocumentStoreBase, EphemeralDocumentStore
from guardrails.embedding import EmbeddingBase, OpenAIEmbedding
from g... | null |
144,166 | import os
import sys
from string import Template
import typer
from pydash import snake_case
from guardrails.cli.hub.hub import hub_command
from guardrails.cli.logger import LEVELS, logger
from guardrails.cli.server.hub_client import HttpError, post_validator_submit
LEVELS = {
"SPAM": 5,
"VERBOSE": 15,
"NOT... | Submit a validator to the Guardrails AI team for review and publishing. |
144,167 | import json
import os
import subprocess
import sys
from email.parser import BytesHeaderParser
from string import Template
from typing import List, Literal, Union
import typer
from pydash.strings import snake_case
from guardrails.classes.generic import Stack
from guardrails.cli.hub.hub import hub_command
from guardrails... | null |
144,168 | import json
import os
import subprocess
import sys
from email.parser import BytesHeaderParser
from string import Template
from typing import List, Literal, Union
import typer
from pydash.strings import snake_case
from guardrails.classes.generic import Stack
from guardrails.cli.hub.hub import hub_command
from guardrails... | Install a validator from the Hub. |
144,169 | import os
from datetime import date
from string import Template
import typer
from pydash import pascal_case, snake_case
from guardrails.cli.hub.hub import hub_command
from guardrails.cli.logger import LEVELS, logger
validator_template = Template(
"""
\"""
This template is intended for creating simple validators.
If... | Lightweight method for creating simple validators. For more complex submissions see here: https://github.com/guardrails-ai/validator-template?tab=readme-ov-file#how-to-create-a-guardrails-validator |
144,170 | import json
from typing import Dict, List, Union
import typer
from guardrails import Guard
from guardrails.cli.guardrails import guardrails
def validate_llm_output(rail: str, llm_output: str) -> Union[str, Dict, List, None]:
"""Validate guardrails.yml file."""
guard = Guard.from_rail(rail)
result = guard.pa... | Validate the output of an LLM against a `rail` spec. |
144,171 | import os
import sys
import uuid
from os.path import expanduser
from typing import Optional
import typer
from guardrails.cli.guardrails import guardrails
from guardrails.cli.logger import LEVELS, logger
from guardrails.cli.server.hub_client import AuthenticationError, get_auth
def save_configuration_file(
client_id... | Set the global configuration for the Guardrails CLI and Hub. |
144,172 | import datetime
from dataclasses import dataclass
from types import SimpleNamespace
from typing import Any, Dict, Iterable
from typing import List as TypedList
from typing import Optional, Sequence, Type, TypeVar, Union
from dateutil.parser import parse
from lxml import etree as ET
from typing_extensions import Self
fr... | null |
144,173 | import datetime
from dataclasses import dataclass
from types import SimpleNamespace
from typing import Any, Dict, Iterable
from typing import List as TypedList
from typing import Optional, Sequence, Type, TypeVar, Union
from dateutil.parser import parse
from lxml import etree as ET
from typing_extensions import Self
fr... | null |
144,174 | import logging
import logging.config
from logging import Handler, LogRecord
from typing import Dict, List, Optional
_logger = logging.getLogger(name)
logger_config = LoggerConfig()
def _setup_handler(log_level=logging.NOTSET, scope=base_scope) -> ScopeHandler:
global handler
if not handler:
handler = Sc... | null |
144,175 | import logging
import logging.config
from logging import Handler, LogRecord
from typing import Dict, List, Optional
base_scope = "base"
logger_config = LoggerConfig()
def get_scope_handler() -> ScopeHandler:
global _logger
try:
scope_handler: ScopeHandler = [
h for h in _logger.handlers if i... | null |
144,176 | from contextvars import ContextVar, copy_context
from typing import Any, Dict, Literal, Optional, Union, cast
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import Tracer
TRACER_KEY: Literal["gr.reserved.tracer"] = "gr.reserved.tracer"
def set_context_var(key, value... | null |
144,177 | from contextvars import ContextVar, copy_context
from typing import Any, Dict, Literal, Optional, Union, cast
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import Tracer
TRACER_CONTEXT_KEY: Literal["gr.reserved.tracer.context"] = "gr.reserved.tracer.context"
def se... | null |
144,178 | from contextvars import ContextVar, copy_context
from typing import Any, Dict, Literal, Optional, Union, cast
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import Tracer
CALL_KWARGS_KEY: Literal["gr.reserved.call_kwargs"] = "gr.reserved.call_kwargs"
def set_context... | null |
144,179 | from contextvars import ContextVar, copy_context
from typing import Any, Dict, Literal, Optional, Union, cast
from opentelemetry import context
from opentelemetry.context import Context
from opentelemetry.trace import Tracer
def get_call_kwargs() -> Dict[str, Any]:
return get_context_var(CALL_KWARGS_KEY) or {}
def... | null |
144,180 | from typing import Any, Optional
def to_int(v: Any) -> Optional[int]:
try:
int_value = int(v)
return int_value
except Exception:
return None | null |
144,181 | from typing import Any, Optional
def to_float(v: Any) -> Optional[float]:
try:
float_value = float(v)
return float_value
except Exception:
return None | null |
144,182 | from typing import Any, Dict
def kwargs(**kwargs) -> Dict[str, Any]:
return kwargs | null |
144,183 | from typing import Literal, get_args
ON_FAIL_TYPES = Literal[
"exception", "fix", "fix_reask", "reask", "filter", "refrain", "noop", "custom"
]
def on_fail(fix_type: ON_FAIL_TYPES = "noop"):
options = get_args(ON_FAIL_TYPES)
assert fix_type in options, f"'{fix_type}' is not in {options}"
return {"on_fa... | null |
144,184 | import collections
from string import Template
from typing import List, Optional, Tuple
def get_template_variables(template: str) -> List[str]:
if hasattr(Template, "get_identifiers"):
return Template(template).get_identifiers() # type: ignore
else:
d = collections.defaultdict(str)
Tem... | null |
144,185 | import typing as t
from guardrails.prompt import Prompt
try:
import nltk # type: ignore
except ImportError:
nltk = None
if nltk is not None:
try:
nltk.data.find("tokenizers/punkt")
except LookupError:
nltk.download("punkt")
The provided code snippet includes necessary dependencies for ... | Split the text into sentences. |
144,186 | import typing as t
from guardrails.prompt import Prompt
The provided code snippet includes necessary dependencies for implementing the `read_pdf` function. Write a Python function `def read_pdf(path) -> str` to solve the following problem:
Reads the pdf at the given path.
Here is the function:
def read_pdf(path) -> ... | Reads the pdf at the given path. |
144,187 | import typing as t
from guardrails.prompt import Prompt
try:
import tiktoken
except ImportError:
tiktoken = None
try:
import nltk # type: ignore
except ImportError:
nltk = None
if nltk is not None:
try:
nltk.data.find("tokenizers/punkt")
except LookupError:
nltk.download("punkt"... | Get chunks of text from a string. Args: text: The text to chunk. chunk_strategy: The strategy to use for chunking. chunk_size: The size of each chunk. If the chunk_strategy is "sentences", this is the number of sentences per chunk. If the chunk_strategy is "characters", this is the number of characters per chunk, and s... |
144,188 | import json
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Type, Union
import regex
from guardrails.datatypes import (
Boolean,
Case,
Choice,
DataType,
Date,
Enum,
Float,
Integer,
)
from guardrails.datatypes import List as ListDataType
from guardrails.da... | Verify that a JSON schema is valid for a given XML. |
144,189 | import json
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Type, Union
import regex
from guardrails.datatypes import (
Boolean,
Case,
Choice,
DataType,
Date,
Enum,
Float,
Integer,
)
from guardrails.datatypes import List as ListDataType
from guardrails.da... | null |
144,190 | from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple, Union
import pydantic
from guardrails.datatypes import List as ListType
from guardrails.datatypes import Object as ObjectType
from guardrails.validator_base import FailResult
class FieldReAsk(ReAsk):
path: Optional[List[Any]] = None
cla... | Prune tree of any elements that are not in `reasks`. Return the tree with only the elements that are keys of `reasks` and their parents. If `reasks` is None, return the entire tree. If an element is removed, remove all ancestors that have no children. Args: root: The XML tree. reasks: The elements that are to be reaske... |
144,191 | from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple, Union
import pydantic
from guardrails.datatypes import List as ListType
from guardrails.datatypes import Object as ObjectType
from guardrails.validator_base import FailResult
class FieldReAsk(ReAsk):
path: Optional[List[Any]] = None
The... | If a ReAsk object exists in the dict, return it as a dictionary. |
144,192 | from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple, Union
import pydantic
from guardrails.datatypes import List as ListType
from guardrails.datatypes import Object as ObjectType
from guardrails.validator_base import FailResult
class FieldReAsk(ReAsk):
path: Optional[List[Any]] = None
The... | Substitute ReAsk objects with their fixed values recursively. Args: value: Either a list, a dictionary, a ReAsk object or a scalar value. Returns: The value with ReAsk objects replaced with their fixed values. |
144,193 | from typing import Any, List
def args(*args: Any) -> List[Any]:
return list(args) | null |
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