id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
141,961 | import os
import fire
import langroid as lr
from langroid.utils.configuration import settings
import langroid.language_models as lm
DEFAULT_LLM = lm.OpenAIChatModel.GPT4_TURBO
settings = Settings()
def app(
m: str = DEFAULT_LLM, # model name
d: bool = False, # debug
nc: bool = False, # no cache
):
... | null |
141,962 | import typer
from rich import print
from rich.prompt import Prompt
from dotenv import load_dotenv
import tempfile
from langroid.agent.openai_assistant import (
OpenAIAssistant,
OpenAIAssistantConfig,
AssistantTool,
ToolType,
)
from langroid.parsing.url_loader import URLLoader
from langroid.agent.task im... | null |
141,963 | import typer
from rich import print
from rich.prompt import Prompt
from dotenv import load_dotenv
from langroid.agent.openai_assistant import OpenAIAssistant, OpenAIAssistantConfig
from langroid.agent.task import Task
from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIChatModel
from langroid.utils.l... | null |
141,964 | import os
import fire
import pandas as pd
import langroid as lr
import langroid.language_models as lm
from langroid.utils.configuration import settings
PATH = "examples/summarize/data/hf-cnn-daily-news/news10.csv"
settings = Settings()
def app(
m: str = "", # ollama/mistral:7b-instruct-v0.2-q8_0",
d: bool = ... | null |
141,965 | import os
import fire
import pandas as pd
import langroid as lr
import langroid.language_models as lm
from langroid.utils.configuration import settings
PATH = "examples/summarize/data/news.csv"
settings = Settings()
def app(
m: str = "", # ollama/mistral:7b-instruct-v0.2-q8_0",
d: bool = False, # debug
):
... | null |
141,966 | from rich import print
from rich.prompt import Prompt
import urllib.parse
from langroid.parsing.utils import closest_string
import logging
The provided code snippet includes necessary dependencies for implementing the `fix_uri` function. Write a Python function `def fix_uri(uri: str) -> str` to solve the following pro... | Fixes a URI by percent-encoding the username and password. |
141,967 | from rich import print
from rich.prompt import Prompt
import urllib.parse
from langroid.parsing.utils import closest_string
import logging
DEFAULT_PORTS = dict(
postgresql=5432,
mysql=3306,
mariadb=3306,
mssql=1433,
oracle=1521,
mongodb=27017,
redis=6379,
)
def _create_database_uri(
sche... | Main function to gather input and print the database URI. |
141,968 | import typer
from rich import print
from rich.prompt import Prompt
from typing import Dict, Any
import json
import os
from sqlalchemy import create_engine, inspect
from sqlalchemy.engine import Engine
from prettytable import PrettyTable
from utils import get_database_uri, fix_uri
from langroid.agent.special.sql.sql_cha... | Ask the user for a path to a JSON file and load context descriptions from it. Returns: dict: The context descriptions, or an empty dictionary if the user decides to skip this step. |
141,969 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
def check_python_version():
is_windows = platform.system() == "Windows"
major = sys.version_info.major
minor = sys.version_info.minor
micro = sys.version_info.micro
if is_windows:
supporte... | null |
141,970 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
dir_repos = "repositories"
script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def repo_dir(... | null |
141,971 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
python = sys.executable
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if desc is not None:
print(desc)
if live:
result = subprocess.run(command, shell=True, env=os.envi... | null |
141,972 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
python = sys.executable
def check_run(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
return result.returncode == 0
def check_run_python(code):
return... | null |
141,973 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
git = os.environ.get('GIT', "git")
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if de... | null |
141,974 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
git = os.environ.get('GIT', "git")
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def git_pull_recursive(dir):
for subdir, _, _ in os.walk(dir):
if os... | null |
141,975 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
python = sys.executable
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if desc is not N... | null |
141,976 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
python = sys.executable
skip_install = False
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def commit_hash():
def run(command, desc=None, errdesc=None, custom_env... | null |
141,977 | import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
def sadtalker_demo(checkpoint_path='checkpoints', config_path='src/config', warpfn=None):
sad_talker = SadTalker(checkpoint_path, config_path, lazy_load=True)
with gr.Blocks(analytics_enabled=False) as sadta... | null |
141,978 | import os
import cv2
import time
import glob
import argparse
import numpy as np
from PIL import Image
import torch
from tqdm import tqdm
from itertools import cycle
from torch.multiprocessing import Pool, Process, set_start_method
from facexlib.alignment import landmark_98_to_68
from facexlib.detection import init_dete... | null |
141,979 | import os
import cv2
import time
import glob
import argparse
import numpy as np
from PIL import Image
import torch
from tqdm import tqdm
from itertools import cycle
from torch.multiprocessing import Pool, Process, set_start_method
from facexlib.alignment import landmark_98_to_68
from facexlib.detection import init_dete... | null |
141,980 | import cv2
import numpy as np
from src.face3d.models.bfm import ParametricFaceModel
from src.face3d.models.facerecon_model import FaceReconModel
import torch
import subprocess, platform
import scipy.io as scio
from tqdm import tqdm
class FaceReconModel(BaseModel):
def modify_commandline_options(parser, is_train=F... | null |
141,999 | import torch
from torch import nn
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueErro... | null |
142,000 | import torch
from torch import nn
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
def forward(self, x):
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
def iresnet34(pretrained=False, p... | null |
142,001 | import torch
from torch import nn
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
def forward(self, x):
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
def iresnet50(pretrained=False, p... | null |
142,015 | import torch
from torch import nn
class CosFace(nn.Module):
def __init__(self, s=64.0, m=0.40):
def forward(self, cosine, label):
class ArcFace(nn.Module):
def __init__(self, s=64.0, m=0.5):
def forward(self, cosine: torch.Tensor, label):
def get_loss(name):
if name == "cosface":
return... | null |
142,020 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
142,029 | import datetime
import os
import pickle
import mxnet as mx
import numpy as np
import sklearn
import torch
from mxnet import ndarray as nd
from scipy import interpolate
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
def calculate_roc(thresholds,
embeddings1,
... | null |
142,064 | import numpy as np
from PIL import Image
from scipy.io import loadmat, savemat
from array import array
import os.path as osp
def LoadExpBasis(bfm_folder='BFM'):
n_vertex = 53215
Expbin = open(osp.join(bfm_folder, 'Exp_Pca.bin'), 'rb')
exp_dim = array('i')
exp_dim.fromfile(Expbin, 1)
expMU = array('f... | null |
142,071 | import os
import numpy as np
from PIL import Image
from skimage import io, img_as_float32, transform
import torch
import scipy.io as scio
def transform_semantic_1(semantic, semantic_radius):
semantic_list = [semantic for i in range(0, semantic_radius*2+1)]
coeff_3dmm = np.concatenate(semantic_list, 0)
retu... | null |
142,076 | from scipy.spatial import ConvexHull
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
def keypoint_transformation(kp_canonical, he, wo_exp=False):
kp = kp_canonical['value'] # (bs, k, 3)
yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']
yaw = headpose_pred_... | null |
142,080 | import torch, uuid
import os, sys, shutil
from src.utils.preprocess import CropAndExtract
from src.test_audio2coeff import Audio2Coeff
from src.facerender.animate import AnimateFromCoeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
from src.utils.init_path impo... | null |
142,082 | import os
from tqdm import tqdm
import torch
import numpy as np
import random
import scipy.io as scio
import src.utils.audio as audio
def crop_pad_audio(wav, audio_length):
def parse_audio_length(audio_length, sr, fps):
def generate_blink_seq_randomly(num_frames):
def get_data(first_coeff_path, audio_path, device, ref... | null |
142,083 | import cv2, os
import numpy as np
from tqdm import tqdm
import uuid
from src.utils.videoio import save_video_with_watermark
def save_video_with_watermark(video, audio, save_path, watermark=False):
temp_file = str(uuid.uuid4())+'.mp4'
cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -vcodec copy "... | null |
142,084 | import numpy as np
import cv2, os, sys, torch
from tqdm import tqdm
from PIL import Image
import safetensors
import safetensors.torch
from src.face3d.util.preprocess import align_img
from src.face3d.util.load_mats import load_lm3d
from src.face3d.models import networks
from scipy.io import loadmat, savemat
from src.u... | Return: coeffs_dict -- a dict of torch.tensors Parameters: coeffs -- torch.tensor, size (B, 256) |
142,088 | import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
from src.utils.hparams import hparams as hp
def preemphasis(wav, k, preemphasize=True):
def _stft(y):
def _amp_to_db(x):
def _normalize(S):
def linearspectrogram(wav):
D = _stft(preemphasis(wav, hp.preemp... | null |
142,093 | import os
import torch
from gfpgan import GFPGANer
from tqdm import tqdm
from src.utils.videoio import load_video_to_cv2
import cv2
def enhancer_generator_no_len(images, method='gfpgan', bg_upsampler='realesrgan'):
""" Provide a generator function so that all of the enhanced images don't need
to be stored in m... | null |
142,094 | import os
import torch
from gfpgan import GFPGANer
from tqdm import tqdm
from src.utils.videoio import load_video_to_cv2
import cv2
class GeneratorWithLen(object):
""" From https://stackoverflow.com/a/7460929 """
def __init__(self, gen, length):
self.gen = gen
self.length = length
def __len... | Provide a generator with a __len__ method so that it can passed to functions that call len() |
142,095 |
def load_x_from_safetensor(checkpoint, key):
x_generator = {}
for k,v in checkpoint.items():
if key in k:
x_generator[k.replace(key+'.', '')] = v
return x_generator | null |
142,097 | import torch
import yaml
import os
import safetensors
from safetensors.torch import save_file
from yacs.config import CfgNode as CN
import sys
from src.face3d.models import networks
from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
from src.facerender.modules.mapping import MappingNet
from sr... | null |
142,098 | import torch
import yaml
import os
import safetensors
from safetensors.torch import save_file
from yacs.config import CfgNode as CN
import sys
from src.face3d.models import networks
from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
from src.facerender.modules.mapping import MappingNet
from sr... | null |
142,099 | import os, sys
import gradio as gr
from src.gradio_demo import SadTalker
def toggle_audio_file(choice):
if choice == False:
return gr.update(visible=True), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True) | null |
142,100 | import os, sys
import gradio as gr
from src.gradio_demo import SadTalker
def ref_video_fn(path_of_ref_video):
if path_of_ref_video is not None:
return gr.update(value=True)
else:
return gr.update(value=False) | null |
142,101 | import os
import shutil
from argparse import Namespace
from src.utils.preprocess import CropAndExtract
from src.test_audio2coeff import Audio2Coeff
from src.facerender.animate import AnimateFromCoeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
from src.utils.ini... | null |
142,102 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def d... | null |
142,103 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def g... | null |
142,104 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def g... | null |
142,105 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def g... | null |
142,106 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def in... | null |
142,107 | import os, sys
from pathlib import Path
import tempfile
import gradio as gr
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
from modules.shared import opts, OptionInfo
from modules import shared, paths, script_callbacks
import launch
import glob
from huggingface_hub import snapshot_download
def o... | null |
142,108 | import os
import pathlib
import signal
from typing import Tuple
from absl import app
from absl import flags
from absl import logging
import docker
from docker import types
_ROOT_MOUNT_DIRECTORY = '/mnt/'
The provided code snippet includes necessary dependencies for implementing the `_create_mount` function. Write a Py... | Create a mount point for each file and directory used by the model. |
142,109 | import enum
import json
import os
import pathlib
import pickle
import random
import shutil
import sys
import time
from typing import Any, Dict, Union
from absl import app
from absl import flags
from absl import logging
from alphafold.common import confidence
from alphafold.common import protein
from alphafold.common im... | null |
142,110 | import enum
import json
import os
import pathlib
import pickle
import random
import shutil
import sys
import time
from typing import Any, Dict, Union
from absl import app
from absl import flags
from absl import logging
from alphafold.common import confidence
from alphafold.common import protein
from alphafold.common im... | Predicts structure using AlphaFold for the given sequence. |
142,111 | import io
import time
from typing import Collection, Optional, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.model import folding
from alphafold.relax import cleanup
from alphafold.relax import utils
import ml_collections
import nump... | Run iterative amber relax. Successive relax iterations are performed until all violations have been resolved. Each iteration involves a restrained Amber minimization, with restraint exclusions determined by violation-participating residues. Args: prot: A protein to be relaxed. stiffness: kcal/mol A**2, the restraint st... |
142,112 | import io
from alphafold.common import residue_constants
from Bio import PDB
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `overwrite_b_factors` function. Write a Python function `def overwrite_b_factors(pdb_str: str, bfactors: np.ndarray) -> str` to solve the follow... | Overwrites the B-factors in pdb_str with contents of bfactors array. Args: pdb_str: An input PDB string. bfactors: A numpy array with shape [1, n_residues, 37]. We assume that the B-factors are per residue; i.e. that the nonzero entries are identical in [0, i, :]. Returns: A new PDB string with the B-factors replaced. |
142,113 | import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from a... | Makes a mapping from PDB-format chain ID to sequence and description. |
142,114 | import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from a... | null |
142,115 | import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from a... | Reshapes and modifies monomer features for multimer models. |
142,116 | import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from a... | Add features to distinguish between chains. Args: all_chain_features: A dictionary which maps chain_id to a dictionary of features for each chain. Returns: all_chain_features: A dictionary which maps strings of the form `<seq_id>_<sym_id>` to the corresponding chain features. E.g. two chains from a homodimer would have... |
142,117 | import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from a... | null |
142,118 | import collections
import dataclasses
import itertools
import re
import string
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set
def _convert_sto_seq_to_a3m(
query_non_gaps: Sequence[bool], sto_seq: str) -> Iterable[str]:
for is_query_res_non_gap, sequence_res in zip(query_non_gaps, sto_seq)... | Converts MSA in Stockholm format to the A3M format. |
142,121 | import collections
import dataclasses
import itertools
import re
import string
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set
class TemplateHit:
"""Class representing a template hit."""
index: int
name: str
aligned_cols: int
sum_probs: Optional[float]
query: str
hit_sequence: str
... | Parses the content of an entire HHR file. |
142,122 | import collections
import dataclasses
import itertools
import re
import string
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set
class TemplateHit:
"""Class representing a template hit."""
index: int
name: str
aligned_cols: int
sum_probs: Optional[float]
query: str
hit_sequence: str
... | Parses an a3m string produced by hmmsearch. Args: query_sequence: The query sequence. a3m_string: The a3m string produced by hmmsearch. skip_first: Whether to skip the first sequence in the a3m string. Returns: A sequence of `TemplateHit` results. |
142,123 | import abc
import dataclasses
import datetime
import functools
import glob
import os
import re
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import mmcif_parsing
from alphafold.data import parsers
from alphafo... | Parses the data file from PDB that lists which pdb_ids are obsolete. |
142,124 | import abc
import dataclasses
import datetime
import functools
import glob
import os
import re
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import mmcif_parsing
from alphafold.data import parsers
from alphafo... | Parses release dates file, returns a mapping from PDBs to release dates. |
142,125 | import abc
import dataclasses
import datetime
import functools
import glob
import os
import re
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import mmcif_parsing
from alphafold.data import parsers
from alphafo... | Tries to extract template features from a single HHSearch hit. |
142,126 | import os
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Union
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import msa_identifiers
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.data.tools import hhblits
from al... | Constructs a feature dict of sequence features. |
142,127 | import os
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Union
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import msa_identifiers
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.data.tools import hhblits
from al... | Constructs a feature dict of MSA features. |
142,128 | import os
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Union
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import msa_identifiers
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.data.tools import hhblits
from al... | Runs an MSA tool, checking if output already exists first. |
142,129 | from typing import Iterable, MutableMapping, List
from alphafold.common import residue_constants
from alphafold.data import msa_pairing
from alphafold.data import pipeline
import numpy as np
MAX_TEMPLATES = 4
MSA_CROP_SIZE = 2048
def _is_homomer_or_monomer(chains: Iterable[pipeline.FeatureDict]) -> bool:
"""Checks if... | Runs processing on features to augment, pair and merge. Args: all_chain_features: A MutableMap of dictionaries of features for each chain. Returns: A dictionary of features. |
142,130 | import os
import subprocess
from typing import Sequence
from absl import logging
from alphafold.data.tools import utils
The provided code snippet includes necessary dependencies for implementing the `_to_a3m` function. Write a Python function `def _to_a3m(sequences: Sequence[str]) -> str` to solve the following proble... | Converts sequences to an a3m file. |
142,133 | from typing import AbstractSet, Any, Mapping, Optional, Sequence
from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np
def clean_and_validate_single_sequence(
input_sequence: str, min_length: int, max_length: int) -> str:
"""Check... | Validates and cleans input sequences. |
142,134 | from typing import AbstractSet, Any, Mapping, Optional, Sequence
from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `merge_chunked_msa` function. Write a... | Merges chunked database hits together into hits for the full database. |
142,135 | from typing import AbstractSet, Any, Mapping, Optional, Sequence
from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `show_msa_info` function. Write a Pyt... | Prints info and shows a plot of the deduplicated single chain MSA. |
142,136 | from typing import AbstractSet, Any, Mapping, Optional, Sequence
from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np
def empty_placeholder_template_features(
num_templates: int, num_res: int) -> Mapping[str, np.ndarray]:
return... | null |
142,137 | from typing import AbstractSet, Any, Mapping, Optional, Sequence
from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `check_cell_execution_order` function... | Check that the cell execution order is correct. Args: cells_ran: Set of cell numbers that have been executed. cell_number: The number of the cell that this check is called in. Raises: If <1:cell_number> cells haven't been executed, raise error. |
142,138 | import collections
import dataclasses
import functools
import io
from typing import Any, Dict, List, Mapping, Optional, Tuple
from alphafold.common import mmcif_metadata
from alphafold.common import residue_constants
from Bio.PDB import MMCIFParser
from Bio.PDB import PDBParser
from Bio.PDB.mmcifio import MMCIFIO
from ... | Takes a mmCIF string and constructs a `Protein` object. WARNING: All non-standard residue types will be converted into UNK. All non-standard atoms will be ignored. Args: mmcif_str: The contents of the mmCIF file chain_id: If chain_id is specified (e.g. A), then only that chain is parsed. Otherwise all chains are parsed... |
142,139 | import collections
import functools
import os
from typing import Final, List, Mapping, Tuple
import numpy as np
import tree
residue_atoms = {
'ALA': ['C', 'CA', 'CB', 'N', 'O'],
'ARG': ['C', 'CA', 'CB', 'CG', 'CD', 'CZ', 'N', 'NE', 'O', 'NH1', 'NH2'],
'ASP': ['C', 'CA', 'CB', 'CG', 'N', 'O', 'OD1', 'OD2'],
... | Returns [num_res_types, num_atom_types] mask array. |
142,140 | import collections
import functools
import os
from typing import Final, List, Mapping, Tuple
import numpy as np
import tree
chi_angles_atoms = {
'ALA': [],
# Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
'ARG': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
['CB', 'CG', 'CD', 'NE... | Define chi-angle rigid groups via one-hot representations. |
142,141 | import collections
import functools
import os
from typing import Final, List, Mapping, Tuple
import numpy as np
import tree
chi_angles_atoms = {
'ALA': [],
# Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
'ARG': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
['CB', 'CG', 'CD', 'NE... | Fill the arrays above. |
142,142 | import haiku as hk
import jax
def safe_dropout(*, tensor, safe_key, rate, is_deterministic, is_training):
if is_training and rate != 0.0 and not is_deterministic:
return hk.dropout(safe_key.get(), rate, tensor)
else:
return tensor | null |
142,143 | import haiku as hk
import jax
def _safe_key_flatten(safe_key):
# Flatten transfers "ownership" to the tree
return (safe_key._key,), safe_key._used # pylint: disable=protected-access | null |
142,144 | import haiku as hk
import jax
class SafeKey:
"""Safety wrapper for PRNG keys."""
def __init__(self, key):
self._key = key
self._used = False
def _assert_not_used(self):
if self._used:
raise RuntimeError('Random key has been used previously.')
def get(self):
self._assert_not_used()
self... | null |
142,145 | import collections
import contextlib
import functools
import numbers
from typing import Mapping
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `stable_softmax` function. Write a Python function `def stable_softmax(... | Numerically stable softmax for (potential) bfloat 16. |
142,146 | import collections
import contextlib
import functools
import numbers
from typing import Mapping
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
def bfloat16_creator(next_creator, shape, dtype, init, context):
"""Creates float32 variables when bfloat16 is requested."""
if context.original_dt... | null |
142,147 | import collections
import contextlib
import functools
import numbers
from typing import Mapping
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
def final_init(config):
if config.zero_init:
return 'zeros'
else:
return 'linear' | null |
142,148 | import numbers
from typing import Union, Sequence
import haiku as hk
import jax.numpy as jnp
import numpy as np
TRUNCATED_NORMAL_STDDEV_FACTOR = np.asarray(.87962566103423978,
dtype=np.float32)
The provided code snippet includes necessary dependencies for implementing the `g... | Get Initializer for weights and scale to multiply activations by. |
142,149 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `rot_to_quat` function. Write a Python function `def rot_to_quat(rot, unstack_inputs=False)` to solve the following problem:
Convert rotation ma... | Convert rotation matrix to quaternion. Note that this function calls self_adjoint_eig which is extremely expensive on the GPU. If at all possible, this function should run on the CPU. Args: rot: rotation matrix (see below for format). unstack_inputs: If true, rotation matrix should be shape (..., 3, 3) otherwise the ro... |
142,150 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `rot_list_to_tensor` function. Write a Python function `def rot_list_to_tensor(rot_list)` to solve the following problem:
Convert list of lists ... | Convert list of lists to rotation tensor. |
142,151 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `vec_list_to_tensor` function. Write a Python function `def vec_list_to_tensor(vec_list)` to solve the following problem:
Convert list to vector... | Convert list to vector tensor. |
142,152 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
QUAT_TO_ROT = np.zeros((4, 4, 3, 3), dtype=np.float32)
QUAT_TO_ROT[0, 0] = [[ 1, 0, 0], [ 0, 1, 0], [ 0, 0, 1]]
QUAT_TO_ROT[1, 1] = [[ 1, 0, 0], [ 0,-1, 0], [ 0, 0,-1]]
QUAT_TO_ROT[2, 2] = [[-1, 0, 0], [ 0, 1, 0], [ 0, 0,-... | Convert a normalized quaternion to a rotation matrix. |
142,153 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
QUAT_MULTIPLY_BY_VEC = QUAT_MULTIPLY[:, 1:, :]
The provided code snippet includes necessary dependencies for implementing the `quat_multiply_by_vec` function. Write a Python function `def quat_multiply_by_vec(quat, vec)` to... | Multiply a quaternion by a pure-vector quaternion. |
142,154 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
QUAT_MULTIPLY = np.zeros((4, 4, 4), dtype=np.float32)
QUAT_MULTIPLY[:, :, 0] = [[ 1, 0, 0, 0],
[ 0,-1, 0, 0],
[ 0, 0,-1, 0],
[ 0, 0, 0,-1]]
QUAT_M... | Multiply a quaternion by another quaternion. |
142,155 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `apply_inverse_rot_to_vec` function. Write a Python function `def apply_inverse_rot_to_vec(rot, vec)` to solve the following problem:
Multiply t... | Multiply the inverse of a rotation matrix by a vector. |
142,156 | import functools
from typing import Tuple
import jax
import jax.numpy as jnp
import numpy as np
def make_canonical_transform(
n_xyz: jnp.ndarray,
ca_xyz: jnp.ndarray,
c_xyz: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Returns translation and rotation matrices to canonicalize residue atoms.
Note ... | Returns rotation and translation matrices to convert from reference. Note that this method does not take care of symmetries. If you provide the atom positions in the non-standard way, the N atom will end up not at [-0.527250, 1.359329, 0.0] but instead at [-0.527250, -1.359329, 0.0]. You need to take care of such cases... |
142,157 | import collections
import contextlib
import functools
import inspect
from typing import Any, Callable, Optional, Tuple, Union
import haiku as hk
import jax
import jax.numpy as jnp
def nullcontext():
yield
def maybe_with_rng(key):
if key is not None:
return hk.with_rng(key)
else:
return nullcontext() | null |
142,158 | import collections
import contextlib
import functools
import inspect
from typing import Any, Callable, Optional, Tuple, Union
import haiku as hk
import jax
import jax.numpy as jnp
def maybe_fold_in(key, data):
if key is not None:
return jax.random.fold_in(key, data)
else:
return None | null |
142,159 | import collections
import contextlib
import functools
import inspect
from typing import Any, Callable, Optional, Tuple, Union
import haiku as hk
import jax
import jax.numpy as jnp
def _check_no_varargs(f):
if list(inspect.signature(
f).parameters.values())[0].kind == inspect.Parameter.VAR_POSITIONAL:
raise ... | Utility to wrap a Haiku function and recursively apply it to an input. A function is valid if it uses only explicit position parameters, and its return type matches its input type. The position parameters can be arbitrarily nested structures with `jnp.ndarray` at the leaf nodes. Note that kwargs are not supported, neit... |
142,160 | import io
import os
from alphafold.model import utils
import haiku as hk
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_model_haiku_params` function. Write a Python function `def get_model_haiku_params(model_name: str, data_dir: str) -> hk.Params` to solve the fo... | Get the Haiku parameters from a model name. |
142,161 | import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `tf_combine_mask` function. Write a Python function `def tf_combine_mask(*masks)` to solve the following problem:
Take the intersection of float-valued masks.
Here is the function:
def tf_combine_mask(*ma... | Take the intersection of float-valued masks. |
142,162 | from alphafold.common import residue_constants
from alphafold.model.tf import shape_helpers
from alphafold.model.tf import shape_placeholders
from alphafold.model.tf import utils
import numpy as np
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `curry1`... | Supply all arguments but the first. |
142,163 | from alphafold.common import residue_constants
from alphafold.model.tf import shape_helpers
from alphafold.model.tf import shape_placeholders
from alphafold.model.tf import utils
import numpy as np
import tensorflow.compat.v1 as tf
def make_all_atom_aatype(protein):
protein['all_atom_aatype'] = protein['aatype']
r... | null |
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