id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
100 | import importlib
import re
from absl import app
from absl import flags
import gin
import seqio
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `sequence_length` function. Write a Python function `def sequence_length(value=512)` to solve the following pro... | Sequence length used when tokenizing. Args: value: an integer or dictionary Returns: a dictionary |
101 | import importlib
import re
from absl import app
from absl import flags
import gin
import seqio
import tensorflow.compat.v1 as tf
FLAGS = flags.FLAGS
The provided code snippet includes necessary dependencies for implementing the `pretty` function. Write a Python function `def pretty(value)` to solve the following probl... | Optional pretty printing helper for detokenized inputs. Makes any text delimiter regex specified in `--delimiters` bold in textual output. Args: value: string representing the detokenized output Returns: a string with appropriate styling applied |
102 | import importlib
import re
from absl import app
from absl import flags
import gin
import seqio
import tensorflow.compat.v1 as tf
def import_modules(modules):
for module in modules:
importlib.import_module(module) | null |
103 | import functools
import os
import re
from typing import Any, Callable, Iterable, Mapping, MutableSequence, Optional, Sequence, Tuple, Union
from absl import logging
import gin
import numpy as np
import seqio
import t5.data
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
import typing_extensions
_MO... | Filters example features, keeping only valid model features. |
104 | import functools
import os
import re
from typing import Any, Callable, Iterable, Mapping, MutableSequence, Optional, Sequence, Tuple, Union
from absl import logging
import gin
import numpy as np
import seqio
import t5.data
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
import typing_extensions
def... | Get checkpoints from model directory. **Note**: This only works for models checkpoints saved using Tensorflow. Args: checkpoint_steps: list, int or str. If checkpoint_step is an int, find the checkpoint with the closest global step and return a singleton list. If checkpoint_step is a list of ints, replace each int with... |
105 | import functools
import os
import re
from typing import Any, Callable, Iterable, Mapping, MutableSequence, Optional, Sequence, Tuple, Union
from absl import logging
import gin
import numpy as np
import seqio
import t5.data
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
import typing_extensions
def... | Run evaluation on the given mixture or task. Args: mixture_or_task_name: str, the name of the Mixture or Task to evaluate on. Must be pre-registered in the global `TaskRegistry` or `MixtureRegistry.` predict_or_score_fn: function, This function takes in the sequence length, checkpoint step, tasks to evaluate, an eval_d... |
106 | import importlib
import os
import sys
from absl import app
from absl import flags
from absl import logging
import gin
from mesh_tensorflow.transformer import utils
import pkg_resources
import t5
from t5.models import mesh_transformer
from t5.models import mtf_model
import tensorflow.compat.v1 as tf
def main(_):
if FL... | null |
107 | import functools
import os
import gin
import gin.tf
import mesh_tensorflow as mtf
from mesh_tensorflow import optimize
from mesh_tensorflow.transformer import dataset as transformer_dataset
from mesh_tensorflow.transformer import learning_rate_schedules
from mesh_tensorflow.transformer import utils as mtf_utils
from t5... | null |
108 | import functools
from absl import logging
import gin
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
from t5.models import utils as model_utils
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing ... | Returns the tf.data.Dataset for training on a given mixture. This uses the format required for utils.run's `train_dataset_fn` argument in the Mesh TF transformer standalone. Args: mixture_or_task_name: string, an identifier for a Mixture or Task in the appropriate registry. Must be specified via gin. sequence_length: d... |
109 | import functools
from absl import logging
import gin
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
from t5.models import utils as model_utils
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing ... | Returns all tf.data.Datasets for LM inference on a given mixture. For Tasks without inputs (such as language modeling), the first `priming_sequence_length` tokens in the target are used as the "inputs" for inference. Args: mixture_or_task_name: string, an identifier for a Mixture or Task in the appropriate registry. Mu... |
110 | import functools
from absl import logging
import gin
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
from t5.models import utils as model_utils
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing ... | Returns all tf.data.Datasets for evaluation on a given mixture. This uses the format required for utils.run's `eval_dataset_fn` argument in the Mesh TF transformer standalone. Args: mixture_or_task_name: string, an identifier for a Mixture or Task in the appropriate registry. Must be specified via gin. sequence_length:... |
111 | import functools
from absl import logging
import gin
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
from t5.models import utils as model_utils
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing ... | r"""Returns a dataset based on a TSV file formatted as `<input>\t<target>`. |
112 | import functools
from absl import logging
import gin
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
from t5.models import utils as model_utils
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing ... | Get the appropriate value for the utils.run.vocabulary argument. Args: mixture_or_task_name: string, an identifier for a Mixture or Task in the appropriate registry. Must be specified via gin. Returns: Either a single seqio.vocabularies.Vocabulary or a tuple of seqio.vocabularies.Vocabulary for inputs and targets. |
113 | import functools
import itertools
import os
import re
import time
from absl import logging
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
import t5.data
from t5.models import utils
from t5.models.t5_model import T5Model
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds... | Convert a dataset of token sequences to batches of padded/masked examples. Args: dataset: tf.data.Dataset containing examples with token sequences. sequence_length: dict of int, a dict mapping feature name to length. batch_size: int, the number of padded sequences in each batch. output_features: list of str, features t... |
114 | import functools
import itertools
import os
import re
import time
from absl import logging
import mesh_tensorflow.transformer.dataset as transformer_dataset
import seqio
import t5.data
from t5.models import utils
from t5.models.t5_model import T5Model
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds... | Get a tf.data.Dataset for a given Task or Mixture. Args: mixture_or_task_or_name: Task or Mixture or str, the name of the Mixture or Task to train on or the Tasks or Mixture object itself. Must be pre-registered in the global `t5.data.TaskRegistry` or `t5.data.MixtureRegistry.` sequence_length: dict of int, a dict mapp... |
115 | import argparse, os, shutil, time
import cv2
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision.transforms import Compose, ToTensor, Resize
from torchvision.transforms.functional import to_pil_image
from threading import Thread, Lock
from tqdm import tqdm
from PIL import Image
fr... | null |
116 | import argparse
import kornia
import torch
import os
import random
from torch import nn
from torch import distributed as dist
from torch import multiprocessing as mp
from torch.nn import functional as F
from torch.cuda.amp import autocast, GradScaler
from torch.utils.tensorboard import SummaryWriter
from torch.utils.da... | null |
117 | import argparse
import torch
import os
import shutil
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms as T
from torchvision.transforms.functional import to_pil_image
from threading import Thread
from tqdm import tqdm
from dataset import... | null |
118 | import os
import re
from setuptools import setup
from setuptools import find_packages
with open("README.md", "r") as fh:
long_description = fh.read()
with open("requirements.txt", encoding="utf8") as f:
requirements = f.readlines()
def find_version(*filepath):
here = os.path.abspath(os.path.dirname(__file_... | null |
119 | import warnings
import os
import numpy as np
from gradient_free_optimizers import (
HillClimbingOptimizer,
StochasticHillClimbingOptimizer,
RepulsingHillClimbingOptimizer,
SimulatedAnnealingOptimizer,
DownhillSimplexOptimizer,
RandomSearchOptimizer,
GridSearchOptimizer,
RandomRestartHill... | null |
120 | import warnings
import os
import gc
import glob
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib as mpl
import matplotlib.pyplot as plt
from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter
def warn(*args, **kwargs):
pass | null |
121 | import warnings
import os
import gc
import glob
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib as mpl
import matplotlib.pyplot as plt
from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter
def plot_search_paths(
path,
optimizer,
opt_para,
n_ite... | null |
122 | import numpy as np
from gradient_free_optimizers import HillClimbingOptimizer
def convex_function(pos_new):
score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
return score | null |
123 | import numpy as np
from gradient_free_optimizers import GridSearchOptimizer
def convex_function(pos_new):
score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
return score | null |
124 | import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def ackley_function(pos_new):
x = pos_new["x1"]
y = pos_new["x2"]
a1 = -20 * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
a2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
score = a1 + a2 + 20
retu... | null |
125 | import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def convex_function(pos_new):
score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
return score | null |
126 | import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def constraint_1(para):
# only values in 'x1' higher than -5 are valid
return para["x1"] > -5 | null |
127 | from keras.models import Sequential
from keras.layers import (
Dense,
Conv2D,
MaxPooling2D,
Flatten,
Dropout,
Activation,
)
from keras.datasets import cifar10
from keras.utils import to_categorical
from gradient_free_optimizers import BayesianOptimizer
import numpy as np
(X_train, y_train), (X_t... | null |
128 | import numpy as np
import matplotlib.pyplot as plt
from gradient_free_optimizers import HillClimbingOptimizer
def gaussian_function(x, A, B, C):
return A * np.exp(-(x-B)**2/(2*C**2))
x_range = np.arange(min_x, max_x, step_x)
y_gauss_hist = plt.hist(gauss_np1, density=True, bins=bins)[0]
def fit_gaussian(para):
... | null |
129 | import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_wine
from gradient_free_optimizers import HillClimbingOptimizer
X, y = data.data, data.target
def model(para):
gbc = GradientBoostingClassifier(
n... | null |
130 | import numpy as np
from gradient_free_optimizers import LipschitzOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
131 | import numpy as np
from gradient_free_optimizers import EvolutionStrategyOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
132 | import numpy as np
from gradient_free_optimizers import SimulatedAnnealingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
133 | import numpy as np
from gradient_free_optimizers import GridSearchOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
134 | import numpy as np
from gradient_free_optimizers import PatternSearch
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
135 | import numpy as np
from gradient_free_optimizers import PowellsMethod
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
136 | import numpy as np
from gradient_free_optimizers import SpiralOptimization
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
137 | import numpy as np
from gradient_free_optimizers import HillClimbingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
138 | import numpy as np
from gradient_free_optimizers import DownhillSimplexOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
139 | import numpy as np
from gradient_free_optimizers import ParallelTemperingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
140 | import numpy as np
from gradient_free_optimizers import StochasticHillClimbingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
141 | import numpy as np
from gradient_free_optimizers import BayesianOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
142 | import numpy as np
from gradient_free_optimizers import DirectAlgorithm
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
143 | import numpy as np
from gradient_free_optimizers import RepulsingHillClimbingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
144 | import numpy as np
from gradient_free_optimizers import TreeStructuredParzenEstimators
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
145 | import numpy as np
from gradient_free_optimizers import ParticleSwarmOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
146 | import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
147 | import numpy as np
from gradient_free_optimizers import RandomRestartHillClimbingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
148 | import numpy as np
from gradient_free_optimizers import RandomAnnealingOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
149 | import numpy as np
from gradient_free_optimizers import ForestOptimizer
def sphere_function(para):
x = para["x"]
y = para["y"]
return -(x * x + y * y) | null |
150 | import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_wine
from gradient_free_optimizers import HillClimbingOptimizer
X, y = data.data, data.target
def model(para):
gbc = DecisionTreeClassifier(
min_samples_s... | null |
151 | import random
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `set_random_seed` function. Write a Python function `def set_random_seed(nth_process, random_state)` to solve the following problem:
Sets the random seed separately for each thread (to avoid getting the same... | Sets the random seed separately for each thread (to avoid getting the same results in each thread) |
152 | import random
import numpy as np
def move_random(ss_positions):
position = []
for search_space_pos in ss_positions:
pos_ = random.choice(search_space_pos)
position.append(pos_)
return np.array(position) | null |
153 | import numpy as np
from scipy.stats import norm
from ..smb_opt.smbo import SMBO
from ..smb_opt.surrogate_models import EnsembleRegressor
from ..smb_opt.acquisition_function import ExpectedImprovement
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm i... | null |
154 | import random
import numpy as np
from ..local_opt import HillClimbingOptimizer
def max_list_idx(list_):
max_item = max(list_)
max_item_idx = [i for i, j in enumerate(list_) if j == max_item]
return max_item_idx[-1:][0] | null |
155 | import numpy as np
from collections import OrderedDict
from ..local_opt import HillClimbingOptimizer
def sort_list_idx(list_):
list_np = np.array(list_)
idx_sorted = list(list_np.argsort()[::-1])
return idx_sorted | null |
156 | import numpy as np
from ..local_opt import HillClimbingOptimizer
def roation(n_dim, vector):
if n_dim == 1:
return -1 # not sure about that
I = np.identity(n_dim - 1)
R = np.pad(I, ((1, 0), (0, 1)), constant_values=(0, 0))
R[0, n_dim - 1] = -1
return np.matmul(R, vector) | null |
157 | import numpy as np
from .base_population_optimizer import BasePopulationOptimizer
from ._spiral import Spiral
def centeroid(array_list):
centeroid = []
for idx in range(array_list[0].shape[0]):
center_dim_pos = []
for array in array_list:
center_dim_pos.append(array[idx])
c... | null |
158 | import math
import numpy as np
from ..core_optimizer import CoreOptimizer
def split(positions_l, population):
div_int = math.ceil(len(positions_l) / population)
dist_init_positions = []
for nth_indiv in range(population):
indiv_pos = []
for nth_indiv_pos in range(div_int):
idx ... | null |
159 | import numpy as np
from sklearn.linear_model import BayesianRidge
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern, WhiteKernel, RBF
from sklearn.ensemble import ExtraTreesRegressor as _ExtraTreesRegressor_
from sklearn.ensemble import RandomForestRegress... | used from: https://github.com/scikit-optimize/scikit-optimize/blob/master/skopt/learning/forest.py |
160 | import numpy as np
from scipy.stats import norm
def normalize(array):
num = array - array.min()
den = array.max() - array.min()
if den == 0:
return np.random.random_sample(array.shape)
else:
return ((num / den) + 0) / 1 | null |
161 | import numpy as np
from scipy.stats import norm
from .smbo import SMBO
from .surrogate_models import (
GPR_linear,
GPR,
)
from .acquisition_function import ExpectedImprovement
def normalize(array):
num = array - array.min()
den = array.max() - array.min()
if den == 0:
return np.random.rand... | null |
162 | import numpy as np
from scipy.stats import norm
from .smbo import SMBO
from .surrogate_models import (
RandomForestRegressor,
ExtraTreesRegressor,
GradientBoostingRegressor,
)
from .acquisition_function import ExpectedImprovement
def normalize(array):
num = array - array.min()
den = array.max() - a... | null |
163 | import random
import numpy as np
from .hill_climbing_optimizer import HillClimbingOptimizer
def sort_list_idx(list_):
list_np = np.array(list_)
idx_sorted = list(list_np.argsort()[::-1])
return idx_sorted | null |
164 | import random
import numpy as np
from .hill_climbing_optimizer import HillClimbingOptimizer
def centeroid(array_list):
centeroid = []
for idx in range(array_list[0].shape[0]):
center_dim_pos = []
for array in array_list:
center_dim_pos.append(array[idx])
center_dim_mean = ... | null |
165 | import numpy as np
from ..base_optimizer import BaseOptimizer
from numpy.random import normal, laplace, logistic, gumbel
def max_list_idx(list_):
max_item = max(list_)
max_item_idx = [i for i, j in enumerate(list_) if j == max_item]
return max_item_idx[-1:][0] | null |
166 | import numpy as np
def _print_times(eval_time, iter_time, n_iter):
opt_time = iter_time - eval_time
iterPerSec = n_iter / iter_time
print(
indent,
"Evaluation time :",
eval_time,
"sec",
indent,
"[{} %]".format(round(eval_time / iter_time * 100, 2)),
)
... | null |
167 | import time
import numpy as np
def time_exceeded(start_time, max_time):
run_time = time.time() - start_time
return max_time and run_time > max_time | null |
168 | import time
import numpy as np
def score_exceeded(score_best, max_score):
return max_score and score_best >= max_score | null |
169 | import time
import numpy as np
def no_change(score_new_list, early_stopping):
if "n_iter_no_change" not in early_stopping:
print(
"Warning n_iter_no_change-parameter must be set in order for early stopping to work"
)
return False
n_iter_no_change = early_stopping["n_iter_no... | null |
170 | import traceback
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List
from readmeai.config.settings import ConfigLoader
from readmeai.core.logger import Logger
_logger = Logger(__name__)
class QuickStart:
"""Information about using, running, and testing a repository."""
insta... | Generates the 'Quick Start' section of the README file. |
171 | import re
from typing import List
EMOJI_PATTERN = re.compile(
pattern="["
"\U0001f600-\U0001f64f" # emoticons
"\U0001f300-\U0001f5ff" # symbols & pictographs
"\U0001f680-\U0001f6ff" # transport & map symbols
"\U0001f700-\U0001f77f" # alchemical symbols
"\U0001f780-\U0001f7ff" # Geometric Sh... | Removes emojis from the content list. |
172 | import re
from typing import List
The provided code snippet includes necessary dependencies for implementing the `split_markdown_headings` function. Write a Python function `def split_markdown_headings(markdown_text: str) -> dict` to solve the following problem:
Splits a markdown document by level 2 headings into sepa... | Splits a markdown document by level 2 headings into separate sections. |
173 | import re
from typing import List
The provided code snippet includes necessary dependencies for implementing the `update_heading_names` function. Write a Python function `def update_heading_names(md_contents: dict) -> dict` to solve the following problem:
Updates dict keys by removing leading emojis, underscores, and ... | Updates dict keys by removing leading emojis, underscores, and spaces. |
174 | from pathlib import Path
from typing import List, Tuple
from readmeai.services.git import fetch_git_file_url
def is_valid_tuple_summary(summary: Tuple[str, str]) -> bool:
"""Checks if a summary is a valid tuple format."""
return isinstance(summary, tuple) and len(summary) == 2
The provided code snippet include... | Converts the given code summaries into a formatted list. |
175 | from pathlib import Path
from typing import List, Tuple
from readmeai.services.git import fetch_git_file_url
def construct_markdown_table(
data: List[Tuple[str, str]], repo_url: str, full_name: str
) -> str:
"""Builds a Markdown table from the provided data."""
headers = ["File", "Summary"]
table_rows =... | Produces Markdown tables for each project sub-directory. |
176 | from typing import Tuple
from readmeai.cli.options import BadgeOptions
from readmeai.config.settings import ConfigLoader
from readmeai.services.git import GitHost
from readmeai.utils.file_handler import FileHandler
from readmeai.utils.file_resources import get_resource_path
_package = "readmeai.generators"
_submodule =... | Generates badges for the README using skill icons, from the repository - https://github.com/tandpfun/skill-icons. |
177 | from typing import Dict, Type
from readmeai.core.parsers import BaseFileParser
from readmeai.parsers.configuration.docker import (
DockerComposeParser,
DockerfileParser,
)
from readmeai.parsers.configuration.properties import PropertiesParser
from readmeai.parsers.language.cpp import (
CMakeParser,
Conf... | Returns a dictionary of callable file parser methods. |
178 | import asyncio
import tempfile
import traceback
from pathlib import Path
from typing import Optional
from readmeai._exceptions import ReadmeGeneratorError
from readmeai.cli.options import ImageOptions, ModelOptions
from readmeai.config.settings import ConfigLoader, GitSettings
from readmeai.core.logger import Logger
fr... | Configures and runs the README file generator agent. |
179 | from __future__ import annotations
from enum import Enum
from typing import Optional
import click
class ImageOptions(str, Enum):
"""
Enum for CLI options for README file header images.
"""
# Custom image options
CUSTOM = "custom"
LLM = "llm"
# Default image options
BLACK = "https://img.i... | Prompt the user for a custom image URL. |
180 | import os
import platform
import shutil
from enum import Enum
from pathlib import Path
from typing import Optional
import git
from readmeai._exceptions import GitCloneError
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `find_git_executable` funct... | Find the path to the git executable, if available. |
181 | import os
import platform
import shutil
from enum import Enum
from pathlib import Path
from typing import Optional
import git
from readmeai._exceptions import GitCloneError
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `validate_file_permissions`... | Validates file permissions of the cloned repository. |
182 | import os
import platform
import shutil
from enum import Enum
from pathlib import Path
from typing import Optional
import git
from readmeai._exceptions import GitCloneError
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `validate_git_executable` f... | Validate the path to the git executable. |
183 | from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import aiohttp
from readmeai.core.logger import Logger
from readmeai.services.git import fetch_git_api_url
_logger = Logger(__name__)
class RepositoryMetadata:
"""Dataclass to store GitHub repository metadata."""
name: str
full_n... | Retrieves GitHub repository metadata and returns a dataclass. |
184 | from typing import Dict, List, Union
import readmeai.config.settings as Settings
from readmeai.core.logger import Logger
_logger = Logger(__name__)
def get_prompt_template(prompts: dict, prompt_type: str) -> str:
"""Retrieves the template for the given prompt type."""
prompt_templates = {
"features": pr... | Generates a prompt for the LLM API. |
185 | from typing import Dict, List, Union
import readmeai.config.settings as Settings
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `set_additional_contexts` function. Write a Python function `async def set_additional_contexts( config: Settings, ... | Generates additional prompts (features, overview, slogan) for LLM. |
186 | from typing import Dict, List, Union
import readmeai.config.settings as Settings
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `set_summary_context` function. Write a Python function `async def set_summary_context( config: Settings, depen... | Generates the summary prompts to be used by the LLM API. |
187 | from tiktoken import get_encoding
from readmeai.config.settings import Settings
from readmeai.core.logger import Logger
_logger = Logger(__name__)
def count_tokens(text: str, encoder: str) -> int:
"""Return the number of tokens in a text string."""
try:
encoding = _set_encoding_cache(encoder)
to... | Handle token count for the prompt. |
188 | from tiktoken import get_encoding
from readmeai.config.settings import Settings
from readmeai.core.logger import Logger
The provided code snippet includes necessary dependencies for implementing the `update_max_tokens` function. Write a Python function `def update_max_tokens( max_tokens: int, prompt: str, target: ... | Adjust the maximum number of tokens based on the specific prompt. |
189 | import re
def clean_text(text: str) -> str:
"""Format and clean generated text from the LLM."""
# Dynamically remove all text before and including the first colon if any exist
text = re.sub(r"^[^:]*:\s*", "", text)
# Remove any text before and including "**:"
text = re.sub(r"\*\*:\s*", "", text, fla... | Post-processes the response from the LLM. |
190 | import re
The provided code snippet includes necessary dependencies for implementing the `fix_md_table_rows` function. Write a Python function `def fix_md_table_rows(md_table: str) -> str` to solve the following problem:
Format a Markdown table with feature and description columns.
Here is the function:
def fix_md_t... | Format a Markdown table with feature and description columns. |
191 | import nox
def install(session, groups, root=True):
"""Install the package in the current session."""
if root:
groups = ["main", *groups]
session.run_always(
"poetry",
"install",
"--no-root",
"--sync",
f"--only={','.join(groups)}",
external=True,
)... | Run the test suite across Python versions |
192 | from typing import List, Optional
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `summary` function. Write a Python function `def summary(text: str, minlength: Optional[int] = None, maxle... | Runs a summarization model against a block of text. Args: text: text to summarize minlength: minimum length for summary maxlength: maximum length for summary Returns: summary text |
193 | from typing import List, Optional
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `batchsummary` function. Write a Python function `def batchsummary(texts: List[str] = Body(...), minlength... | Runs a summarization model against a block of text. Args: texts: list of text to summarize minlength: minimum length for summary maxlength: maximum length for summary Returns: list of summary text |
194 | from typing import List
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `tabular` function. Write a Python function `def tabular(file: str)` to solve the following problem:
Splits tabular ... | Splits tabular data into rows and columns. Args: file: file to process Returns: list of (id, text, tag) elements |
195 | from typing import List
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `batchtabular` function. Write a Python function `def batchtabular(files: List[str] = Body(...))` to solve the follo... | Splits tabular data into rows and columns. Args: files: list of files to process Returns: list of (id, text, tag) elements |
196 | from typing import List, Optional
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `translate` function. Write a Python function `def translate(text: str, target: Optional[str] = "en", sour... | Translates text from source language into target language. Args: text: text to translate target: target language code, defaults to "en" source: source language code, detects language if not provided Returns: translated text |
197 | from typing import List, Optional
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `batchtranslate` function. Write a Python function `def batchtranslate(texts: List[str] = Body(...), targe... | Translates text from source language into target language. Args: texts: list of text to translate target: target language code, defaults to "en" source: source language code, detects language if not provided Returns: list of translated text |
198 | from typing import List, Optional
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
The provided code snippet includes necessary dependencies for implementing the `extract` function. Write a Python function `def extract(queue: List[dict] = Body(...), texts: Optional[Li... | Extracts answers to input questions. Args: queue: list of {name: value, query: value, question: value, snippet: value} texts: optional list of text Returns: list of {name: value, answer: value} |
199 | from typing import List
from fastapi import APIRouter, Body
from .. import application
from ..route import EncodingAPIRoute
def label(text: str = Body(...), labels: List[str] = Body(...)):
"""
Applies a zero shot classifier to text using a list of labels. Returns a list of
{id: value, score: value} sorted b... | Applies a zero shot classifier to list of text using a list of labels. Returns a list of {id: value, score: value} sorted by highest score, where id is the index in labels per text element. Args: texts: list of text labels: list of labels Returns: list of {id: value score: value} per text element |
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