crossfile_context_retrievalwref dict | prompt stringlengths 252 32.6k | right_context stringlengths 0 81.2k | metadata dict | crossfile_context_retrieval dict | groundtruth stringlengths 5 208 |
|---|---|---|---|---|---|
{
"list": [
{
"filename": "train.py",
"retrieved_chunk": " dist.print0()\n dist.print0('Training options:')\n dist.print0(json.dumps(c, indent=2))\n dist.print0()\n dist.print0(f'Output directory: {c.run_dir}')\n dist.print0(f'Dataset path: {c.dataset_kwargs.path}')... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
stats_jsonl = None
batch_mul_dict = {512: 1, 256: 2, 128: 4, 64: 16, 32: 32, 16: 64}
if train_on_latents:
p_list = np.array([(1 - real_p), real_p])
patch_list = np.array([img_resolution // 2, img_resolution])
batch_mul_avg = np.sum(p_list * np.array([2, 1]))
else:
p_list... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 144,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 145,
"task_id": "project_cc_python/4562"
} | {
"list": [
{
"filename": "train.py",
"retrieved_chunk": " dist.print0(f'Batch size: {c.batch_size}')\n dist.print0(f'Mixed-precision: {c.network_kwargs.use_fp16}')\n dist.print0()\n # Dry run?\n if opts.dry_run:\n dist.print0('Dry run; exiting.')\n re... | update_progress(cur_nimg // 1000, total_kimg) |
{
"list": [
{
"filename": "meta-vsc-descriptor-runtime/runtime/validation.py",
"retrieved_chunk": "logger.setLevel(logging.INFO)\nclass DataValidationError(AssertionError):\n pass\ndef validate_total_descriptors(dataset: str, n_features: int, total_seconds: float):\n if n_features > total_second... | import numpy as np
import pytest
import validation
# Run with python -m pytest tests/test_validation.py from runtime/
def test_dim_too_large():
features = np.random.randn(10, 513).astype("float32")
with pytest.raises(validation.DataValidationError):
validation.validate_descriptor_dim("test", features... |
def test_length_validation():
video_ids = np.array(["Q200001", "Q200001", "Q200002", "Q200003"])
timestamps = np.array([[0, 10], [10, 20], [0, 10]])
features = np.random.randn(4, 16).astype("float32")
submission = {
"video_ids": video_ids,
"timestamps": timestamps,
"features":... | {
"context_start_lineno": 0,
"file": "meta-vsc-descriptor-runtime/runtime/tests/test_validation.py",
"groundtruth_start_lineno": 29,
"repository": "line-Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution-b8cf098",
"right_context_start_lineno": 30,
"task_id": "project_cc_python/4386"
} | {
"list": [
{
"filename": "meta-vsc-descriptor-runtime/runtime/validation.py",
"retrieved_chunk": " query_features = np.load(args.query_features, allow_pickle=False)\n ref_features = np.load(args.ref_features, allow_pickle=False)\n query_meta = pd.read_csv(args.query_metadata)\n ref_meta =... | validate_total_descriptors("test", features.shape[0], total_seconds) |
{
"list": [
{
"filename": "meta-vsc-descriptor-runtime/runtime/validation.py",
"retrieved_chunk": " validate_descriptor_dtype(\"reference\", ref_features[\"features\"])\n validate_descriptor_dim(\"query\", query_features[\"features\"], max_dim=512)\n validate_descriptor_dim(\"reference\", ref... | import numpy as np
import pytest
import validation
# Run with python -m pytest tests/test_validation.py from runtime/
def test_dim_too_large():
features = np.random.randn(10, 513).astype("float32")
with pytest.raises(validation.DataValidationError):
validation.validate_descriptor_dim("test", features... |
def test_total_descriptors():
features = np.random.randn(100, 64).astype("float32")
total_seconds = 50
with pytest.raises(validation.DataValidationError):
validation.validate_total_descriptors("test", features.shape[0], total_seconds)
def test_length_validation():
video_ids = np.array(["Q20... | {
"context_start_lineno": 0,
"file": "meta-vsc-descriptor-runtime/runtime/tests/test_validation.py",
"groundtruth_start_lineno": 22,
"repository": "line-Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution-b8cf098",
"right_context_start_lineno": 23,
"task_id": "project_cc_python/4385"
} | {
"list": [
{
"filename": "meta-vsc-descriptor-runtime/runtime/validation.py",
"retrieved_chunk": " validate_descriptor_dtype(\"reference\", ref_features[\"features\"])\n validate_descriptor_dim(\"query\", query_features[\"features\"], max_dim=512)\n validate_descriptor_dim(\"reference\", ref... | validate_sorted_ids("test", video_ids) |
{
"list": [
{
"filename": "meta-vsc-matching-runtime/submission_src/src/vsc/storage.py",
"retrieved_chunk": " feats = []\n timestamps = []\n for feature in features:\n video_id = format_video_id(feature.video_id, dataset)\n video_ids.append(np.full(len(feature), video_id))\n ... | import numpy as np
import pytest
import validation
# Run with python -m pytest tests/test_validation.py from runtime/
def test_dim_too_large():
features = np.random.randn(10, 513).astype("float32")
with pytest.raises(validation.DataValidationError):
validation.validate_descriptor_dim("test", features... |
timestamps = np.array([[0, 10], [10, 20], [0, 10], [10, 20]])
features = np.random.randn(3, 16).astype("float32")
submission = {
"video_ids": video_ids,
"timestamps": timestamps,
"features": features,
}
with pytest.raises(validation.DataValidationError):
validation.... | {
"context_start_lineno": 0,
"file": "meta-vsc-descriptor-runtime/runtime/tests/test_validation.py",
"groundtruth_start_lineno": 42,
"repository": "line-Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution-b8cf098",
"right_context_start_lineno": 43,
"task_id": "project_cc_python/4387"
} | {
"list": [
{
"filename": "meta-vsc-matching-runtime/submission_src/src/vsc/storage.py",
"retrieved_chunk": " np.savez(f, video_ids=video_ids, features=feats, timestamps=timestamps)\ndef same_value_ranges(values):\n start = 0\n value = values[start]\n for i, v in enumerate(values):\n ... | validate_lengths("test", submission) |
{
"list": [
{
"filename": "meta-vsc-matching-runtime/submission_src/src/vsc/index.py",
"retrieved_chunk": " query_id = query_ids[i]\n query_idx = query_indices[i]\n query_metadata = query_metadatas[query_id]\n ref_id = self.video_clip_to_video_ids[j]\n ... | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import abc
from typing import List
import numpy as np
import torch
from src.vsc.index import VideoFeature
from src.vsc.metrics import Candi... |
matches.append(match)
return matches
def localize(self, candidate: CandidatePair) -> List[Match]:
return self.localize_all([candidate])
def score(self, candidate: CandidatePair, match: Match, box, similarity) -> float:
return 1.0
class VCSLLocalizationMaxSim(VCSLLoca... | {
"context_start_lineno": 0,
"file": "meta-vsc-matching-runtime/submission_src/src/vsc/localization.py",
"groundtruth_start_lineno": 86,
"repository": "line-Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution-b8cf098",
"right_context_start_lineno": 87,
"task_id": "project_cc_python/4379"
} | {
"list": [
{
"filename": "meta-vsc-matching-runtime/submission_src/src/vsc/index.py",
"retrieved_chunk": " )\n pair_nns[query_id, ref_id].append(match)\n return [\n PairMatches(query_id, ref_id, matches)\n for ((query_id, ref_id), matches) in pair_nn... | _replace(score=score) |
{
"list": [
{
"filename": "training/dataset.py",
"retrieved_chunk": " if fname not in self._all_fnames:\n return None\n with self._open_file(fname) as f:\n labels = json.load(f)['labels']\n if labels is None:\n return None\n labels = dict(la... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
if progressive:
p_cumsum = p_list.cumsum()
p_cumsum[-1] = 10.
prog_mask = (cur_nimg // 1000 / total_kimg) <= p_cumsum
patch_size = int(patch_list[prog_mask][0])
batch_mul_avg = batch_mul_dict[patch_size]... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 160,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 161,
"task_id": "project_cc_python/4563"
} | {
"list": [
{
"filename": "training/dataset.py",
"retrieved_chunk": " return labels\n#----------------------------------------------------------------------------",
"score": 50.603534135175444
},
{
"filename": "dataset_tool.py",
"retrieved_chunk": " if img.ndim ... | ddp_sync(ddp, (round_idx == num_accumulation_rounds - 1)): |
{
"list": [
{
"filename": "training/loss.py",
"retrieved_chunk": " self.epsilon_t = epsilon_t\n def __call__(self, net, images, labels, augment_pipe=None):\n rnd_uniform = torch.rand([images.shape[0], 1, 1, 1], device=images.device)\n sigma = self.sigma(1 + rnd_uniform * (self.... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
loss.sum().mul(loss_scaling / batch_gpu_total / batch_mul).backward()
# loss.mean().mul(loss_scaling / batch_mul).backward()
# Update weights.
for g in optimizer.param_groups:
g['lr'] = optimizer_kwargs['lr'] * min(cur_nimg / max(lr_rampup_kimg * 1000, 1e-8)... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 187,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 188,
"task_id": "project_cc_python/4564"
} | {
"list": [
{
"filename": "training/patch_loss.py",
"retrieved_chunk": " D_yn = net(yn, sigma, x_pos=images_pos, class_labels=labels, augment_labels=augment_labels)\n loss = weight * ((D_yn - y) ** 2)\n return loss\n#----------------------------------------------------------------... | report('Loss/loss', loss) |
{
"list": [
{
"filename": "train.py",
"retrieved_chunk": " if opts.implicit_mlp:\n c.network_kwargs.implicit_mlp = True\n c.network_kwargs.update(dropout=opts.dropout, use_fp16=opts.fp16)\n # Training options.\n c.total_kimg = max(int(opts.duration * 1000), 1)\n c.ema_halflife_ki... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<9.1f}"]
fields += [f"loss {loss.mean().item():<9.3f}"]
fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
fields += [f"sec/tick {training_s... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 216,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 217,
"task_id": "project_cc_python/4565"
} | {
"list": [
{
"filename": "train.py",
"retrieved_chunk": " if opts.seed is not None:\n c.seed = opts.seed\n else:\n seed = torch.randint(1 << 31, size=[], device=torch.device('cuda'))\n torch.distributed.broadcast(seed, src=0)\n c.seed = int(seed)\n # Transfer lear... | report0('Progress/tick', cur_tick):<5d}"] |
{
"list": [
{
"filename": "generate.py",
"retrieved_chunk": " class_labels = None\n if net.label_dim:\n class_labels = torch.eye(net.label_dim, device=device)[rnd.randint(net.label_dim, size=[batch_size], device=device)]\n if class_idx is not None:\n class_la... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
data[key] = value.cpu()
del value # conserve memory
if dist.get_rank() == 0:
with open(os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl'), 'wb') as f:
pickle.dump(data, f)
del data # conserve memory
... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 241,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 242,
"task_id": "project_cc_python/4567"
} | {
"list": [
{
"filename": "generate.py",
"retrieved_chunk": " images = sampler_fn(net, latents, latents_pos, mask_pos, class_labels, randn_like=rnd.randn_like, **sampler_kwargs)\n if on_latents:\n images = 1 / 0.18215 * images\n images = img_vae.decode(images.float(... | check_ddp_consistency(value) |
{
"list": [
{
"filename": "generate.py",
"retrieved_chunk": " images = sampler_fn(net, latents, latents_pos, mask_pos, class_labels, randn_like=rnd.randn_like, **sampler_kwargs)\n if on_latents:\n images = 1 / 0.18215 * images\n images = img_vae.decode(images.float(... | # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-... |
if dist.get_rank() == 0:
if stats_jsonl is None:
stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'at')
stats_jsonl.write(json.dumps(dict(training_stats.default_collector.as_dict(), timestamp=time.time())) + '\n')
stats_jsonl.flush()
dist.upda... | {
"context_start_lineno": 0,
"file": "training/training_loop.py",
"groundtruth_start_lineno": 254,
"repository": "Zhendong-Wang-Patch-Diffusion-929b0c0",
"right_context_start_lineno": 255,
"task_id": "project_cc_python/4568"
} | {
"list": [
{
"filename": "generate.py",
"retrieved_chunk": " if image_np.shape[2] == 1:\n PIL.Image.fromarray(image_np[:, :, 0], 'L').save(image_path)\n else:\n PIL.Image.fromarray(image_np, 'RGB').save(image_path)\n # Done.\n torch.distribute... | default_collector.update() |
{
"list": [
{
"filename": "util/outputWriter.py",
"retrieved_chunk": " for sample_key, sample_value in each_candidate.support_cnv_calls.items():\n if sample_value:\n ids = []\n scores = []\n ... | import pandas as pd
import re
import logging as logger
import os
import pysam
import gzip
from analysis.cnv_candidate import CNVCandidate
class VCFSNVParser(object):
def __init__(self, min_chromosome_len=1e6, as_dev=False):
self.as_dev = as_dev
logger.basicConfig(level=logger.DEBUG) if as_dev else... |
candidate.statistics['z-score']['sample_score'] = sample_gq
if "GT" in format_set:
gt_idx = format_set.index('GT')
candidate.gt = sample_cells[gt_idx]
# Could be left black as only details for the known variant are known and l... | {
"context_start_lineno": 0,
"file": "util/vcf_parser.py",
"groundtruth_start_lineno": 198,
"repository": "fritzsedlazeck-Spectre-01b8ed5",
"right_context_start_lineno": 199,
"task_id": "project_cc_python/4573"
} | {
"list": [
{
"filename": "util/outputWriter.py",
"retrieved_chunk": " file_handler.write(f'{vcf_header}\\n')\n file_handler.write(f'{vcf_sample_header}\\n')\n file_handler.write(f'{vcf_lines}\\n')\n file_handler.close()\nclass IntermediateFile(object):\n def __init__(se... | statistics['z-score'] = {} |
{
"list": [
{
"filename": "examples/classification/nin_cifar.py",
"retrieved_chunk": " root=root, train=True, download=True, transform=augmentation\n)\ntrain_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size, shuffle=True)\nval_dataset = torchvision.datasets.CIFAR10(\n root=root... | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from catalyst import dl
import sys
import os
from torch_integral import IntegralWrapper
from torch_integral import UniformDistribution
from torch_integral import standard_continuous_dims
class MnistNet(nn.Module):
de... |
wrapper = IntegralWrapper(init_from_discrete=True)
model = wrapper(model, [1, 1, 28, 28], continuous_dims)
ranges = [[16, 16], [32, 64], [16, 32]]
model.reset_distributions([UniformDistribution(*r) for r in ranges])
# ------------------------------------------------------------------------------------
# Train
# -----... | {
"context_start_lineno": 0,
"file": "examples/classification/mnist.py",
"groundtruth_start_lineno": 68,
"repository": "TheStageAI-TorchIntegral-baf578b",
"right_context_start_lineno": 69,
"task_id": "project_cc_python/4577"
} | {
"list": [
{
"filename": "examples/classification/nin_cifar.py",
"retrieved_chunk": "# ------------------------------------------------------------------------------------\nmodel = nin_cifar10().cuda()\ncontinuous_dims = {}\nfor name, mod in model.named_modules():\n if \"stage3\" in name:\n ... | update({"linear.weight": [1], "linear.bias": [], "conv_1.weight": [0]}) |
{
"list": [
{
"filename": "src/stripe_integrations/webhooks/subscriptions.py",
"retrieved_chunk": " if self.event.customer:\n StripeCustomer.sync(self.event.customer)\nclass CustomerSubscriptionCreatedWebhook(CustomerSubscriptionBaseWebhook):\n name = \"customer.subscription.creat... | # Third Party Stuff
from django.apps import apps
from django.conf import settings
# Stripe Integrations Stuff
from stripe_integrations.actions import StripeCustomer
from stripe_integrations.settings import stripe_settings
from stripe_integrations.webhooks.base import BaseWebhook
class CustomerUpdatedWebhook(BaseWebh... | {
"context_start_lineno": 0,
"file": "src/stripe_integrations/webhooks/customers.py",
"groundtruth_start_lineno": 55,
"repository": "twopointone-stripe-integrations-ec992eb",
"right_context_start_lineno": 56,
"task_id": "project_cc_python/4524"
} | {
"list": [
{
"filename": "src/stripe_integrations/webhooks/subscriptions.py",
"retrieved_chunk": "class CustomerSubscriptionTrialWillEndWebhook(CustomerSubscriptionBaseWebhook):\n name = \"customer.subscription.trial_will_end\"\n description = \"Occurs three days before the trial period of a su... | soft_delete(self.event.customer) | |
{
"list": [
{
"filename": "examples/server.py",
"retrieved_chunk": "import asyncio\nfrom sa import *\nfrom sa.samp import *\nimport traceback\nimport math\ndef on_message(message, internal_packet, peer, server):\n if message.id == MSG.RPC:\n rpc = message\n if rpc.rpc_id == RPC.CLICK_... | import asyncio
from sa import samp
from sa.samp import MSG, RPC
'''
Login; placeholders(username + password)
'''
class ZombieServer(samp.Server):
def __init__(self):
super().__init__(('127.0.0.1', 7777))
self.hostname = 'Zombie Apocalypse'
self.gamemode = 'Survival'
self.language =... |
#todo peer.push_message(samp.ShowTextdraw(1, 0, samp.Vec2(5, 5), 0xff0000ff, samp.Vec2(5, 5), 0, 0, 0, 0, 0, 0, samp.Vec2(100, 100), 0, samp.Vec3(0,0,0), 0, 0, 0, 'aaa'))
elif message.id == MSG.CONNECTION_REQUEST:
peer.password = message.password
async def main():
s = ZombieSer... | {
"context_start_lineno": 0,
"file": "examples/zombie.py",
"groundtruth_start_lineno": 20,
"repository": "pitaya1001-hta-c83dc5c",
"right_context_start_lineno": 21,
"task_id": "project_cc_python/4515"
} | {
"list": [
{
"filename": "examples/server.py",
"retrieved_chunk": " if rpc.player_id == 0:\n peer.push_message(GiveWeapon(WEAPON.M4, 250))\n elif rpc.player_id == 1:\n peer.push_message(RemoveAllWeapons())\n elif rpc.rpc_id == RPC.REQUEST_CHA... | ChatMessage('Welcome survivor!', 0x1aab84ff)) |
{
"list": [
{
"filename": "torch_integral/model.py",
"retrieved_chunk": " \"start_index\": start,\n }\n )\n self.rearranger(params, feature_maps, group.size)\n def preprocess_model(\n self,\n model,\n ... | import torch
from .tsp_solver import two_opt_find_permutation
def total_variance(tensors):
"""
Calculates total variation of tensors along given dimension.
Parameters
----------
tensors: List[Dict[str, obj]].
List of dicts with keys 'value' and 'dim'.
Returns
-------
total_va... |
return indices
def _select_tensors(self, params, feature_maps):
"""Returns list of tensors which total variation should be optimized."""
return params
class NOptOutFiltersPermutation(NOptPermutation):
"""
Class implements NOptPermutation
interface for optimzation of out filt... | {
"context_start_lineno": 0,
"file": "torch_integral/permutation.py",
"groundtruth_start_lineno": 99,
"repository": "TheStageAI-TorchIntegral-baf578b",
"right_context_start_lineno": 100,
"task_id": "project_cc_python/4575"
} | {
"list": [
{
"filename": "torch_integral/model.py",
"retrieved_chunk": " if group is not another_group:\n start += another_group.size\n else:\n break\n for p in parent.params:\n param... | type(torch.long).to(device) |
{
"list": [
{
"filename": "examples/server.py",
"retrieved_chunk": "import asyncio\nfrom sa import *\nfrom sa.samp import *\nimport traceback\nimport math\ndef on_message(message, internal_packet, peer, server):\n if message.id == MSG.RPC:\n rpc = message\n if rpc.rpc_id == RPC.CLICK_... | import asyncio
from sa import samp
from sa.samp import RPC, MSG
def on_message(message, internal_packet, peer, client):
print(message)
#if message.id == MSG.RPC:
# rpc = message
# print(rpc)
async def f(c):
await asyncio.sleep(5)
c.server_peer.push_message(samp.RconCommand('login changem... |
c.name = 'bob'
c.message_callbacks.append(on_message)
await c.start()
asyncio.get_event_loop().create_task(f(c))
while True:
await asyncio.sleep(0.01)
c.update()
try:
asyncio.run(main())
except KeyboardInterrupt:
pass
| {
"context_start_lineno": 0,
"file": "examples/client.py",
"groundtruth_start_lineno": 15,
"repository": "pitaya1001-hta-c83dc5c",
"right_context_start_lineno": 16,
"task_id": "project_cc_python/4511"
} | {
"list": [
{
"filename": "examples/server.py",
"retrieved_chunk": " if rpc.player_id == 0:\n peer.push_message(GiveWeapon(WEAPON.M4, 250))\n elif rpc.player_id == 1:\n peer.push_message(RemoveAllWeapons())\n elif rpc.rpc_id == RPC.REQUEST_CHA... | Client(('127.0.0.1', 7777)) |
{
"list": [
{
"filename": "python/tests/test_vcf_reader.py",
"retrieved_chunk": "def test_vcf_reader_missing_file():\n with pytest.raises(OSError):\n VCFReader(\"test.vcf\")\ndef test_vcf_indexed_reader_query():\n reader = VCFIndexedReader(DATA / \"vcf_file.vcf.gz\")\n rbr = reader.que... | # Test the fasta reader can be converted to a polars dataframe
from pathlib import Path
import importlib
import pytest
from biobear import BamReader, BamIndexedReader
DATA = Path(__file__).parent / "data"
@pytest.mark.skipif(
not importlib.util.find_spec("polars"), reason="polars not installed"
)
def test_bam... |
assert 1 == sum(b.num_rows for b in rbr)
def test_bam_indexed_reader_no_file():
with pytest.raises(OSError):
BamIndexedReader("test.bam")
| {
"context_start_lineno": 0,
"file": "python/tests/test_bam_reader.py",
"groundtruth_start_lineno": 41,
"repository": "wheretrue-biobear-5be051c",
"right_context_start_lineno": 42,
"task_id": "project_cc_python/4606"
} | {
"list": [
{
"filename": "python/tests/test_bcf_reader.py",
"retrieved_chunk": "def test_bcf_indexed_reader_query():\n \"\"\"Test the BCFIndexedReader.query() method.\"\"\"\n reader = BCFIndexedReader(DATA / \"index.bcf\")\n rbr = reader.query(\"1\")\n assert 191 == sum(b.num_rows for b i... | query("chr1:12203700-12205426") |
{
"list": [
{
"filename": "services/extract_metadata.py",
"retrieved_chunk": " {\"role\": \"user\", \"content\": text},\n ]\n completion = get_chat_completion(messages, \"gpt-4\")\n print(f\"completion: {completion}\")\n try:\n metadata = json.loads(completion)\n except:\n... | from services.openai import get_chat_completion
def screen_text_for_pii(text: str) -> bool:
# This prompt is just an example, change it to fit your use case
messages = [
{
"role": "system",
"content": f"""
You can only respond with the word "True" or "False", where ... |
return True
return False
| {
"context_start_lineno": 0,
"file": "services/pii_detection.py",
"groundtruth_start_lineno": 26,
"repository": "jamescalam-ask-lex-plugin-3769174",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4507"
} | {
"list": [
{
"filename": "services/extract_metadata.py",
"retrieved_chunk": " {\"role\": \"user\", \"content\": text},\n ]\n completion = get_chat_completion(messages, \"gpt-4\")\n print(f\"completion: {completion}\")\n try:\n metadata = json.loads(completion)\n except:\n... | startswith("True"): |
{
"list": [
{
"filename": "bot/manager.py",
"retrieved_chunk": " def __init__(self, model: Model) -> None:\n self.piston_provider = Piston(model)\n self.providers: list[Provider] = [GodBolt(model), self.piston_provider]\n self.runtimes = models.RuntimeTree()\n self.model... | import crescent
import hikari
from bot.config import CONFIG
from bot.manager import Manager
Plugin = crescent.Plugin[hikari.GatewayBot, "Model"]
INTENTS = hikari.Intents.ALL_UNPRIVILEGED | hikari.Intents.MESSAGE_CONTENT
class Model:
def __init__(self) -> None:
self.manager = Manager(self)
async def... |
client = crescent.Client(app, model := Model())
@app.listen(hikari.StartingEvent)
async def _(_: hikari.StartingEvent) -> None:
await model.startup()
@app.listen(hikari.StoppingEvent)
async def _(_: hikari.StoppingEvent) -> None:
await model.shutdown()
client.plugins.load_fol... | {
"context_start_lineno": 0,
"file": "bot/app.py",
"groundtruth_start_lineno": 22,
"repository": "CircuitSacul-io-775ac7c",
"right_context_start_lineno": 23,
"task_id": "project_cc_python/4598"
} | {
"list": [
{
"filename": "bot/manager.py",
"retrieved_chunk": " await asyncio.gather(\n *(asyncio.create_task(p.shutdown()) for p in self.providers)\n )\n async def update_data(self) -> None:\n await asyncio.gather(\n *(asyncio.create_task(p.update_data()... | TOKEN, intents=INTENTS) |
{
"list": [
{
"filename": "bot/plugins/instance.py",
"retrieved_chunk": " ComponentID.CODE_BLOCK,\n placeholder=\"Select the code block to run\",\n )\n for x, block in enumerate(self.codes):\n if block.filename:\n la... | from __future__ import annotations
import re
import typing as t
from hikari import Attachment, Message
from bot import models
CODE_BLOCK_REGEX = re.compile(r"```(?P<lang>\w*)[\n\s]*(?P<code>(.|\n)*?)```")
CODE_LINE_REGEX = re.compile(r"`(?P<code>[^`\n]+)`")
async def get_codes(message: Message) -> list[models.Cod... |
if language := dct.get("lang"):
code.language = language
blocks.append(code)
content = CODE_BLOCK_REGEX.sub("", content)
for line in CODE_LINE_REGEX.finditer(content):
blocks.append(models.Code(code=line.groupdict()["code"]))
return blocks
async def _get_code_attachm... | {
"context_start_lineno": 0,
"file": "bot/utils/parse.py",
"groundtruth_start_lineno": 28,
"repository": "CircuitSacul-io-775ac7c",
"right_context_start_lineno": 29,
"task_id": "project_cc_python/4600"
} | {
"list": [
{
"filename": "bot/plugins/help.py",
"retrieved_chunk": " ):\n return\n await message.message.respond(embeds=HELP_EMBEDS, reply=True)",
"score": 31.07049818212053
},
{
"filename": "bot/plugins/instance.py",
"retrieved_chunk": " # version\n ... | Code(code=dct["code"]) |
{
"list": [
{
"filename": "ljd/rawdump/header.py",
"retrieved_chunk": "class Flags:\n def __init__(self):\n self.is_big_endian = False\n self.is_stripped = False\n self.has_ffi = False\n self.fr2 = False\nclass Header:\n def __init__(self):\n self.version = 0\n... | #
# Copyright (C) 2013 Andrian Nord. See Copyright Notice in main.py
#
import ljd.bytecode.constants as constants
import ljd.bytecode.debuginfo as debug
class Flags:
def __init(self):
self.has_sub_prototypes = False
self.is_variadic = False
self.has_ffi = False
self.has_jit = True... | {
"context_start_lineno": 0,
"file": "ljd/bytecode/prototype.py",
"groundtruth_start_lineno": 30,
"repository": "weaweawe01-luajit_decompile-2b32d4b",
"right_context_start_lineno": 31,
"task_id": "project_cc_python/4505"
} | {
"list": [
{
"filename": "ljd/rawdump/header.py",
"retrieved_chunk": " self.origin = b''\n self.name = b''\ndef read(state, header):\n r = True\n header.origin = state.stream.name\n r = r and _check_magic(state)\n r = r and _read_version(state, header)\n r = r and _read_f... | DebugInformation() | |
{
"list": [
{
"filename": "kaflow/_utils/inspect.py",
"retrieved_chunk": "R = TypeVar(\"R\")\ndef is_not_coroutine_function(\n func: Callable[P, R | Awaitable[R]]\n) -> TypeGuard[Callable[P, R]]:\n \"\"\"Check if a function is not a coroutine function. This function narrows the type\n of the ... | from __future__ import annotations
import asyncio
import contextvars
import functools
from typing import Awaitable, Callable, TypeVar
from typing_extensions import ParamSpec
P = ParamSpec("P")
R = TypeVar("R")
# Inspired by https://github.com/tiangolo/asyncer
def asyncify(func: Callable[P, R]) -> Callable[P, Await... |
ctx = contextvars.copy_context()
func_call = functools.partial(ctx.run, func, *args, **kwargs)
return await loop.run_in_executor(None, func_call) # type: ignore
return wrapper
| {
"context_start_lineno": 0,
"file": "kaflow/_utils/asyncio.py",
"groundtruth_start_lineno": 26,
"repository": "gabrielmbmb-kaflow-1c5ec86",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4672"
} | {
"list": [
{
"filename": "kaflow/_utils/inspect.py",
"retrieved_chunk": " \"\"\"\n return not inspect.iscoroutinefunction(func)",
"score": 114.08288852772068
},
{
"filename": "kaflow/testclient.py",
"retrieved_chunk": " self.app._publish = intercept_publish(self... | get_running_loop() |
{
"list": [
{
"filename": "tests/test_connections.py",
"retrieved_chunk": " assert result is not None\n result = m.execute(line=\"\", cell=\"PRAGMA version\")\n assert result is not None\ndef test_cn_file():\n # Not expected to occur, but should gracefully handle\n ipshell = Interactive... | from IPython.terminal.embed import InteractiveShellEmbed
from magic_duckdb import duckdb_mode
def create_shell() -> object:
ipshell = InteractiveShellEmbed()
ipshell.run_cell("%load_ext magic_duckdb")
return ipshell
def test_types():
# Not expected to occur, but should gracefully handle
ipshell ... |
if "draw" not in e:
er = ipshell.run_cell(f"%dql -e {e} select * from range(10)")
assert er.error_in_exec is None
o = er.result
assert o is not None
| {
"context_start_lineno": 0,
"file": "tests/test_types.py",
"groundtruth_start_lineno": 30,
"repository": "iqmo-org-magic_duckdb-62e6fcc",
"right_context_start_lineno": 31,
"task_id": "project_cc_python/4701"
} | {
"list": [
{
"filename": "tests/test_connections.py",
"retrieved_chunk": " assert len(df) == 1\n assert df.at[0, \"file\"].endswith(filename)\ndef test_co_file():\n # Not expected to occur, but should gracefully handle\n ipshell = InteractiveShellEmbed()\n m = DuckDbMagic(shell=ipshell... | DuckDbMode.explain_functions: |
{
"list": [
{
"filename": "magic_duckdb/autocomplete/autocompletion_v2.py",
"retrieved_chunk": " # if ends in a period, checks to see if prefix is a tablename, and if so, returns column names\n # otherwise returns all sql phrases and tablenames\n # https://github.com/ipython/ipyth... | import duckdb
from pandas import DataFrame
import numpy as np
from magic_duckdb import magic
from magic_duckdb.autocomplete.autocompletion_v2 import DqlCustomCompleter
from types import SimpleNamespace
def test_simple_autocomplete():
with duckdb.connect() as con:
con.execute(
"CREATE TABLE IF ... |
results = [sc.text for sc in r["completions"]]
assert results is not None
assert "SELECT" in results
event = SimpleNamespace(
token="from",
full_text="%dql select * from ",
)
r = completer.line_completer(event)
results = [sc.text for sc i... | {
"context_start_lineno": 0,
"file": "tests/test_autocomplete.py",
"groundtruth_start_lineno": 44,
"repository": "iqmo-org-magic_duckdb-62e6fcc",
"right_context_start_lineno": 45,
"task_id": "project_cc_python/4699"
} | {
"list": [
{
"filename": "magic_duckdb/autocomplete/common.py",
"retrieved_chunk": "]\npragma_phrases = [\n \"PRAGMA version\",\n \"PRAGMA database_list\",\n \"PRAGMA database_size\",\n \"PRAGMA show_tables\",\n \"PRAGMA show_tables_expanded\",\n \"PRAGMA table_info('\",\n \"PRAG... | line_completer(event) |
{
"list": [
{
"filename": "jax/internal/camera_utils.py",
"retrieved_chunk": " far: float,\n xnp: types.ModuleType) -> utils.Rays:\n \"\"\"Generates a spherical camera ray batch.\"\"\"\n theta_vals = xnp.linspace(0, 2 * xnp.pi, width + 1)\n phi_vals = x... | # Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
if __name__ == '__main__':
absltest.main()
| {
"context_start_lineno": 0,
"file": "jax/tests/ref_utils_test.py",
"groundtruth_start_lineno": 82,
"repository": "ZX-Yin-ms-nerf-9009137",
"right_context_start_lineno": 83,
"task_id": "project_cc_python/4666"
} | {
"list": [
{
"filename": "jax/internal/camera_utils.py",
"retrieved_chunk": " xnp.sin(phi) * xnp.cos(theta),\n ],\n axis=-1)\n # For jax, need to specify high-precision matmul.\n matmul = math.matmul if xnp == jnp else xnp.matmul\n directions = matmul(camtoworld[:3, :... | any(jnp.isnan(de))) |
{
"list": [
{
"filename": "mmllama/datasets/prompter.py",
"retrieved_chunk": " f\"Using prompt template {template_name}: {self.template['description']}\"\n )\n def generate_prompt(\n self,\n instruction: str,\n input: Union[None, str] = None,\n ... | import argparse
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from transformers import LlamaTokenizer
from mmllama.datasets import Prompter
from mmllama.registry import MODELS
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet test... |
inputs = tokenizer(prompt, return_tensors='pt')
input_ids = inputs['input_ids'].to('cuda')
model.model.test_cfg.temperature=temperature
model.model.test_cfg.top_k=top_k
model.model.test_cfg.max_new_tokens=max_new_tokens
# TODO: beam search
with torch.no_grad():
... | {
"context_start_lineno": 0,
"file": "tools/generate.py",
"groundtruth_start_lineno": 72,
"repository": "RangiLyu-llama.mmengine-1887d56",
"right_context_start_lineno": 73,
"task_id": "project_cc_python/4703"
} | {
"list": [
{
"filename": "mmllama/datasets/prompter.py",
"retrieved_chunk": " if not template_name:\n # Enforce the default here, so the constructor can be called with '' and will not break.\n template_name = 'alpaca'\n file_name = osp.join('templates', f'{template... | generate_prompt(instruction, input) |
{
"list": [
{
"filename": "jax/internal/ref_utils.py",
"retrieved_chunk": " dot(v, n) = dot(u, n), and dot(u, u) = dot(v, v). The solution to these two\n equations is u = 2 dot(n, v) n - v.\n Args:\n viewdirs: [..., 3] array of view directions.\n normals: [..., 3] array of normal directions (... | # Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
cos_angle_reflected = jnp.sum(reflected_directions * normals, axis=-1)
np.testing.assert_allclose(
cos_angle_original, cos_angle_reflected, atol=1E-5, rtol=1E-5)
def test_spherical_harmonics(self):
"""Make sure the fast spherical harmonics are accurate."""
shape = (12, 11, 13)
# ... | {
"context_start_lineno": 0,
"file": "jax/tests/ref_utils_test.py",
"groundtruth_start_lineno": 54,
"repository": "ZX-Yin-ms-nerf-9009137",
"right_context_start_lineno": 55,
"task_id": "project_cc_python/4659"
} | {
"list": [
{
"filename": "jax/tests/stepfun_test.py",
"retrieved_chunk": " v_batch = stepfun.resample(t, tp, vp, use_avg=use_avg)\n v_indiv = []\n for i in range(t.shape[0]):\n v_indiv.append(\n jnp.array([\n stepfun.resample(t[i, j], tp[i, j], vp[i, j], use_avg=us... | sum(directions * normals, axis=-1) |
{
"list": [
{
"filename": "mmllama/models/llama.py",
"retrieved_chunk": " # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n loss = loss_fct(shift_logits, shift_labels)\n return dict(loss=loss)\n @torch.no_grad()\n def predict(self, inp... | import argparse
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from transformers import LlamaTokenizer
from mmllama.datasets import Prompter
from mmllama.registry import MODELS
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet test... |
if args.instructions is not None:
instructions = args.instructions
else:
instructions = [
'Tell me about alpacas.',
'Tell me about the president of Mexico in 2019.',
'Tell me about the king of France in 2019.',
'List all Canadian provinces in alphabetical order.... | {
"context_start_lineno": 0,
"file": "tools/generate.py",
"groundtruth_start_lineno": 83,
"repository": "RangiLyu-llama.mmengine-1887d56",
"right_context_start_lineno": 84,
"task_id": "project_cc_python/4704"
} | {
"list": [
{
"filename": "mmllama/models/llama.py",
"retrieved_chunk": " empty = torch.empty(B, T_new, dtype=input_ids.dtype, device=input_ids.device)\n empty[:, :T] = input_ids\n input_ids = empty\n max_seq_length = self.block_size\n # generate max_new_tokens token... | get_response(output) |
{
"list": [
{
"filename": "jax/tests/render_test.py",
"retrieved_chunk": " np.testing.assert_allclose(\n jax.vmap(jax.vmap(jnp.diag))(cov), cov_diag, atol=1E-5, rtol=1E-5)\n def test_rotated_conic_frustums(self):\n # Test that conic frustum Gaussians are closed under rotation.\n diag ... | # Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
phi = random.uniform(key2, shape, minval=0.0, maxval=2.0*jnp.pi)
# Convert to Cartesian coordinates.
x = jnp.sin(theta) * jnp.cos(phi)
y = jnp.sin(theta) * jnp.sin(phi)
z = jnp.cos(theta)
xyz = jnp.stack([x, y, z], axis=-1)
deg_view = 5
de = ref_utils.generate_dir_enc_fn(deg_view)(xyz... | {
"context_start_lineno": 0,
"file": "jax/tests/ref_utils_test.py",
"groundtruth_start_lineno": 67,
"repository": "ZX-Yin-ms-nerf-9009137",
"right_context_start_lineno": 68,
"task_id": "project_cc_python/4660"
} | {
"list": [
{
"filename": "jax/tests/render_test.py",
"retrieved_chunk": " i_results = []\n for i_t0, i_t1 in zip(t0, t1):\n key, rng = random.split(rng)\n i_results.append(\n conical_frustum_to_gaussian_sample(key, raydir, i_t0, i_t1, r))\n mean_gt, cov_gt = [j... | uniform(key1, shape, minval=0.0, maxval=jnp.pi) |
{
"list": [
{
"filename": "jax/tests/coord_test.py",
"retrieved_chunk": " def test_construct_ray_warps_special_reciprocal(self):\n \"\"\"Test fn=1/x against its closed form.\"\"\"\n n = 100\n rng = random.PRNGKey(0)\n key, rng = random.split(rng)\n t_near = jnp.exp(jax.random.normal(ke... | # Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
key, rng = random.split(rng)
mat1 = jax.random.normal(key, [num_dims, num_points])
sq_dist = geopoly.compute_sq_dist(mat0, mat1)
sq_dist_ref = np.zeros([num_points, num_points])
for i in range(num_points):
for j in range(num_points):
sq_dist_ref[i, j] = np.sum((mat0[:, i] - mat1[:, ... | {
"context_start_lineno": 0,
"file": "jax/tests/geopoly_test.py",
"groundtruth_start_lineno": 41,
"repository": "ZX-Yin-ms-nerf-9009137",
"right_context_start_lineno": 42,
"task_id": "project_cc_python/4650"
} | {
"list": [
{
"filename": "jax/tests/stepfun_test.py",
"retrieved_chunk": " key, rng = random.split(rng)\n d0, d1 = random.randint(key, [2], minval=10, maxval=20)\n key, rng = random.split(rng)\n t0 = jnp.sort(random.uniform(key, [d0 + 1]), axis=-1)\n key, rng = random.split(r... | random.normal(key, [num_dims, num_points]) |
{
"list": [
{
"filename": "Solver/MCMCSolver.py",
"retrieved_chunk": " self.device = torch.device('cuda')\n self.model = model.to(self.device)\n self.sampler = sampler\n def train(self,\n loader: DataLoader,\n total_epoch=2000,\n lr=1e-4,\... | from Solver import MCMCSolver
from sampler import EnergyBasedLangevinDynamicSampler
from data import get_CIFAR10_train, get_CIFAR10_test
from models import UnconditionalResNet32
import torch
from torchvision import transforms
to_img = transforms.ToPILImage()
loader = get_CIFAR10_train(batch_size=256)
model = Uncondit... |
print(model(x.cuda()).sum(), x.shape)
#
x = torch.rand(1, 3, 32, 32).cuda()
print(model(x.cuda()).sum())
x = sampler.sample(x, step=600)
print(model(x.cuda()).sum(), x.shape)
x = to_img(x[0].squeeze())
x.save('test.png')
| {
"context_start_lineno": 0,
"file": "main.py",
"groundtruth_start_lineno": 25,
"repository": "huanranchen-EnergyBasedGenerativeModel-ad5989a",
"right_context_start_lineno": 26,
"task_id": "project_cc_python/4637"
} | {
"list": [
{
"filename": "Solver/MCMCSolver.py",
"retrieved_chunk": " self.buffer = torch.rand(64, *self.sampler.img_size, device=self.device)\n optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n for epoch in range(1, total_epoch + 1):\n pbar = tqdm(loader)... | sample(x, step=600) |
{
"list": [
{
"filename": "codegen/model.py",
"retrieved_chunk": " # construct prompt\n message = (\n f\"Please complete the following code snippet.\\n```\\n{prompt.strip()}\\n```\"\n )\n config = create_chatgpt_config(\n message=message,\n ... | import ast
import os
import random
from typing import Dict, List
import openai
from evalplus.data import to_raw
from evalplus.gen import BaseGen
from evalplus.gen.util import trusted_check_exec
from evalplus.gen.util.api_request import create_chatgpt_config, request_chatgpt_engine
class ChatGPTGen(BaseGen):
def... |
seeds = self.seed_selection()
new_inputs = self.chatgpt_generate(seeds)
for new_input in new_inputs:
if hash(str(new_input)) not in self.seed_hash:
if trusted_check_exec(self.contract, [new_input], self.entry_point):
self.s... | {
"context_start_lineno": 0,
"file": "evalplus/gen/chatgpt_gen.py",
"groundtruth_start_lineno": 62,
"repository": "evalplus-evalplus-8b713cd",
"right_context_start_lineno": 63,
"task_id": "project_cc_python/4589"
} | {
"list": [
{
"filename": "codegen/model.py",
"retrieved_chunk": " model=self.model_name,\n )\n ret = request_chatgpt_engine(config)\n return self._chatgpt_parse(ret, prompt.strip())\nclass IncoderDecoder(HFTorchDecoder):\n def __init__(\n self, name: str, bat... | new_inputs) < num and self.iteration >= 0: |
{
"list": [
{
"filename": "MAESC_training_for_generated_dual_prompts_multitasks_Aspect.py",
"retrieved_chunk": " best_dev_res = None\n best_dev_test_res = None\n best_test_res = None\n # res_dev = eval_utils.eval(model, dev_loader, metric, device)\n while epoch < args.epochs:\n l... | from datetime import datetime
import numpy as np
from torch.cuda.amp import autocast
import src.model.utils as utils
import src.eval_utils as eval_utils
# from src.utils import TaskType
import torch
def pretrain(task_list,
epoch,
model,
train_loaders,
optimizer_dict... |
utils.set_lr(optimizer, liner_warm_rate * args.lr)
optimizer.zero_grad()
loss.backward()
utils.clip_gradient(optimizer, args.grad_clip)
optimizer.step() | {
"context_start_lineno": 0,
"file": "src/training_multitasks.py",
"groundtruth_start_lineno": 162,
"repository": "YangXiaocui1215-GMP-d3cb04f",
"right_context_start_lineno": 163,
"task_id": "project_cc_python/4644"
} | {
"list": [
{
"filename": "src/eval_utils_multitasks.py",
"retrieved_chunk": " metric.evaluate(aesc_infos['spans'], predict,\n aesc_infos['labels'].to(device))\n # break\n aspects_num_eval_acc = num_correct/len(loader.dataset)\n res = metric.get_metric()\n ... | liner_warmup(cur_step, t_step, args.warmup) |
{
"list": [
{
"filename": "evalplus/gen/type_mut.py",
"retrieved_chunk": " str: set(),\n }\n for x in inputs:\n self.fetch_ingredient(x)\n def seed_selection(self):\n # random for now.\n return random.choice(self.seed_pool)\n def mutate(self, see... | import random
from abc import abstractmethod
from typing import Any, List
from evalplus.gen import BaseGen
from evalplus.gen.util import trusted_check_exec
class MutateGen(BaseGen):
def __init__(self, inputs: List, signature: str, contract_code: str):
super().__init__(inputs, signature, contract_code)
... |
seed = self.seed_selection()
new_input = self.mutate(seed)
if hash(str(new_input)) not in self.seed_hash:
if trusted_check_exec(self.contract, [new_input], self.entry_point):
self.seed_pool.append(new_input)
self.seed_hash.add(... | {
"context_start_lineno": 0,
"file": "evalplus/gen/mut_gen.py",
"groundtruth_start_lineno": 21,
"repository": "evalplus-evalplus-8b713cd",
"right_context_start_lineno": 22,
"task_id": "project_cc_python/4579"
} | {
"list": [
{
"filename": "evalplus/gen/type_mut.py",
"retrieved_chunk": " str: set(),\n }\n for x in inputs:\n self.fetch_ingredient(x)\n def seed_selection(self):\n # random for now.\n return random.choice(self.seed_pool)\n def mutate(self, see... | new_inputs) < num: |
{
"list": [
{
"filename": "MAESC_training_for_generated_dual_prompts_multitasks_Aspect.py",
"retrieved_chunk": " best_dev_res = None\n best_dev_test_res = None\n best_test_res = None\n # res_dev = eval_utils.eval(model, dev_loader, metric, device)\n while epoch < args.epochs:\n l... | from datetime import datetime
import numpy as np
from torch.cuda.amp import autocast
import src.model.utils as utils
import src.eval_utils as eval_utils
# from src.utils import TaskType
import torch
def pretrain(task_list,
epoch,
model,
train_loaders,
optimizer_dict... |
optimizer.zero_grad()
loss.backward()
utils.clip_gradient(optimizer, args.grad_clip)
optimizer.step() | {
"context_start_lineno": 0,
"file": "src/training_multitasks.py",
"groundtruth_start_lineno": 163,
"repository": "YangXiaocui1215-GMP-d3cb04f",
"right_context_start_lineno": 164,
"task_id": "project_cc_python/4645"
} | {
"list": [
{
"filename": "MAESC_training_for_generated_dual_prompts_multitasks_Aspect.py",
"retrieved_chunk": " metric=metric,\n optimizer=optimizer,\n args=args,\n device=device,\n logger=logger,\n ... | set_lr(optimizer, liner_warm_rate * args.lr) |
{
"list": [
{
"filename": "evalplus/_experimental/type_mut_for_eff.py",
"retrieved_chunk": " # @dispatch(set)\n # def typed_fetch(self, seed_input: Set):\n # self._fetch_list_like(seed_input)\n # Dict\n @dispatch(dict)\n def typed_fetch(self, seed_input: Dict):\n self._fet... | import copy
import random
import string
import time
from typing import Any, Dict, List, Set, Tuple
from multipledispatch import dispatch
from evalplus.gen.mut_gen import MutateGen
from evalplus.gen.util import trusted_check_exec
MAX_MULTI_STEP_SIZE = 5
MUTATE_BOUND_SIZE = 8
NoneType = type(None)
# decorator to us... |
if num_generated % 1000 == 0:
print(
f"generated {num_generated} already with {len(self.new_inputs)} new inputs ... "
)
new_input = self.seed_selection()
# Multi-step instead of single-step
for _ in range(random.randint... | {
"context_start_lineno": 0,
"file": "evalplus/gen/type_mut.py",
"groundtruth_start_lineno": 309,
"repository": "evalplus-evalplus-8b713cd",
"right_context_start_lineno": 310,
"task_id": "project_cc_python/4584"
} | {
"list": [
{
"filename": "evalplus/_experimental/type_mut_for_eff.py",
"retrieved_chunk": " def concat(x: int, y: int):\n return x + y\n @dispatch(float, float)\n def concat(x: float, y: float):\n return x + y\n @dispatch(bool, bool)\n def concat(x: bool, y: bool):\n ... | new_inputs) < num and time.time() - start < self.timeout: |
{
"list": [
{
"filename": "MAESC_training_for_generated_dual_prompts_multitasks_Aspect.py",
"retrieved_chunk": " pad_token_id=eos_token_id,\n restricter=None)\n # model = MultiModalBartModel_AESC(bart_config, args.bart_... | from datetime import datetime
import numpy as np
from torch.cuda.amp import autocast
import src.model.utils as utils
import src.eval_utils as eval_utils
# from src.utils import TaskType
import torch
def pretrain(task_list,
epoch,
model,
train_loaders,
optimizer_dict... |
optimizer.step() | {
"context_start_lineno": 0,
"file": "src/training_multitasks.py",
"groundtruth_start_lineno": 168,
"repository": "YangXiaocui1215-GMP-d3cb04f",
"right_context_start_lineno": 169,
"task_id": "project_cc_python/4646"
} | {
"list": [
{
"filename": "MAESC_training_for_generated_dual_prompts_multitasks_Aspect.py",
"retrieved_chunk": " logger.info('Loading data...')\n collate_aesc = Collator(\n args.task,\n tokenizer,\n mlm_enabled=Fals... | clip_gradient(optimizer, args.grad_clip) |
{
"list": [
{
"filename": "evalplus/data/__init__.py",
"retrieved_chunk": " return plus\ndef get_human_eval() -> Dict[str, Dict]:\n \"\"\"Get HumanEval from OpenAI's github repo and return as a list of parsed dicts.\n Returns:\n List[Dict[str, str]]: List of dicts with keys \"prompt\",... | import ast
import os
import random
from typing import Dict, List
import openai
from evalplus.data import to_raw
from evalplus.gen import BaseGen
from evalplus.gen.util import trusted_check_exec
from evalplus.gen.util.api_request import create_chatgpt_config, request_chatgpt_engine
class ChatGPTGen(BaseGen):
def... |
@staticmethod
def _parse_ret(ret: Dict) -> List:
rets = []
output = ret["choices"][0]["message"]["content"]
if "```" in output:
for x in output.split("```")[1].splitlines():
if x.strip() == "":
continue
try:
... | {
"context_start_lineno": 0,
"file": "evalplus/gen/chatgpt_gen.py",
"groundtruth_start_lineno": 27,
"repository": "evalplus-evalplus-8b713cd",
"right_context_start_lineno": 28,
"task_id": "project_cc_python/4588"
} | {
"list": [
{
"filename": "evalplus/data/__init__.py",
"retrieved_chunk": " \"\"\"\n # Check if human eval file exists in CACHE_DIR\n human_eval_path = os.path.join(CACHE_DIR, \"HumanEval.jsonl\")\n human_eval = None\n if not os.path.exists(human_eval_path):\n # Install HumanEval... | seed_pool, k=min(len(self.seed_pool), 5)) |
{
"list": [
{
"filename": "codegen/generate.py",
"retrieved_chunk": " log += f\" (resuming from {n_existing})\"\n nsamples = args.n_samples - n_existing\n p.console.print(log)\n sidx = args.n_samples - nsamples\n while sidx < args.n_sample... | import json
import os
import pickle
from os import PathLike
from typing import List
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from evalplus.eval import estimate_pass_at_k
SMALL_SIZE = 10
MEDIUM_SIZE = 14
BIGGER_SIZE = 18
plt.rc("font", size=SMALL_SIZE) # controls default text sizes
p... |
pass_at_k_new = estimate_pass_at_k(ntotal, npass_new, k).mean() * 100
d_old.setdefault(k, []).append(pass_at_k_old)
d_new.setdefault(k, []).append(pass_at_k_new)
for nsamples in passk_old:
print("=====================================")
print(f"{nsamp... | {
"context_start_lineno": 0,
"file": "tools/viz_passrate.py",
"groundtruth_start_lineno": 59,
"repository": "evalplus-evalplus-8b713cd",
"right_context_start_lineno": 60,
"task_id": "project_cc_python/4593"
} | {
"list": [
{
"filename": "codegen/generate.py",
"retrieved_chunk": " num_samples=args.n_samples - sidx,\n )\n for impl in outputs:\n try:\n with open(\n os.path.join(workdir, p_name, ... | mean() * 100 |
{
"list": [
{
"filename": "nn/models/epd.py",
"retrieved_chunk": " def predict(self, features: clrs.Features) -> Result:\n self.net_.eval()\n raw_preds, aux = self.net_(features)\n preds = decoders.postprocess(raw_preds, self._spec)\n return preds, (raw_preds, aux)\n ... |
import clrs
import torch
from nn import losses as loss
from nn.models.impl import _dimensions, _bfs_op_mask, _expand_to, \
_get_fts, _hints_i, _own_hints_i, _reset_hints
from nn.models.impl import decoders
from nn.models.epd import EncodeProcessDecode_Impl as Net
from random import random
from typing import Calla... |
return preds, (raw_preds, aux)
@torch.no_grad()
def verbose_loss(self, feedback: _Feedback, preds, aux_preds):
losses = {}
total_loss = 0
n_hints = 0
for truth in feedback.features.hints:
if self.no_feats(truth.name):
continue
n_... | {
"context_start_lineno": 0,
"file": "nn/models/mf_net.py",
"groundtruth_start_lineno": 105,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 106,
"task_id": "project_cc_python/4716"
} | {
"list": [
{
"filename": "nn/models/mf_net_pipeline.py",
"retrieved_chunk": " raw_preds, aux = self.net_(features)\n preds = decoders.postprocess(raw_preds, self._spec)\n return preds, (raw_preds, aux)\n @torch.no_grad()\n def verbose_loss(self, feedback: _Feedback, preds, ... | postprocess(raw_preds, self.spec) |
{
"list": [
{
"filename": "nn/models/mf_net_pipeline.py",
"retrieved_chunk": " dummy_trajectory,\n num_hidden,\n encode_hints,\n decode_hints,\n processor,\n ... |
import clrs
import torch
from nn import losses as loss
from nn.models.impl import _dimensions, _bfs_op_mask, _expand_to, \
_get_fts, _hints_i, _own_hints_i, _reset_hints
from nn.models.impl import decoders
from nn.models.epd import EncodeProcessDecode_Impl as Net
from random import random
from typing import Calla... |
self.is_annealing_enabled = annealing
self.annealing_state = 0
self.device = device
self.spec = spec
self.encode_hints = encode_hints
self.to(device)
self.hiddens = None
def op(self, trajectories, h_bfs, adj, is_bfs_op):
_, h_bfs, h_preds_bfs = sel... | {
"context_start_lineno": 0,
"file": "nn/models/mf_net.py",
"groundtruth_start_lineno": 166,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 167,
"task_id": "project_cc_python/4717"
} | {
"list": [
{
"filename": "nn/models/mf_net_pipeline.py",
"retrieved_chunk": " dummy_trajectory,\n num_hidden,\n encode_hints,\n decode_hints,\n processor,\n ... | encoders['c_h'] |
{
"list": [
{
"filename": "nn/models/mf_net.py",
"retrieved_chunk": " self.no_feats = lambda x: x in no_feats or x.startswith('__') # noqa\n self.bfs_net = Net(spec, dummy_trajectory, num_hidden,\n encode_hints, decode_hints,\n process... | import clrs
import torch
from nn import losses as loss
from nn.models.impl import _dimensions, _bfs_op_mask, _expand_to, \
_get_fts, _hints_i, _own_hints_i, _reset_hints
from nn.models.impl import decoders
from nn.models.epd import EncodeProcessDecode_Impl as Net
from random import random
from typing import Calla... |
self.mincut_net = Net(
spec,
dummy_trajectory,
num_hidden,
encode_hints=False,
decode_hints=False,
processor=processor,
aggregator=aggregator,
max_steps=c.data.shape[2] + 1,
... | {
"context_start_lineno": 0,
"file": "nn/models/mf_net_pipeline.py",
"groundtruth_start_lineno": 174,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 175,
"task_id": "project_cc_python/4728"
} | {
"list": [
{
"filename": "nn/models/mf_net.py",
"retrieved_chunk": " device=device)\n del self.bfs_net.encoders['c_h']\n self.is_annealing_enabled = annealing\n self.annealing_state = 0\n self.device = device\n self.spec = spec\n se... | data.shape[2]) |
{
"list": [
{
"filename": "nn/models/epd.py",
"retrieved_chunk": " encode_hints=encode_hints,\n decode_hints=decode_hints,\n processor=processor,\n ... | import clrs
import os
import torch
import typer
from config.hyperparameters import HP_SPACE
from functools import partial
from nn.models import EncodeProcessDecode, MF_Net, MF_NetPipeline
from pathlib import Path
from statistics import mean, stdev
from utils.data import load_dataset
from utils.experiments import evalu... |
preds, aux = model.predict(features)
for key in preds:
preds[key].data = preds[key].data.cpu()
metrics = {}
for truth in feedback.outputs:
type_ = preds[truth.name].type_
y_pred = preds[truth.name].data.numpy()
y_true = truth.data.numpy(... | {
"context_start_lineno": 0,
"file": "run.py",
"groundtruth_start_lineno": 202,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 203,
"task_id": "project_cc_python/4705"
} | {
"list": [
{
"filename": "nn/models/epd.py",
"retrieved_chunk": " self.no_feats = lambda x: x in no_feats or x.startswith('__')\n self.encode_hints = encode_hints\n self.decode_hints = decode_hints\n def dump_model(self, path):\n torch.save(self.net_.state_dict(), path)... | restore_model(test_path / f'trial_{i}' / 'model_0.pth', 'cuda') |
{
"list": [
{
"filename": "nn/models/epd.py",
"retrieved_chunk": " encode_hints=encode_hints,\n decode_hints=decode_hints,\n processor=processor,\n ... | import clrs
import torch
from nn import losses as loss
from nn.models.impl import _dimensions, _bfs_op_mask, _expand_to, \
_get_fts, _hints_i, _own_hints_i, _reset_hints
from nn.models.impl import decoders
from nn.models.epd import EncodeProcessDecode_Impl as Net
from random import random
from typing import Calla... |
del self.flow_net.hint_decoders['c_h']
del self.bfs_net.hint_decoders['c_h']
del self.mincut_net.decoders['f']
self.is_annealing_enabled = annealing
self.annealing_state = 0
self.device = device
self.spec = spec
self.encode_hints = encode_hin... | {
"context_start_lineno": 0,
"file": "nn/models/mf_net_pipeline.py",
"groundtruth_start_lineno": 187,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 188,
"task_id": "project_cc_python/4729"
} | {
"list": [
{
"filename": "nn/models/mf_net.py",
"retrieved_chunk": " device=device)\n del self.bfs_net.encoders['c_h']\n self.is_annealing_enabled = annealing\n self.annealing_state = 0\n self.device = device\n self.spec = spec\n se... | decoders['c'] |
{
"list": [
{
"filename": "nn/models/mf_net.py",
"retrieved_chunk": " self.no_feats = lambda x: x in no_feats or x.startswith('__') # noqa\n self.bfs_net = Net(spec, dummy_trajectory, num_hidden,\n encode_hints, decode_hints,\n process... | import clrs
import torch
from nn import losses as loss
from nn.models.impl import _dimensions, _bfs_op_mask, _expand_to, \
_get_fts, _hints_i, _own_hints_i, _reset_hints
from nn.models.impl import decoders
from nn.models.epd import EncodeProcessDecode_Impl as Net
from random import random
from typing import Calla... |
del self.bfs_net.hint_decoders['c_h']
del self.mincut_net.decoders['f']
self.is_annealing_enabled = annealing
self.annealing_state = 0
self.device = device
self.spec = spec
self.encode_hints = encode_hints
self.to(device)
def op(self, trajec... | {
"context_start_lineno": 0,
"file": "nn/models/mf_net_pipeline.py",
"groundtruth_start_lineno": 188,
"repository": "danilonumeroso-dar-64e8631",
"right_context_start_lineno": 189,
"task_id": "project_cc_python/4730"
} | {
"list": [
{
"filename": "nn/models/mf_net.py",
"retrieved_chunk": " device=device)\n del self.bfs_net.encoders['c_h']\n self.is_annealing_enabled = annealing\n self.annealing_state = 0\n self.device = device\n self.spec = spec\n se... | hint_decoders['c_h'] |
{
"list": [
{
"filename": "code_genie/pipeline.py",
"retrieved_chunk": " # there should be atleast 1 sink\n if all(step.sink is None for step in v):\n raise ValueError(\"atleast one of the steps should have a sink; none found\")\n return v\n def _get_filepath(self, f... | import os
import pandas as pd
import pytest
from code_genie import Genie
from code_genie.io import IntArg, StringArg
from code_genie.io.local import CsvToDataFrameSource, DataFrameToCsvSink
from code_genie.pipeline import GeniePipeline, PipelineStep
@pytest.fixture(scope="module")
def pipeline_cache_dir() -> str:
... |
assert pipeline_imported == pipeline
# now we will run this pipeline with a new df and multiplier and check results
sink_path = os.path.join(pipeline_cache_dir, "data", "df_eval_result.csv")
pipeline_imported.run({source_path_key: df_eval_path, multiplier_key: 5, sink_path_key: sink_path})
# read... | {
"context_start_lineno": 0,
"file": "tests/test_pipeline.py",
"groundtruth_start_lineno": 72,
"repository": "thismlguy-code-genie-58f789c",
"right_context_start_lineno": 73,
"task_id": "project_cc_python/4684"
} | {
"list": [
{
"filename": "code_genie/pipeline.py",
"retrieved_chunk": " steps: List[PipelineStep]\n \"\"\"List of steps in the pipeline\"\"\"\n def add(self, step: PipelineStep):\n \"\"\"Add a step to the pipeline\"\"\"\n return self.copy(update={\"steps\": self.steps + [step]}... | load(os.path.join(pipeline_cache_dir, "test-pipe.json")) |
{
"list": [
{
"filename": "code_genie/pipeline.py",
"retrieved_chunk": " # there should be atleast 1 sink\n if all(step.sink is None for step in v):\n raise ValueError(\"atleast one of the steps should have a sink; none found\")\n return v\n def _get_filepath(self, f... | import os
import pandas as pd
import pytest
from code_genie import Genie
from code_genie.io import IntArg, StringArg
from code_genie.io.local import CsvToDataFrameSource, DataFrameToCsvSink
from code_genie.pipeline import GeniePipeline, PipelineStep
@pytest.fixture(scope="module")
def pipeline_cache_dir() -> str:
... |
pipeline_imported = GeniePipeline.load(os.path.join(pipeline_cache_dir, "test-pipe.json"))
assert pipeline_imported == pipeline
# now we will run this pipeline with a new df and multiplier and check results
sink_path = os.path.join(pipeline_cache_dir, "data", "df_eval_result.csv")
pipeline_import... | {
"context_start_lineno": 0,
"file": "tests/test_pipeline.py",
"groundtruth_start_lineno": 70,
"repository": "thismlguy-code-genie-58f789c",
"right_context_start_lineno": 71,
"task_id": "project_cc_python/4683"
} | {
"list": [
{
"filename": "code_genie/pipeline.py",
"retrieved_chunk": " steps: List[PipelineStep]\n \"\"\"List of steps in the pipeline\"\"\"\n def add(self, step: PipelineStep):\n \"\"\"Add a step to the pipeline\"\"\"\n return self.copy(update={\"steps\": self.steps + [step]}... | export("test-pipe.json") |
{
"list": [
{
"filename": "tests/test_genie.py",
"retrieved_chunk": " genie = Genie(data=df, client=client)\n # call the method\n assert genie.plz(instructions=\"sum of mean of x and y\").result == 7\n assert set(genie.plz(instructions=\"distinct values of x\").result) == {1, 2, 3}\ndef te... | import os
import pandas as pd
import pytest
from code_genie import Genie
from code_genie.io import IntArg, StringArg
from code_genie.io.local import CsvToDataFrameSource, DataFrameToCsvSink
from code_genie.pipeline import GeniePipeline, PipelineStep
@pytest.fixture(scope="module")
def pipeline_cache_dir() -> str:
... |
genie = Genie(data=gr_grp.result, cache_dir=pipeline_cache_dir, client=client)
gr_mul = genie.plz("multiply values of x by multiplier", additional_inputs={"multiplier": multiplier})
result_values = gr_mul.result.reset_index().set_index("y")["x"].to_dict()
assert result_values == {"a": 3.0 * multiplier,... | {
"context_start_lineno": 0,
"file": "tests/test_pipeline.py",
"groundtruth_start_lineno": 45,
"repository": "thismlguy-code-genie-58f789c",
"right_context_start_lineno": 46,
"task_id": "project_cc_python/4682"
} | {
"list": [
{
"filename": "tests/test_genie.py",
"retrieved_chunk": " assert genie.plz(instructions=\"add all inputs\").result == 60\n assert genie.plz(instructions=\"multiply all inputs\").result == 6000\ndef test_math_additional_inputs(client):\n # use genie to get a method to add 2 numbers... | plz("create a df with mean values of x grouped by y") |
{
"list": [
{
"filename": "tests/test_pipeline.py",
"retrieved_chunk": " multiplier = 2\n gr_grp = genie.plz(\"create a df with mean values of x grouped by y\")\n genie = Genie(data=gr_grp.result, cache_dir=pipeline_cache_dir, client=client)\n gr_mul = genie.plz(\"multiply values of x by m... | import pandas as pd
import pytest
from code_genie import Genie
@pytest.fixture(scope="session")
def df():
return pd.DataFrame({"x": [1, 2, 3, 1, 2, 3], "y": [4, 5, 6, 4, 5, 6]})
def test_math(client):
# use genie to get a method to add 2 numbers
genie = Genie(data=[10, 20, 30], client=client)
# ca... |
def test_cache(client):
# use genie to get a method to add 2 numbers
genie = Genie(data=[1, 2, 3, 4], client=client)
# call the method
gr_1 = genie.plz(instructions="add all elements of input")
assert gr_1.result == 10
gr_2 = genie.plz(instructions="add all elements of input")
assert gr... | {
"context_start_lineno": 0,
"file": "tests/test_genie.py",
"groundtruth_start_lineno": 46,
"repository": "thismlguy-code-genie-58f789c",
"right_context_start_lineno": 47,
"task_id": "project_cc_python/4686"
} | {
"list": [
{
"filename": "tests/test_pipeline.py",
"retrieved_chunk": " source = CsvToDataFrameSource(path=StringArg(name=source_path_key))\n sink = DataFrameToCsvSink(path=StringArg(name=sink_path_key))\n steps = [\n PipelineStep(\n genie_result=gr_grp,\n data=s... | custom(code=code).result) == {1, 2, 3} |
{
"list": [
{
"filename": "app.py",
"retrieved_chunk": " for download_file in download:\n # Get file name from lookup\n zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))\n ... | import argparse
import os
import pathlib
from urllib.parse import urlparse
import warnings
import numpy as np
import threading
import torch
from app import VadOptions, WhisperTranscriber
from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.download import download_url... |
taskArgs = dict(args)
taskArgs["task"] = model_task["task"]
result = transcriber.transcribe_file(model, source_path, temperature=temperature, vadOptions=vadOptions, **taskArgs)
transcriber.write_result(result, source_name, output_dir, hig... | {
"context_start_lineno": 0,
"file": "cli.py",
"groundtruth_start_lineno": 198,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 199,
"task_id": "project_cc_python/4745"
} | {
"list": [
{
"filename": "app.py",
"retrieved_chunk": " print(\"Deleting source file \" + source.source_path)\n try:\n os.remove(source.source_path)\n except Exception as e:\n # ... | from_string(vad_initial_prompt_mode)) |
{
"list": [
{
"filename": "app.py",
"retrieved_chunk": " # Set default initial prompt mode\n if (initial_prompt_mode is None):\n initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT\n if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or \... | from src.config import VadInitialPromptMode
from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
class PrependPromptStrategy(AbstractPromptStrategy):
"""
A simple prompt strategy that prepends a single prompt to all segments of audio, or prepends the prompt to the first segment of audio.
"... |
raise ValueError(f"Unsupported initial prompt mode {initial_prompt_mode}")
def get_segment_prompt(self, segment_index: int, whisper_prompt: str, detected_language: str) -> str:
if (self.initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS):
return self._concat_prompt(se... | {
"context_start_lineno": 0,
"file": "src/prompts/prependPromptStrategy.py",
"groundtruth_start_lineno": 21,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 22,
"task_id": "project_cc_python/4759"
} | {
"list": [
{
"filename": "app.py",
"retrieved_chunk": " else:\n raise ValueError(\"Invalid vadInitialPromptMode: \" + initial_prompt_mode)\n # Callable for processing an audio file\n whisperCallable = model.create_callback(language, task, prompt_strategy=prompt_strateg... | PREPREND_FIRST_SEGMENT]: |
{
"list": [
{
"filename": "app.py",
"retrieved_chunk": " # Set default initial prompt mode\n if (initial_prompt_mode is None):\n initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT\n if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or \... | from src.config import VadInitialPromptMode
from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
class PrependPromptStrategy(AbstractPromptStrategy):
"""
A simple prompt strategy that prepends a single prompt to all segments of audio, or prepends the prompt to the first segment of audio.
"... |
elif (self.initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
return self._concat_prompt(self.initial_prompt, whisper_prompt) if segment_index == 0 else whisper_prompt
else:
raise ValueError(f"Unknown initial prompt mode {self.initial_prompt_mode}") | {
"context_start_lineno": 0,
"file": "src/prompts/prependPromptStrategy.py",
"groundtruth_start_lineno": 26,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4760"
} | {
"list": [
{
"filename": "app.py",
"retrieved_chunk": " else:\n raise ValueError(\"Invalid vadInitialPromptMode: \" + initial_prompt_mode)\n # Callable for processing an audio file\n whisperCallable = model.create_callback(language, task, prompt_strategy=prompt_strateg... | _concat_prompt(self.initial_prompt, whisper_prompt) |
{
"list": [
{
"filename": "src/prompts/prependPromptStrategy.py",
"retrieved_chunk": " raise ValueError(f\"Unsupported initial prompt mode {initial_prompt_mode}\")\n def get_segment_prompt(self, segment_index: int, whisper_prompt: str, detected_language: str) -> str:\n if (self.in... | import json
from typing import Dict
from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
class JsonPromptSegment():
def __init__(self, segment_index: int, prompt: str, format_prompt: bool = False):
self.prompt = prompt
self.segment_index = segment_index
self.format_prompt ... | {
"context_start_lineno": 0,
"file": "src/prompts/jsonPromptStrategy.py",
"groundtruth_start_lineno": 48,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 49,
"task_id": "project_cc_python/4761"
} | {
"list": [
{
"filename": "src/prompts/prependPromptStrategy.py",
"retrieved_chunk": " raise ValueError(f\"Unsupported initial prompt mode {initial_prompt_mode}\")\n def get_segment_prompt(self, segment_index: int, whisper_prompt: str, detected_language: str) -> str:\n if (self.in... | _concat_prompt(prompt.prompt, whisper_prompt) | |
{
"list": [
{
"filename": "src/whisper/whisperFactory.py",
"retrieved_chunk": " from src.whisper.whisperContainer import WhisperContainer\n return WhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)\n ... | import argparse
import os
import pathlib
from urllib.parse import urlparse
import warnings
import numpy as np
import threading
import torch
from app import VadOptions, WhisperTranscriber
from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.download import download_url... |
transcriber.set_auto_parallel(auto_parallel)
if (transcriber._has_parallel_devices()):
print("Using parallel devices:", transcriber.parallel_device_list)
for audio_path in args.pop("audio"):
sources = []
# Detect URL and download the audio
if (uri_validator(audio_path)):
... | {
"context_start_lineno": 0,
"file": "cli.py",
"groundtruth_start_lineno": 173,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 174,
"task_id": "project_cc_python/4741"
} | {
"list": [
{
"filename": "src/whisper/whisperFactory.py",
"retrieved_chunk": " from src.whisper.whisperContainer import WhisperContainer\n return WhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)\n ... | set_parallel_devices(args.pop("vad_parallel_devices")) |
{
"list": [
{
"filename": "app.py",
"retrieved_chunk": " # Set default initial prompt mode\n if (initial_prompt_mode is None):\n initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT\n if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or \... | from src.config import VadInitialPromptMode
from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
class PrependPromptStrategy(AbstractPromptStrategy):
"""
A simple prompt strategy that prepends a single prompt to all segments of audio, or prepends the prompt to the first segment of audio.
"... |
raise ValueError(f"Unsupported initial prompt mode {initial_prompt_mode}")
def get_segment_prompt(self, segment_index: int, whisper_prompt: str, detected_language: str) -> str:
if (self.initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS):
return self._concat_prompt(se... | {
"context_start_lineno": 0,
"file": "src/prompts/prependPromptStrategy.py",
"groundtruth_start_lineno": 21,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 22,
"task_id": "project_cc_python/4758"
} | {
"list": [
{
"filename": "app.py",
"retrieved_chunk": " else:\n raise ValueError(\"Invalid vadInitialPromptMode: \" + initial_prompt_mode)\n # Callable for processing an audio file\n whisperCallable = model.create_callback(language, task, prompt_strategy=prompt_strateg... | PREPEND_ALL_SEGMENTS, VadInitialPromptMode.PREPREND_FIRST_SEGMENT]: |
{
"list": [
{
"filename": "src/whisper/whisperContainer.py",
"retrieved_chunk": " if model.name == self.model_name:\n return model\n return None\n def _create_model(self):\n print(\"Loading whisper model \" + self.model_name)\n model_config = self._get... | import os
from typing import List, Union
from faster_whisper import WhisperModel, download_model
from src.config import ModelConfig, VadInitialPromptMode
from src.hooks.progressListener import ProgressListener
from src.languages import get_language_from_name
from src.modelCache import ModelCache
from src.prompts.abstr... |
model_config = self._get_model_config()
model_url = model_config.url
if model_config.type == "whisper":
if model_url not in ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]:
raise Exception("FasterWhisperContainer does not yet support Whisper mod... | {
"context_start_lineno": 0,
"file": "src/whisper/fasterWhisperContainer.py",
"groundtruth_start_lineno": 40,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 41,
"task_id": "project_cc_python/4755"
} | {
"list": [
{
"filename": "src/whisper/whisperContainer.py",
"retrieved_chunk": " prompt_strategy: AbstractPromptStrategy = None,\n **decodeOptions: dict) -> AbstractWhisperCallback:\n \"\"\"\n Create a WhisperCallback object that can be used... | device)) |
{
"list": [
{
"filename": "src/config.py",
"retrieved_chunk": " self.device = device\n self.verbose = verbose\n self.task = task\n self.language = language\n self.vad_initial_prompt_mode = vad_initial_prompt_mode\n self.vad_merge_window = vad_merge_window\n ... | import argparse
import os
import pathlib
from urllib.parse import urlparse
import warnings
import numpy as np
import threading
import torch
from app import VadOptions, WhisperTranscriber
from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.download import download_url... |
transcriber.write_result(result, source_name, output_dir, highlight_words, extras="_" + model.model_name + "_" + taskArgs["task"])
transcriber.close()
def uri_validator(x):
try:
result = urlparse(x)
return all([result.scheme, result.netloc])
except:... | {
"context_start_lineno": 0,
"file": "cli.py",
"groundtruth_start_lineno": 201,
"repository": "Cerlancism-faster-whisper-webui-884f5fd",
"right_context_start_lineno": 202,
"task_id": "project_cc_python/4746"
} | {
"list": [
{
"filename": "src/config.py",
"retrieved_chunk": " self.best_of = best_of\n self.beam_size = beam_size\n self.patience = patience\n self.length_penalty = length_penalty\n self.suppress_tokens = suppress_tokens\n self.initial_prompt = initial_promp... | transcribe_file(model, source_path, temperature=temperature, vadOptions=vadOptions, **taskArgs) |
{
"list": [
{
"filename": "exps/main.py",
"retrieved_chunk": " args.batch_size,\n workers=args.workers,\n input_size=args.input_size,\n nclass=args.nclass,\n holdout=\"train\" if holdout else None,\n timm_aug=args.aug,\n )\n if holdout:\n holdout_... | import os
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import utils.datasets as datasets
from timm.data import create_transform
def get_train_loader(
data_path,
batch_size,
workers=5,
... |
if torch.distributed.is_initialized():
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
if holdout == "val":
shfl = False
else:
shfl = train_sampler is None
train_loader = torch.utils.data.DataLoader(
... | {
"context_start_lineno": 0,
"file": "utils/loaders.py",
"groundtruth_start_lineno": 59,
"repository": "snu-mllab-Efficient-CNN-Depth-Compression-2828ae8",
"right_context_start_lineno": 60,
"task_id": "project_cc_python/4767"
} | {
"list": [
{
"filename": "utils/datasets.py",
"retrieved_chunk": " and check if the file is a valid file (used to check of corrupt files)\n Attributes:\n classes (list): List of the class names sorted alphabetically.\n class_to_idx (dict): Dict with items (class_name, cla... | ImageFolder(traindir, aug, nclass=nclass, holdout=holdout) |
{
"list": [
{
"filename": "rayen/utils.py",
"retrieved_chunk": "# Everything in Pytorch\ndef powerIteration(A, v, tol=1e-5, max_iter=100000, eps=1e-12, check_freq=2):\n\t\tn_iter = 0\n\t\tv = torch.nn.functional.normalize(v)\n\t\twhile n_iter < max_iter:\n\t\t\t\tn_iter += 1\n\t\t\t\tu = torch.nn.func... | # Import packages.
import cvxpy as cp
import numpy as np
from numpy import linalg as LA
import utils
import torch
import scipy
A=torch.Tensor([
[[2.0, -12.0],
[1.0, -5.0]],
[[-7.0, 0.0],
[0.0, 5.0]],
[[-2.0, 0.0],
[0... |
L, V = torch.linalg.eig(A)
print(L)
print(f"Found lambda={lamb}")
exit()
dim=3
for trial in range(200):
# Generate a random SDP.
tmp = np.random.rand(dim, dim)
# tmp = np.random.randint(0, 20, size=(dim, dim))
H = np.dot(tmp, tmp.transpose()) + np.eye(dim) #H is psd by construction
tmp = np.... | {
"context_start_lineno": 0,
"file": "examples/other/testing_sdp.py",
"groundtruth_start_lineno": 24,
"repository": "leggedrobotics-rayen-2ac7086",
"right_context_start_lineno": 25,
"task_id": "project_cc_python/4802"
} | {
"list": [
{
"filename": "rayen/utils.py",
"retrieved_chunk": "\t\t\t\t\t\t\t\tv = u\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\tv = u\n\t\telse:\n\t\t\t\tprint(f\"distance={distance}\")\n\t\t\t\tprint('Power iteration did not converge')\n\t\tlamb = torch.transpose(v,1,2)@A@v #torch.dot(v, torch.mv(A, v))\n\t\t... | findLargestEigenvalue(A, guess_v) |
{
"list": [
{
"filename": "ardilla/schemas.py",
"retrieved_chunk": " pk = field.name\n fields.append(field_schema[\"schema\"])\n constrains.append(field_schema[\"constraint\"]) if field_schema[\n \"constraint\"\n ] else None\n schema = (\n f\"CREATE... | import sqlite3
from pathlib import Path
from datetime import datetime
from ardilla import Model, Field
from ardilla.errors import ModelIntegrityError
from pydantic import Json
def test_default_tablename():
class Foo(Model):
id: int
assert Foo.__tablename__ == "foo"
def test_field_pk():
cla... |
def test_pk():
assert User.__pk__ == "id"
def test_double_pks():
try:
class Book(Model):
id: int = Field(pk=True)
name: str = Field(pk=True)
except Exception as e:
assert isinstance(e, ModelIntegrityError) | {
"context_start_lineno": 0,
"file": "tests/test_models.py",
"groundtruth_start_lineno": 82,
"repository": "chrisdewa-ardilla-312a373",
"right_context_start_lineno": 83,
"task_id": "project_cc_python/4822"
} | {
"list": [
{
"filename": "ardilla/schemas.py",
"retrieved_chunk": " return schema\ndef get_pk(schema: str) -> Optional[str]:\n \"\"\"Gets the primary key field name from the passed schema\n Args:\n schema (str): table schema\n Returns:\n Optional[str]: the name of the primar... | __schema__.strip() == schema.strip() |
{
"list": [
{
"filename": "ardilla/schemas.py",
"retrieved_chunk": "SQLFieldType = Union[int, float, str, bool, datetime, bytes, date, time]\nFIELD_MAPPING: dict[type, str] = {\n int: \"INTEGER\",\n float: \"REAL\",\n str: \"TEXT\",\n bool: \"INTEGER\",\n datetime: \"DATETIME\",\n by... | from typing import Annotated
from fastapi import FastAPI, Depends, status, HTTPException
from pydantic import BaseModel, Field
from ardilla import Model
from ardilla.asyncio import Engine
from ardilla.errors import QueryExecutionError
app = FastAPI(docs_url="/") # set the docs to index for easier access
class Ite... |
await engine.crud(Item) # cruds are cached, calling this here means
# we don't lose instantiating it elsewhere
@app.on_event("shutdown")
async def on_shutdown_event():
await engine.close()
async def get_item_by_id(id_: int) -> Item:
"""Returns the item with the specified id
... | {
"context_start_lineno": 0,
"file": "examples/fastapi_app.py",
"groundtruth_start_lineno": 30,
"repository": "chrisdewa-ardilla-312a373",
"right_context_start_lineno": 31,
"task_id": "project_cc_python/4825"
} | {
"list": [
{
"filename": "ardilla/models.py",
"retrieved_chunk": " __schema__: str # best effort will be made if it's missing\n # there's no support for constrains or foreign fields yet but you can\n # define your own schema to support them\n def __init_subclass__(cls, **kws) -> None:\n ... | connect() |
{
"list": [
{
"filename": "tests/test_async.py",
"retrieved_chunk": " await crud.save_many(*users)\n to_delete = users[:-1]\n await crud.delete_many(*to_delete)\n assert await crud.count() == 1, \"Delete many didn't delete the correct amount of users\"\n@pytest.mark.asyncio... | from typing import Annotated
from fastapi import FastAPI, Depends, status, HTTPException
from pydantic import BaseModel, Field
from ardilla import Model
from ardilla.asyncio import Engine
from ardilla.errors import QueryExecutionError
app = FastAPI(docs_url="/") # set the docs to index for easier access
class Ite... |
# we don't lose instantiating it elsewhere
@app.on_event("shutdown")
async def on_shutdown_event():
await engine.close()
async def get_item_by_id(id_: int) -> Item:
"""Returns the item with the specified id
Args:
id_ (int): the id of the item to lookup
Raise... | {
"context_start_lineno": 0,
"file": "examples/fastapi_app.py",
"groundtruth_start_lineno": 31,
"repository": "chrisdewa-ardilla-312a373",
"right_context_start_lineno": 32,
"task_id": "project_cc_python/4826"
} | {
"list": [
{
"filename": "tests/test_async.py",
"retrieved_chunk": " await engine.close()\n await crud.insert(name='moni')\n except Exception as e:\n assert isinstance(e, DisconnectedEngine), f'Wrong exception raised'\n finally:\n await engine.close()\n unlink... | crud(Item) # cruds are cached, calling this here means |
{
"list": [
{
"filename": "deps/volume-rendering-jax/src/volrendjax/morton3d/lowering.py",
"retrieved_chunk": " ctx: mlir.LoweringRule,\n # input array\n idcs: ir.Value,\n):\n length, = ir.RankedTensorType(idcs.type).shape\n opaque = volrendutils_cuda.make_morton3d_descriptor(length)\n ... | from jax.interpreters import mlir
from jax.interpreters.mlir import ir
from .. import volrendutils_cuda
try:
from jaxlib.mhlo_helpers import custom_call
except ModuleNotFoundError:
# A more recent jaxlib would have `hlo_helpers` instead of `mhlo_helpers`
# <https://github.com/google/jax/commit/b8ae8e3fa10... |
shapes = {
"in.density_threshold": (n_bits,),
"in.density_grid": (n_bits,),
"out.occupied_mask": (n_bits,),
"out.occupancy_bitfield": (n_bytes,),
}
return custom_call(
call_target_name="pack_density_into_bits",
out_types = [
ir.RankedTensorType... | {
"context_start_lineno": 0,
"file": "deps/volume-rendering-jax/src/volrendjax/packbits/lowering.py",
"groundtruth_start_lineno": 28,
"repository": "blurgyy-jaxngp-adbe49e",
"right_context_start_lineno": 29,
"task_id": "project_cc_python/4680"
} | {
"list": [
{
"filename": "deps/volume-rendering-jax/src/volrendjax/morton3d/lowering.py",
"retrieved_chunk": " \"in.xyzs\": (length, 3),\n \"out.idcs\": (length,),\n }\n return [custom_call(\n call_target_name=\"morton3d\",\n out_types=[\n ir.RankedTensorT... | make_packbits_descriptor(n_bytes) |
{
"list": [
{
"filename": "deps/volume-rendering-jax/src/volrendjax/packbits/lowering.py",
"retrieved_chunk": "def default_layouts(*shapes):\n return [range(len(shape) - 1, -1, -1) for shape in shapes]\ndef packbits_lowering_rule(\n ctx: mlir.LoweringRule,\n # input array\n density_thresho... | from jax.interpreters import mlir
from jax.interpreters.mlir import ir
from .. import volrendutils_cuda
try:
from jaxlib.mhlo_helpers import custom_call
except ModuleNotFoundError:
# A more recent jaxlib would have `hlo_helpers` instead of `mhlo_helpers`
# <https://github.com/google/jax/commit/b8ae8e3fa10... |
shapes = {
"in.xyzs": (length, 3),
"out.idcs": (length,),
}
return [custom_call(
call_target_name="morton3d",
out_types=[
ir.RankedTensorType.get(shapes["out.idcs"], ir.IntegerType.get_unsigned(32)),
],
operands=[
xyzs,
],
... | {
"context_start_lineno": 0,
"file": "deps/volume-rendering-jax/src/volrendjax/morton3d/lowering.py",
"groundtruth_start_lineno": 26,
"repository": "blurgyy-jaxngp-adbe49e",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4679"
} | {
"list": [
{
"filename": "deps/volume-rendering-jax/src/volrendjax/packbits/lowering.py",
"retrieved_chunk": " opaque = volrendutils_cuda.make_packbits_descriptor(n_bytes)\n shapes = {\n \"in.density_threshold\": (n_bits,),\n \"in.density_grid\": (n_bits,),\n \"out.occupied... | make_morton3d_descriptor(length) |
{
"list": [
{
"filename": "ardilla/migration.py",
"retrieved_chunk": " cols = ', '.join(name for name in new.__fields__)\n script = f'''\n \\rALTER TABLE {tablename} RENAME TO _{tablename};\n \\r\n \\r{new_table_schema}\n \\r\n \\rINSERT INTO {tablename... | import sqlite3
from pathlib import Path
from datetime import datetime
from ardilla import Model, Field
from ardilla.errors import ModelIntegrityError
from pydantic import Json
def test_default_tablename():
class Foo(Model):
id: int
assert Foo.__tablename__ == "foo"
def test_field_pk():
cla... |
def test_complex_schema_works():
try:
db = Path(__file__).parent / 'db.sqlite3'
db.unlink(missing_ok=True)
con = sqlite3.connect(db)
con.execute(Complex.__schema__)
con.commit()
finally:
con.close()
db.unlink(missing_ok=True)
class User(Model):
id... | {
"context_start_lineno": 0,
"file": "tests/test_models.py",
"groundtruth_start_lineno": 53,
"repository": "chrisdewa-ardilla-312a373",
"right_context_start_lineno": 54,
"task_id": "project_cc_python/4821"
} | {
"list": [
{
"filename": "ardilla/migration.py",
"retrieved_chunk": " \\rDROP TABLE _{tablename};\n \\r'''\n scripts.append(script)\n return \"\\n\\n\".join(scripts)",
"score": 54.183045649211245
},
{
"filename": "ardilla/schemas.py",
"retrieved_chunk... | __schema__.strip() == complex_schema.strip() |
{
"list": [
{
"filename": "deps/volume-rendering-jax/src/volrendjax/marching/lowering.py",
"retrieved_chunk": " occupancy_bitfield: ir.Value,\n # static args\n total_samples: int, # int\n diagonal_n_steps: int, # int\n K: int, # int\n G: int, # int\n bound: float, # float\n ... | from jax.interpreters import mlir
from jax.interpreters.mlir import ir
from .. import volrendutils_cuda
try:
from jaxlib.mhlo_helpers import custom_call
except ModuleNotFoundError:
# A more recent jaxlib would have `hlo_helpers` instead of `mhlo_helpers`
# <https://github.com/google/jax/commit/b8ae8e3fa10... |
shapes = {
"in.rays_sample_startidx": (n_rays,),
"in.rays_n_samples": (n_rays,),
"in.bgs": (n_rays, 3),
"in.dss": (total_samples,),
"in.z_vals": (total_samples,),
"in.drgbs": (total_samples, 4),
"in.final_rgbds": (n_rays, 4),
"in.final_opacities": ... | {
"context_start_lineno": 0,
"file": "deps/volume-rendering-jax/src/volrendjax/integrating/lowering.py",
"groundtruth_start_lineno": 106,
"repository": "blurgyy-jaxngp-adbe49e",
"right_context_start_lineno": 107,
"task_id": "project_cc_python/4677"
} | {
"list": [
{
"filename": "deps/spherical-harmonics-encoding-jax/src/shjax/__init__.py",
"retrieved_chunk": " opaque = cudaops.make_spherical_harmonics_encoding_descriptor(n, degree)\n # Documentation says directly return the `custom_call` would suffice, but directly returning\n # here result... | make_integrating_backward_descriptor(n_rays, total_samples, near_distance) |
{
"list": [
{
"filename": "ardilla/schemas.py",
"retrieved_chunk": " pk = field.name\n fields.append(field_schema[\"schema\"])\n constrains.append(field_schema[\"constraint\"]) if field_schema[\n \"constraint\"\n ] else None\n schema = (\n f\"CREATE... | import sqlite3
from pathlib import Path
from datetime import datetime
from ardilla import Model, Field
from ardilla.errors import ModelIntegrityError
from pydantic import Json
def test_default_tablename():
class Foo(Model):
id: int
assert Foo.__tablename__ == "foo"
def test_field_pk():
cla... |
def test_double_pks():
try:
class Book(Model):
id: int = Field(pk=True)
name: str = Field(pk=True)
except Exception as e:
assert isinstance(e, ModelIntegrityError) | {
"context_start_lineno": 0,
"file": "tests/test_models.py",
"groundtruth_start_lineno": 86,
"repository": "chrisdewa-ardilla-312a373",
"right_context_start_lineno": 87,
"task_id": "project_cc_python/4823"
} | {
"list": [
{
"filename": "ardilla/schemas.py",
"retrieved_chunk": " return schema\ndef get_pk(schema: str) -> Optional[str]:\n \"\"\"Gets the primary key field name from the passed schema\n Args:\n schema (str): table schema\n Returns:\n Optional[str]: the name of the primar... | __pk__ == "id" |
{
"list": [
{
"filename": "deps/spherical-harmonics-encoding-jax/src/shjax/__init__.py",
"retrieved_chunk": "def _spherical_harmonics_encoding_lowering_cuda(\n ctx: mlir.LoweringRuleContext,\n coord: ir.Value,\n hint: ir.Value,\n ):\n coord_type = ir.RankedTensorType(coord.t... | from jax.interpreters import mlir
from jax.interpreters.mlir import ir
from .. import volrendutils_cuda
try:
from jaxlib.mhlo_helpers import custom_call
except ModuleNotFoundError:
# A more recent jaxlib would have `hlo_helpers` instead of `mhlo_helpers`
# <https://github.com/google/jax/commit/b8ae8e3fa10... |
shapes = {
"in.rays_sample_startidx": (n_rays,),
"in.rays_n_samples": (n_rays,),
"in.bgs": (n_rays, 3),
"in.dss": (total_samples,),
"in.z_vals": (total_samples,),
"in.drgbs": (total_samples, 4),
"helper.measured_batch_size": (1,),
"out.final_rgbds... | {
"context_start_lineno": 0,
"file": "deps/volume-rendering-jax/src/volrendjax/integrating/lowering.py",
"groundtruth_start_lineno": 32,
"repository": "blurgyy-jaxngp-adbe49e",
"right_context_start_lineno": 33,
"task_id": "project_cc_python/4676"
} | {
"list": [
{
"filename": "deps/spherical-harmonics-encoding-jax/src/shjax/__init__.py",
"retrieved_chunk": " opaque = cudaops.make_spherical_harmonics_encoding_descriptor(n, degree)\n # Documentation says directly return the `custom_call` would suffice, but directly returning\n # here result... | make_integrating_descriptor(n_rays, total_samples) |
{
"list": [
{
"filename": "tests/test_entities_viewers.py",
"retrieved_chunk": " </head>\n <body>\n<p><span class=\"entity lb-PERSON\"><span class=\"label\">PERSON</span>Ted</span> is a <span class=\"entity lb-POSITION\"><span class=\"label\">POSITION</span>Pitcher</span>.</p>\n </body>\n</ht... | import os
import sys
import re
sys.path.insert(0, os.path.join('../src'))
from extr import RegEx, RegExLabel, Entity, Location
from extr.entities import create_entity_extractor, \
EntityAnnotator, \
HtmlEntityAnnotator, \
KnowledgeBaseEntit... |
assert annotated_text == '##ENTITY_PERSON_2## is a ##ENTITY_POSITION_1##.'
def test_html_annotate():
entities = [
Entity(1, 'POSITION', 'Pitcher', Location(9, 16)),
Entity(2, 'PERSON', 'Ted', Location(0, 3))
]
annotated_text = HtmlEntityAnnotator().annotate('Ted is a Pitcher.', entit... | {
"context_start_lineno": 0,
"file": "tests/test_entities.py",
"groundtruth_start_lineno": 69,
"repository": "dpasse-extr-f695d3c",
"right_context_start_lineno": 70,
"task_id": "project_cc_python/4779"
} | {
"list": [
{
"filename": "tests/test_entities_viewers.py",
"retrieved_chunk": " viewer = HtmlViewer()\n viewer.append('Ted is a Pitcher.', entities)\n html_doc = viewer.create_view(\"\"\"\n.lb-PERSON { background-color: orange; }\n.lb-POSITION { background-color: yellow; }\n\"\"\")\n asse... | annotate('Ted is a Pitcher.', entities) |
{
"list": [
{
"filename": "src/chatgpdp/modules/ui/chat_window.py",
"retrieved_chunk": " if is_loading:\n self.loading_text, self.loading_index, self.loading_timer = \" Thinking\", 0, QTimer()\n self.loading_timer.timeout.connect(self.update_loading_text)\n sel... | import markdown2
from PyQt5.QtWidgets import QSizePolicy, QTextEdit, QLabel, QWidget, QVBoxLayout
from PyQt5.QtCore import Qt, pyqtSignal
from PyQt5.QtGui import QIcon
from chatgpdp.modules.utils.settings import Settings
from chatgpdp.modules.utils.utilities import Utilities
from chatgpdp.styles.use_style import Style... |
return style
def format_code(self, code):
return f"<style>{Message.styles}</style>{code}" if Message.styles else code
def resize(self):
margins = self.contentsMargins()
height = int(self.doc.size().height() + margins.top() + margins.bottom())
self.setFixedHeight(height... | {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/chat/message.py",
"groundtruth_start_lineno": 85,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 86,
"task_id": "project_cc_python/4788"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/ui/chat_window.py",
"retrieved_chunk": " self.send_button.setText(f\"{self.loading_text}{'.' * self.loading_index}{' ' * (3 - self.loading_index)}\")\n def set_to_save(self):\n self.setWindowTitle(\n f\"{self.window_title} - {... | get_style("markdown.css") |
{
"list": [
{
"filename": "src/chatgpdp/modules/chat/message.py",
"retrieved_chunk": " def __init__(self, chatbot, message, mode):\n super().__init__()\n self.layout = QVBoxLayout(self)\n self.setLayout(self.layout)\n styles = {\n \"author\": f\"color: {self.c... | from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import (
QWidget,
QVBoxLayout,
QScrollArea,
)
from chatgpdp.modules.chat.message import MessageBox
class ChatBox(QScrollArea):
def __init__(self, parent):
super().__init__()
self.parent = parent
self.setWidgetResizable(True)
... |
self.layout.addWidget(message_widget)
def clear(self):
for i in reversed(range(self.layout.count())):
self.layout.itemAt(i).widget().deleteLater()
def scroll_to_bottom(self):
if not self.is_editing:
self.scrollbar.setValue(self.scrollbar.maximum())
| {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/ui/components/chat_box.py",
"groundtruth_start_lineno": 32,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 33,
"task_id": "project_cc_python/4793"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/chat/message.py",
"retrieved_chunk": " self.text_message = Message(chatbot, message)\n self.text_message.messageChanged.connect(self.emit_signal)\n self.text_message.setStyleSheet(styles[\"message\"])\n self.layout.addWidget(s... | messageChanged.connect(self.parent.set_to_save) |
{
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/components/color_picker.py",
"retrieved_chunk": " label = QLabel(self.title)\n self.button = QPushButton()\n self.button.setFixedSize(20, 20)\n self.button.setCursor(Qt.PointingHandCursor)\n self.set_color()\n ... | from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPixmap, QCursor
from PyQt5.QtWidgets import QDialog, QVBoxLayout, QLabel, QPushButton, QHBoxLayout
from chatgpdp.resources import resources_rc
from chatgpdp.modules.utils.utilities import Utilities
class AboutDialog(QDialog):
metadata = Utilities.get_metadata(... |
layout.addWidget(label)
layout.addWidget(QLabel(""))
button_layout = QHBoxLayout()
ok_button = QPushButton("Cheers!")
ok_button.setCursor(QCursor(Qt.PointingHandCursor))
ok_button.clicked.connect(self.accept)
button_layout.addWidget(ok_button)
layou... | {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/dialogs/about.py",
"groundtruth_start_lineno": 34,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 35,
"task_id": "project_cc_python/4798"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/components/color_picker.py",
"retrieved_chunk": " if color.isValid():\n self.color = color.name()\n self.set_color()\n def set_color(self):\n self.button.setStyleSheet(f\"background-color: {self.color}; bord... | open_link(url)) |
{
"list": [
{
"filename": "src/chatgpdp/modules/ui/components/prompt_box.py",
"retrieved_chunk": " self.installEventFilter(self)\n self.textChanged.connect(self.resize_prompt)\n def eventFilter(self, obj, event):\n if event.type() == QEvent.KeyPress and obj is self:\n ... | import markdown2
from PyQt5.QtWidgets import QSizePolicy, QTextEdit, QLabel, QWidget, QVBoxLayout
from PyQt5.QtCore import Qt, pyqtSignal
from PyQt5.QtGui import QIcon
from chatgpdp.modules.utils.settings import Settings
from chatgpdp.modules.utils.utilities import Utilities
from chatgpdp.styles.use_style import Style... |
super().mouseReleaseEvent(event)
def wheelEvent(self, event):
event.ignore()
| {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/chat/message.py",
"groundtruth_start_lineno": 164,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 165,
"task_id": "project_cc_python/4789"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/ui/components/prompt_box.py",
"retrieved_chunk": " return super().eventFilter(obj, event)\n def resize_prompt(self):\n documentHeight = self.document().size().height()\n scrollbarHeight = self.verticalScrollBar().sizeHint().height... | open_link(anchor) |
{
"list": [
{
"filename": "tests/test_context.py",
"retrieved_chunk": " regexes=[\n RegEx([r'is ruled out'], flags=re.IGNORECASE)\n ]\n ),\n RegExLabel(\n 'HISTORY_RIGHT',\n regexes=[\n RegEx([r'history of'], flags=re.IGNORECASE)\n ]\n ... | import os
import sys
import re
sys.path.insert(0, os.path.join('../src'))
from extr import RegEx, RegExLabel, Entity, Location
from extr.entities import create_entity_extractor, \
EntityAnnotator, \
HtmlEntityAnnotator, \
KnowledgeBaseEntit... |
assert len(entities) == 2
def test_get_entities_with_overlap():
regex_labels = [
RegExLabel('PERSON', [
RegEx([r'ted'], re.IGNORECASE)
]),
RegExLabel('POSITION', [
RegEx([r'pitch'], re.IGNORECASE),
RegEx([r'pitcher'], re.IGNORECASE)
]),
... | {
"context_start_lineno": 0,
"file": "tests/test_entities.py",
"groundtruth_start_lineno": 23,
"repository": "dpasse-extr-f695d3c",
"right_context_start_lineno": 24,
"task_id": "project_cc_python/4778"
} | {
"list": [
{
"filename": "tests/test_context.py",
"retrieved_chunk": " RegExLabel(\n 'INFECTION',\n regexes=[\n RegEx([r'pneumonia'], flags=re.IGNORECASE)\n ]\n ),\n]\nrules = [\n ConTextRule(\n 'NEGATED',",
"score": 34.32314314817424
},
{... | get_entities('Ted is a Pitcher.') |
{
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": "from PyQt5.QtCore import Qt\nfrom chatgpdp.modules.chat.bot import Chatbot\nfrom chatgpdp.modules.dialogs.components.color_picker import ColorPicker\nfrom chatgpdp.modules.utils.settings import Settings\nclass Se... | from PyQt5.QtWidgets import (
QDialog,
QDialogButtonBox,
QTextEdit,
QVBoxLayout,
)
from chatgpdp.modules.utils.settings import Settings
class PersonalityDialog(QDialog):
def __init__(self, parent=None):
super().__init__(parent)
self.setWindowTitle("Change Personality")
sel... |
self.text_area = QTextEdit()
button_box = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)
button_box.accepted.connect(self.save_file_and_close)
button_box.rejected.connect(self.reject)
vbox = QVBoxLayout()
vbox.addWidget(self.text_area)
vbox.... | {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/dialogs/personality.py",
"groundtruth_start_lineno": 16,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 17,
"task_id": "project_cc_python/4799"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": " self.api_settings = ApiSettings(self.settings)\n colors_settings = ColorSetting(self.settings)\n button_box = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)\n for b... | get_by_key("chat/initial_prompt") |
{
"list": [
{
"filename": "src/extr/models.py",
"retrieved_chunk": " @property\n def actual_end(self) -> int:\n return self.end - 1\n def is_in(self: TLocation, other: TLocation) -> bool:\n return self.start >= other.start and self.start <= other.actual_end \\\n or se... | from abc import ABC, abstractmethod
from typing import Callable, Generator, List, Optional, Set
from enum import Enum
from dataclasses import dataclass
from ..models import Entity, Token
from ..tokenizers import word_tokenizer as tokenizer
class DirectionType(Enum):
LEFT=0
RIGHT=1
BOTH=2
class ConTextRul... |
tokens = token_group.tokens
for current_index, token in enumerate(tokens):
for rule in self._rule_grouping.get_rules(token):
for tokens_by_direction in self._get_tokens_for_rule(
current_index,
rule,
tokens
... | {
"context_start_lineno": 0,
"file": "src/extr/entities/context.py",
"groundtruth_start_lineno": 90,
"repository": "dpasse-extr-f695d3c",
"right_context_start_lineno": 91,
"task_id": "project_cc_python/4771"
} | {
"list": [
{
"filename": "src/extr/tokenizers/tokenizer.py",
"retrieved_chunk": " actual_term = cache[start:end]\n assert actual_term == term, f'mismatch(\"{actual_term}\", \"{term}\")'\n tokens_in_sentence.append(\n Token(term, Location(offset + start, offset + end), ... | apply_entities(entities) |
{
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": " self.api_settings = ApiSettings(self.settings)\n colors_settings = ColorSetting(self.settings)\n button_box = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)\n for b... | from PyQt5.QtWidgets import (
QDialog,
QDialogButtonBox,
QTextEdit,
QVBoxLayout,
)
from chatgpdp.modules.utils.settings import Settings
class PersonalityDialog(QDialog):
def __init__(self, parent=None):
super().__init__(parent)
self.setWindowTitle("Change Personality")
sel... |
self.accept()
def reject(self):
self.done(QDialog.Rejected)
| {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/dialogs/personality.py",
"groundtruth_start_lineno": 33,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 34,
"task_id": "project_cc_python/4800"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": " scroll_area_content = QWidget(scroll_area)\n scroll_area.setWidget(scroll_area_content)\n scroll_area_layout = QVBoxLayout(scroll_area_content)\n scroll_area_layout.addWidget(self.api... | get().setValue("chat/initial_prompt", self.personality) |
{
"list": [
{
"filename": "src/chatgpdp/modules/utils/settings.py",
"retrieved_chunk": " self._load()\n def _setup(self):\n \"\"\"\n Creates the config file if it doesn't exist and returns the QSettings object\n \"\"\"\n documents_path = QStandardPaths.writableLoc... | import os
import openai
from chatgpdp.modules.utils.config import PATHS, engines
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import AIMessage, HumanMessage
from langchain.chains import Convers... |
openai.api_key = key
def create_messages(self, prompt):
self.add_to_history({"role": "user", "content": prompt})
return self.history
def get_initial_prompt(self):
for message in self.history:
if message["role"] == "system":
return message["content"]... | {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/chat/bot.py",
"groundtruth_start_lineno": 37,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 38,
"task_id": "project_cc_python/4785"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/utils/settings.py",
"retrieved_chunk": " settings.setIniCodec(\"UTF-8\")\n return settings\n def _load(self):\n \"\"\"\n Loads the saved settings from the config file, if present, otherwise sets default options\n \"\... | get_by_key("OPENAI_API_KEY") |
{
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": " def __init__(self, settings):\n super().__init__(\"OpenAI API Key\")\n self.settings = settings\n layout = QVBoxLayout(self)\n self.value = QLineEdit(self.settings.value(\"OPENAI_A... | import markdown2
from PyQt5.QtWidgets import QSizePolicy, QTextEdit, QLabel, QWidget, QVBoxLayout
from PyQt5.QtCore import Qt, pyqtSignal
from PyQt5.QtGui import QIcon
from chatgpdp.modules.utils.settings import Settings
from chatgpdp.modules.utils.utilities import Utilities
from chatgpdp.styles.use_style import Style... |
class Message(QTextEdit):
heightChanged = pyqtSignal()
messageChanged = pyqtSignal()
plugins = ["fenced-code-blocks", "codehilite", "tables", "break-on-newline"]
styles = ""
def __init__(self, chatbot, message):
super().__init__()
self.doc = self.document()
self.chatbot =... | {
"context_start_lineno": 0,
"file": "src/chatgpdp/modules/chat/message.py",
"groundtruth_start_lineno": 55,
"repository": "gabacode-chatGPDP-4701532",
"right_context_start_lineno": 56,
"task_id": "project_cc_python/4787"
} | {
"list": [
{
"filename": "src/chatgpdp/modules/dialogs/settings.py",
"retrieved_chunk": " super().__init__(\"Colors\")\n self.settings = settings\n layout = QVBoxLayout(self)\n for scope in [\"system\", \"assistant\", \"user\"]:\n group_box = QGroupBox(scope.cap... | get_name_from_mode(mode) + ":") |
{
"list": [
{
"filename": "src/processing/02_ica.py",
"retrieved_chunk": "bad_subjects = ['01P', '02P', '03P', '04P', '05P', '06P', '07P']#these ica need to be done manually\nall_fnames = zip(\n get_all_fnames(args.subject, kind='filt', exclude=exclude),\n get_all_fnames(args.subject, kind='ica'... | """
Compute the Power Spectral Density (PSD) for each channel.
Running:
import subprocess
subprocess.run('/net/tera2/home/aino/work/mtbi-eeg/python/processing/eeg/runall.sh', shell=True)
"""
import argparse
from mne.io import read_raw_fif
from mne.time_frequency import psd_array_welch
from h5io import write_hdf5
fr... |
elif '2' in task:
task_wo_run = task.removesuffix('_run2')
run = 2
else:
task_wo_run = task
raw = read_raw_fif(fname.clean(subject=args.subject, task=task_wo_run, run=run,ses='01'),
preload=True)
# Reduce logging level (technically, one could... | {
"context_start_lineno": 0,
"file": "src/processing/03_psds.py",
"groundtruth_start_lineno": 50,
"repository": "BioMag-mtbi_meeg-b757aa8",
"right_context_start_lineno": 51,
"task_id": "project_cc_python/4887"
} | {
"list": [
{
"filename": "src/processing/02_ica.py",
"retrieved_chunk": " # Reduce logging level (technically, one could define it in the read_raw_fif function, but it seems to be buggy)\n # More info about the bug can be found here: https://github.com/mne-tools/mne-python/issues/8872\n set_... | removesuffix('_run1') |
{
"list": [
{
"filename": "local_groundingdino/util/inference.py",
"retrieved_chunk": " except ValueError:\n class_ids.append(None)\n return np.array(class_ids)",
"score": 22.227762920743313
},
{
"filename": "scripts/sam.py",
"retrieved_chunk"... | import os
import gc
import glob
import copy
from PIL import Image
from collections import OrderedDict
import numpy as np
import torch
import cv2
from sam_hq.automatic import SamAutomaticMaskGeneratorHQ
from modules import scripts, shared
from modules.paths import extensions_dir
from modules.devices import torch_gc
gl... |
annotations = sorted(annotations, key=lambda x: x['area'], reverse=True)
print(f"Auto SAM generated {len(annotations)} masks")
for ann in annotations:
valid_mask = torch.tensor(maskUtils.decode(ann['segmentation'])).bool()
propose_classes_ids = torch.tensor(class_ids[valid_mask])
nu... | {
"context_start_lineno": 0,
"file": "scripts/auto.py",
"groundtruth_start_lineno": 84,
"repository": "continue-revolution-sd-webui-segment-anything-5df716b",
"right_context_start_lineno": 85,
"task_id": "project_cc_python/4828"
} | {
"list": [
{
"filename": "local_groundingdino/util/inference.py",
"retrieved_chunk": " except ValueError:\n class_ids.append(None)\n return np.array(class_ids)",
"score": 22.227762920743313
},
{
"filename": "scripts/sam.py",
"retrieved_chunk"... | generate(img) |
{
"list": [
{
"filename": "src/analysis/02_plot_processed_data.py",
"retrieved_chunk": "import sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nSRC_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\nsys.path.append(SRC_DIR)\nfrom ... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 11 12:18:46 2022
@author: aino
Plots histograms for each frequency band. (should these be moved to plot.py??)
Performs the Wilcoxon signed rank test.
"""
from readdata import dataframe as df
from plot import global_df as gdf
import matplotlib.pypl... |
controls = gdf.loc[gdf['Group']==0]
plot = True
delta_p = patients.iloc[:, 1]
delta_c =controls.iloc[:, 1]
theta_p = patients.iloc[:, 2]
theta_c = controls.iloc[:, 2]
alpha_p = patients.iloc[:, 3]
alpha_c = controls.iloc[:, 3]
beta_p = patients.iloc[:, 4]
beta_c = controls.iloc[:, 4]
gamma_p = patients.iloc[:, 5]
g... | {
"context_start_lineno": 0,
"file": "src/other_files/wilcoxon.py",
"groundtruth_start_lineno": 21,
"repository": "BioMag-mtbi_meeg-b757aa8",
"right_context_start_lineno": 22,
"task_id": "project_cc_python/4885"
} | {
"list": [
{
"filename": "src/analysis/02_plot_processed_data.py",
"retrieved_chunk": "#sns.set_style()\ndef initialize_argparser(metadata):\n \"\"\" Initialize argparser and add args to metadata.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('-v', '--verbosity', action='s... | loc[gdf['Group']==1] |
{
"list": [
{
"filename": "local_groundingdino/models/GroundingDINO/backbone/swin_transformer.py",
"retrieved_chunk": " ):\n super().__init__()\n self.pretrain_img_size = pretrain_img_size\n self.num_layers = len(depths)\n self.embed_dim = embed_dim\n self.ape = a... | # ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------... |
else:
raise NotImplementedError("Unknown backbone {}".format(args.backbone))
assert len(bb_num_channels) == len(
return_interm_indices
), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
model = Joiner(backbone, position_embeddi... | {
"context_start_lineno": 0,
"file": "local_groundingdino/models/GroundingDINO/backbone/backbone.py",
"groundtruth_start_lineno": 206,
"repository": "continue-revolution-sd-webui-segment-anything-5df716b",
"right_context_start_lineno": 207,
"task_id": "project_cc_python/4829"
} | {
"list": [
{
"filename": "local_groundingdino/models/GroundingDINO/backbone/swin_transformer.py",
"retrieved_chunk": " # if use_checkpoint:\n # print(\"use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!\")\n # split image into non-overlapping patches\n self.patch_embed = PatchEmbe... | num_features[4 - len(return_interm_indices) :] |
{
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " Bullet(\"Elements:\"),\n Bullet(\"First element\", level=1, symbol=\"(1)\"),\n Bullet(\"Second element\", level=1, symbol=\"(2)\"),\n Bullet(\"Third element\... | from manim import *
from powermanim.layouts.arrangedbullets import Bullet
from powermanim.showcase.showcasescene import ShowcaseScene
from powermanim.templates.bulletlist import BulletList
class BulletListShowcase(ShowcaseScene):
def showcasing():
return BulletList
def construct(self):
rows ... |
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.clear())
self.play(bullets.only_next())
se... | {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/templates/bulletlist.py",
"groundtruth_start_lineno": 26,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4875"
} | {
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " ).set_opacity(1)\n self.play(\n MoveToTarget(g_rows),\n rate_func=there_and_back_with_pause,\n run_time=3,\n )\n self.wait()",
"score"... | add(bullets) |
{
"list": [
{
"filename": "src/fnames.py",
"retrieved_chunk": "\"\"\"Utility class to manage a list of filenames.\nUse the `add` method to add new filenames. You specify a short \"alias\" for\nthem, which you can use to retrieve the full filename later:\n>>> fname = FileNames()\n>>> fname.add('my_file... | """
These are all the relevant parameters that are unique to the EEG analysis
pipeline.
"""
import os
from fnames import FileNames
from config_common import (raw_data_dir, processed_data_dir, figures_dir,
reports_dir)
tasks = ['ec', 'eo', 'PASAT']
#######################################... |
fname.add('processed_data_dir', processed_data_dir)
# Continuous data
fname.add('raw', '{raw_data_dir}/sub-{subject}/ses-{ses}/meg/sub-{subject}_ses-{ses}_task-{task}_run-0{run}_proc-raw_meg.fif')
fname.add('filt', '{processed_data_dir}/sub-{subject}/ses-01/eeg/sub-{subject}_ses-{ses}_task-{task}_run-0{run}_filt.fif'... | {
"context_start_lineno": 0,
"file": "src/config_eeg.py",
"groundtruth_start_lineno": 380,
"repository": "BioMag-mtbi_meeg-b757aa8",
"right_context_start_lineno": 381,
"task_id": "project_cc_python/4884"
} | {
"list": [
{
"filename": "src/fnames.py",
"retrieved_chunk": " files = dict()\n for name, value in self.__dict__.items():\n public_methods = ['list_filenames', 'add']\n if not name.startswith('_') and name not in public_methods:\n files[name] = value... | add('raw_data_dir', raw_data_dir) |
{
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": "import typing as T\nfrom manim import *\nclass VGroupHighlight(VGroup):\n def __init__(\n self,\n *args: VMobject,\n anim_run_time: float = 1.0,\n anim_lag_ratio: float = 0,\n ... | from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.showcase.showcasescene import ShowcaseScene
class VGroupHighlightShowcase(ShowcaseScene):
def showcasing():
return VGroupHighlight
def construct(self):
dots = [
Dot(radius=0.25, ... |
self.play(group.highlight(1))
self.play(group.highlight([2, 3]))
self.play(group.highlight([1, 3]))
self.play(group.highlight([]))
self.play(group.highlight([2, 4]))
self.play(group.highlight([]))
self.wait(0.5)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/vgroupghighlight.py",
"groundtruth_start_lineno": 27,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 28,
"task_id": "project_cc_python/4865"
} | {
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": " scale_about_point=None,\n scale_about_edge=None,\n **kwargs: VMobject,\n ) -> None:\n \"\"\"Group component that can highlight a subset of its submobjects.\n Args:\n ... | play(group.highlight(0)) |
{
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " width_ratio: float = 1.0,\n offset: float = 0.5,\n fill_color: Color = BLUE,\n fill_opacity: float = 0.5,\n stroke_color: Color = WHITE,\n stroke_width: float = 1.0,\n ... | from manim import *
from scipy.special import softmax
from powermanim.components.chartbars import ChartBars
from powermanim.showcase.showcasescene import ShowcaseScene
class ChartBarsShowcase(ShowcaseScene):
def showcasing():
return ChartBars
def construct(self):
axes = Axes(x_range=[0, 6], ... |
for i in range(changes):
dist2 = softmax(np.random.randn(size))
self.play(bars.animate.set_values(dist2), run_time=2)
self.play(bars.animate.set_values(dist1), run_time=2)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/chartbars.py",
"groundtruth_start_lineno": 19,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 20,
"task_id": "project_cc_python/4861"
} | {
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " values: The values to plot. There should be one value for each bar.\n xs: The x-coordinates of the bars. If None, the bars will be centered on\n each integer from 1 to the numb... | add(axes, bars) |
{
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": " self.scale_about_point = scale_about_point\n self.scale_about_edge = scale_about_edge\n self.previously_active_idxs = []\n def highlight(self, indices: T.Union[int, T.Sequence[int]]) ... | import typing as T
from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.layouts.arrangedbullets import ArrangedBullets
class BulletList(VGroup):
def __init__(
self,
*rows: T.Union[Tex, Text],
line_spacing: float = MED_LARGE_BUFF * 1.5,
... |
def only_next(self) -> Animation:
"""Highlights only the next item in the list."""
if self.highlighted > self.arranged_list.ngroups:
raise StopIteration("No more elements to highlight.")
anims = self.rows.highlight(indices=self.highlighted)
self.highlighted += 1
... | {
"context_start_lineno": 0,
"file": "src/powermanim/templates/bulletlist.py",
"groundtruth_start_lineno": 57,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 58,
"task_id": "project_cc_python/4882"
} | {
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": " If a sequence of integers is given, all indices in the sequence are highlighted. The\n previously highlighted indices are dehighlighted smoothly.\n \"\"\"\n anims ... | highlight(indices=list(range(self.highlighted))) |
{
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " Bullet(\"Elements:\"),\n Bullet(\"First element\", level=1, symbol=\"(1)\"),\n Bullet(\"Second element\", level=1, symbol=\"(2)\"),\n Bullet(\"Third element\... | from manim import *
from powermanim.layouts.arrangedbullets import Bullet
from powermanim.showcase.showcasescene import ShowcaseScene
from powermanim.templates.bulletlist import BulletList
class BulletListShowcase(ShowcaseScene):
def showcasing():
return BulletList
def construct(self):
rows ... |
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.clear())
self.play(bullets.only_next())
self.play(bullets.only_next())
sel... | {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/templates/bulletlist.py",
"groundtruth_start_lineno": 28,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 29,
"task_id": "project_cc_python/4877"
} | {
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " ).set_opacity(1)\n self.play(\n MoveToTarget(g_rows),\n rate_func=there_and_back_with_pause,\n run_time=3,\n )\n self.wait()",
"score"... | also_next()) |
{
"list": [
{
"filename": "src/powermanim/showcase/templates/bulletlist.py",
"retrieved_chunk": " )\n self.add(bullets)\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n ... | from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.showcase.showcasescene import ShowcaseScene
class VGroupHighlightShowcase(ShowcaseScene):
def showcasing():
return VGroupHighlight
def construct(self):
dots = [
Dot(radius=0.25, ... | {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/vgroupghighlight.py",
"groundtruth_start_lineno": 34,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 35,
"task_id": "project_cc_python/4867"
} | {
"list": [
{
"filename": "src/powermanim/showcase/templates/bulletlist.py",
"retrieved_chunk": " )\n self.add(bullets)\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n self.play(bullets.also_next())\n ... | wait(0.5) | |
{
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " if group is None:\n group = i\n group2bullet[group].append(row)\n group_rows = []\n for _, bullets in group2bullet.items():\n group_rows.append(VGro... | from manim import *
from powermanim.layouts.arrangedbullets import Bullet
from powermanim.showcase.showcasescene import ShowcaseScene
from powermanim.templates.bulletlist import BulletList
class BulletListShowcase(ShowcaseScene):
def showcasing():
return BulletList
def construct(self):
rows ... |
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.clear())
self.wait()
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/templates/bulletlist.py",
"groundtruth_start_lineno": 35,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 36,
"task_id": "project_cc_python/4879"
} | {
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " if group is None:\n group = i\n group2bullet[group].append(row)\n group_rows = []\n for _, bullets in group2bullet.items():\n group_rows.append(VGro... | only_next()) |
{
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": "import typing as T\nfrom manim import *\nclass VGroupHighlight(VGroup):\n def __init__(\n self,\n *args: VMobject,\n anim_run_time: float = 1.0,\n anim_lag_ratio: float = 0,\n ... | from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.showcase.showcasescene import ShowcaseScene
class VGroupHighlightShowcase(ShowcaseScene):
def showcasing():
return VGroupHighlight
def construct(self):
dots = [
Dot(radius=0.25, ... |
self.play(group.highlight(1))
self.play(group.highlight([2, 3]))
self.play(group.highlight([1, 3]))
self.play(group.highlight([]))
self.play(group.highlight([2, 4]))
self.play(group.highlight([]))
self.wait(0.5)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/vgroupghighlight.py",
"groundtruth_start_lineno": 27,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 28,
"task_id": "project_cc_python/4866"
} | {
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": " scale_about_point=None,\n scale_about_edge=None,\n **kwargs: VMobject,\n ) -> None:\n \"\"\"Group component that can highlight a subset of its submobjects.\n Args:\n ... | highlight(0)) |
{
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": "import typing as T\nfrom manim import *\nclass VGroupHighlight(VGroup):\n def __init__(\n self,\n *args: VMobject,\n anim_run_time: float = 1.0,\n anim_lag_ratio: float = 0,\n ... | from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.showcase.showcasescene import ShowcaseScene
class VGroupHighlightShowcase(ShowcaseScene):
def showcasing():
return VGroupHighlight
def construct(self):
dots = [
Dot(radius=0.25, ... |
self.play(group.highlight(0))
self.play(group.highlight(1))
self.play(group.highlight([2, 3]))
self.play(group.highlight([1, 3]))
self.play(group.highlight([]))
self.play(group.highlight([2, 4]))
self.play(group.highlight([]))
self.wait(0.5)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/vgroupghighlight.py",
"groundtruth_start_lineno": 26,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 27,
"task_id": "project_cc_python/4864"
} | {
"list": [
{
"filename": "src/powermanim/components/vgrouphighlight.py",
"retrieved_chunk": " scale_about_point=None,\n scale_about_edge=None,\n **kwargs: VMobject,\n ) -> None:\n \"\"\"Group component that can highlight a subset of its submobjects.\n Args:\n ... | add(group) |
{
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " width_ratio: float = 1.0,\n offset: float = 0.5,\n fill_color: Color = BLUE,\n fill_opacity: float = 0.5,\n stroke_color: Color = WHITE,\n stroke_width: float = 1.0,\n ... | from manim import *
from scipy.special import softmax
from powermanim.components.chartbars import ChartBars
from powermanim.showcase.showcasescene import ShowcaseScene
class ChartBarsShowcase(ShowcaseScene):
def showcasing():
return ChartBars
def construct(self):
axes = Axes(x_range=[0, 6], ... |
self.play(bars.animate.set_values(dist1), run_time=2)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/chartbars.py",
"groundtruth_start_lineno": 22,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 23,
"task_id": "project_cc_python/4863"
} | {
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " values: The values to plot. There should be one value for each bar.\n xs: The x-coordinates of the bars. If None, the bars will be centered on\n each integer from 1 to the numb... | animate.set_values(dist2), run_time=2) |
{
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " width_ratio: float = 1.0,\n offset: float = 0.5,\n fill_color: Color = BLUE,\n fill_opacity: float = 0.5,\n stroke_color: Color = WHITE,\n stroke_width: float = 1.0,\n ... | from manim import *
from scipy.special import softmax
from powermanim.components.chartbars import ChartBars
from powermanim.showcase.showcasescene import ShowcaseScene
class ChartBarsShowcase(ShowcaseScene):
def showcasing():
return ChartBars
def construct(self):
axes = Axes(x_range=[0, 6], ... |
self.play(bars.animate.set_values(dist1), run_time=2)
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/components/chartbars.py",
"groundtruth_start_lineno": 22,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 23,
"task_id": "project_cc_python/4862"
} | {
"list": [
{
"filename": "src/powermanim/components/chartbars.py",
"retrieved_chunk": " values: The values to plot. There should be one value for each bar.\n xs: The x-coordinates of the bars. If None, the bars will be centered on\n each integer from 1 to the numb... | play(bars.animate.set_values(dist2), run_time=2) |
{
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " Args:\n rows: A list of items to be displayed.\n line_spacing: The spacing between the rows.\n indent_buff: The spacing between the bullet and the text.\n left... | import typing as T
from manim import *
from powermanim.components.vgrouphighlight import VGroupHighlight
from powermanim.layouts.arrangedbullets import ArrangedBullets
class BulletList(VGroup):
def __init__(
self,
*rows: T.Union[Tex, Text],
line_spacing: float = MED_LARGE_BUFF * 1.5,
... |
self.rows = VGroupHighlight(
*self.arranged_list,
active_opacity=active_opacity,
scale_active=scale_active,
scale_about_edge=LEFT,
)
super().__init__(self.rows)
self.highlighted = 0
def also_next(self) -> Animation:
"""Highl... | {
"context_start_lineno": 0,
"file": "src/powermanim/templates/bulletlist.py",
"groundtruth_start_lineno": 38,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 39,
"task_id": "project_cc_python/4881"
} | {
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " self.global_shift = global_shift\n rows = [(row if isinstance(row, MathBullet) else Bullet(row)) for row in rows]\n self.arrage_rows((row for row in rows if row.autoplace))\n groups ... | set_opacity(inactive_opacity) |
{
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " if group is None:\n group = i\n group2bullet[group].append(row)\n group_rows = []\n for _, bullets in group2bullet.items():\n group_rows.append(VGro... | from manim import *
from powermanim.layouts.arrangedbullets import Bullet
from powermanim.showcase.showcasescene import ShowcaseScene
from powermanim.templates.bulletlist import BulletList
class BulletListShowcase(ShowcaseScene):
def showcasing():
return BulletList
def construct(self):
rows ... |
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.only_next())
self.play(bullets.clear())
self.wait()
| {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/templates/bulletlist.py",
"groundtruth_start_lineno": 34,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 35,
"task_id": "project_cc_python/4878"
} | {
"list": [
{
"filename": "src/powermanim/layouts/arrangedbullets.py",
"retrieved_chunk": " if group is None:\n group = i\n group2bullet[group].append(row)\n group_rows = []\n for _, bullets in group2bullet.items():\n group_rows.append(VGro... | clear()) |
{
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " Bullet(\"Elements:\"),\n Bullet(\"First element\", level=1, symbol=\"(1)\"),\n Bullet(\"Second element\", level=1, symbol=\"(2)\"),\n Bullet(\"Third element\... | from manim import *
from powermanim.layouts.arrangedbullets import Bullet
from powermanim.showcase.showcasescene import ShowcaseScene
from powermanim.templates.bulletlist import BulletList
class BulletListShowcase(ShowcaseScene):
def showcasing():
return BulletList
def construct(self):
rows ... |
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.also_next())
self.play(bullets.clear())
self.play(bullets.only_next())
self.play(bullets.only_next())
sel... | {
"context_start_lineno": 0,
"file": "src/powermanim/showcase/templates/bulletlist.py",
"groundtruth_start_lineno": 28,
"repository": "lucmos-powermanim-1bcf62a",
"right_context_start_lineno": 29,
"task_id": "project_cc_python/4876"
} | {
"list": [
{
"filename": "src/powermanim/showcase/layouts/arrangedbullets.py",
"retrieved_chunk": " ).set_opacity(1)\n self.play(\n MoveToTarget(g_rows),\n rate_func=there_and_back_with_pause,\n run_time=3,\n )\n self.wait()",
"score"... | play(bullets.also_next()) |
{
"list": [
{
"filename": "protovalidate/internal/constraints.py",
"retrieved_chunk": " return _repeated_field_to_cel(msg, field)\n elif field.message_type is not None and not msg.HasField(field.name):\n return None\n else:\n return _scalar_field_value_to_cel(getattr(msg, fi... | # Copyright 2023 Buf Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... |
for constraint in self._factory.get(message.DESCRIPTOR):
constraint.validate(ctx, message)
if ctx.done:
break
return ctx.violations
class ValidationError(ValueError):
"""
An error raised when a message fails to validate.
"""
violations: express... | {
"context_start_lineno": 0,
"file": "protovalidate/validator.py",
"groundtruth_start_lineno": 83,
"repository": "bufbuild-protovalidate-python-2eee9ba",
"right_context_start_lineno": 84,
"task_id": "project_cc_python/4892"
} | {
"list": [
{
"filename": "protovalidate/internal/constraints.py",
"retrieved_chunk": " def __init__(self, rules: message.Message | None):\n self._runners = []\n if rules is not None:\n self._rules_cel = _msg_to_cel(rules)\n def _validate_cel(self, ctx: ConstraintContext... | ConstraintContext(fail_fast=fail_fast, violations=into) |
{
"list": [
{
"filename": "protovalidate/validator.py",
"retrieved_chunk": "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport typing\nfrom google.protobuf import me... | # Copyright 2023 Buf Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... |
assert len(violations.violations) == 0
def test_oneofs():
msg1 = oneofs_pb2.Oneof()
msg1.y = 123
protovalidate.validate(msg1)
msg2 = oneofs_pb2.Oneof()
msg2.z.val = True
protovalidate.validate(msg2)
violations = protovalidate.collect_violations(msg1)
protovalidate.collect_violat... | {
"context_start_lineno": 0,
"file": "tests/validate_test.py",
"groundtruth_start_lineno": 22,
"repository": "bufbuild-protovalidate-python-2eee9ba",
"right_context_start_lineno": 23,
"task_id": "project_cc_python/4897"
} | {
"list": [
{
"filename": "protovalidate/validator.py",
"retrieved_chunk": "class Validator:\n \"\"\"\n Validates protobuf messages against static constraints.\n Each validator instance caches internal state generated from the static\n constraints, so reusing the same instance for multiple... | collect_violations(msg) |
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