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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "zb9d5N92iMz2"
},
"source": [
"# Chroma quickstart\n",
"\n",
"First, run the [setup cell](#setup) below. Then, run [this cell](#unconditional-chain) to get a Chroma sample. Further examples are below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "form",
"id": "KaT878cbeOQm"
},
"outputs": [],
"source": [
"# @title Setup\n",
"\n",
"# @markdown [Get your API key here](https://chroma-weights.generatebiomedicines.com) and enter it below before running.\n",
"\n",
"import os\n",
"\n",
"os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n",
"import contextlib\n",
"\n",
"api_key = \"2cdade6d058b4fd1b85fa5badb501312\" # @param {type:\"string\"}\n",
"\n",
"\n",
"import torch\n",
"\n",
"# torch.use_deterministic_algorithms(False)\n",
"\n",
"import warnings\n",
"from tqdm import tqdm, TqdmExperimentalWarning\n",
"\n",
"warnings.filterwarnings(\"ignore\", category=TqdmExperimentalWarning)\n",
"from functools import partialmethod\n",
"\n",
"tqdm.__init__ = partialmethod(tqdm.__init__, leave=False)\n",
"\n",
"import ipywidgets as widgets\n",
"\n",
"\n",
"def create_button(filename, description=\"\"):\n",
" button = widgets.Button(description=description)\n",
" display(button)\n",
"\n",
" def on_button_click(b):\n",
" files.download(filename)\n",
"\n",
" button.on_click(on_button_click)\n",
"\n",
"\n",
"def render(protein, trajectories, output=\"protein.cif\"):\n",
" display(protein)\n",
" print(protein)\n",
" protein.to_CIF(output)\n",
" traj_output = output.replace(\".cif\", \"_trajectory.cif\")\n",
" trajectories[\"trajectory\"].to_CIF(traj_output)\n",
" create_button(output, description=\"Download sample\")\n",
" create_button(traj_output, description=\"Download trajectory\")\n",
"\n",
"\n",
"import locale\n",
"\n",
"locale.getpreferredencoding = lambda: \"UTF-8\"\n",
"\n",
"from chroma import Chroma, Protein, conditioners\n",
"from chroma.models import graph_classifier, procap\n",
"from chroma.utility.api import register_key\n",
"from chroma.utility.chroma import letter_to_point_cloud, plane_split_protein\n",
"\n",
"register_key(api_key)\n",
"device = \"cuda\"\n",
"with contextlib.redirect_stdout(None):\n",
" chroma = Chroma(device=device)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "sfNODIk5BZEH"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Integrating SDE: 0%| | 0/200 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Potts Sampling: 0%| | 0/500 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Sequential decoding: 0%| | 0/160 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57f832c5197d4f4ebfa2ed5fae343db3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Protein: system\n",
"> Chain A (160 residues)\n",
"MRIEARTPEAARRAVDLAIKLKEKGYEVLLVLIGDPSNPELLEIARRLAEAGAKIRVIALVDDSPEAQAGVERLRQVCEELREKGADVELDVITAPLDDPEAQQRARELAEKYISEGEEEAKKKNKPFILILVRPSTDEEEAQREADEAEKKIEEYLKSL\n",
"\n",
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3385daceb5b0490092690b7f8bcbaccd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='Download sample', style=ButtonStyle())"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12e79378174b42f2890daf1102bfecea",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='Download trajectory', style=ButtonStyle())"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# @title Get a protein! <a name=\"unconditional-chain\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Specify the desired length. Chroma will output a fully designed single chain protein.\n",
"# @markdown As with all examples in this notebook, the trajectory can also be downloaded.\n",
"\n",
"length = 160 # @param {type:\"slider\", min:50, max:250, step:10}\n",
"\n",
"protein, trajectories = chroma.sample(\n",
" chain_lengths=[length], steps=200, full_output=True\n",
")\n",
"render(protein, trajectories, output=\" .cif\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "14AUlQHIxPle"
},
"source": [
"## Conditional generation\n",
"\n",
"After running the setup at the top of the notebook, all examples are completely independent.\n",
"\n",
"[Single chain](#unconditional-chain): the simplest example of protein generation with Chroma.\n",
"\n",
"[Complex](#unconditional-complex): a protein with multiple chains.\n",
"\n",
"[Symmetry](#symmetry): a symmetric complex, where the symmetry group and subunit size can be input by the user.\n",
"\n",
"[Substructure](#substructure): infilling a PDB structure, where the residues to design can be specified by a PyMOL-style string.\n",
"\n",
"[Shape](#shape): Chroma generation conditioned on shape, using letters as an example.\n",
"\n",
"[Topology](#proclass-chain): chain-level conditioning using ProClass, where a CAT code can be specified.\n",
"\n",
"[Secondary structure](#proclass-residue): ProClass also provides conditioning of secondary structure, which can be input as a per-residue string.\n",
"\n",
"[Natural language](#procap): ProCap takes a user caption in order to condition Chroma generation.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KLbZia40CQhK"
},
"outputs": [],
"source": [
"# @title Complexes <a name=\"unconditional-complex\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Given the lengths of individual chains, Chroma can generate a complex.\n",
"\n",
"chain1_length = 400 # @param {type:\"slider\", min:100, max:500, step:10}\n",
"chain2_length = 100 # @param {type:\"slider\", min:0, max:200, step:10}\n",
"chain3_length = 100 # @param {type:\"slider\", min:0, max:200, step:10}\n",
"chain4_length = 100 # @param {type:\"slider\", min:0, max:200, step:10}\n",
"\n",
"protein, trajectories = chroma.sample(\n",
" chain_lengths=[chain1_length, chain2_length, chain3_length, chain4_length],\n",
" steps=200,\n",
" full_output=True,\n",
")\n",
"render(protein, trajectories, output=\"complex.cif\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7FpA3VzWWTM_"
},
"outputs": [],
"source": [
"# @title Symmetry <a name=\"symmetry\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Specify the desired symmetry type and the size of a single subunit.\n",
"\n",
"symmetry_group = \"C_7\" # @param [\"C_2\", \"C_3\", \"C_4\", \"C_5\", \"C_6\", \"C_7\", \"C_8\", \"D_2\", \"D_3\", \"D_4\", \"D_5\", \"D_6\", \"D_7\", \"D_8\", \"T\", \"O\", \"I\"]\n",
"subunit_size = 100 # @param {type:\"slider\", min:10, max:150, step:5}\n",
"knbr = 2\n",
"\n",
"conditioner = conditioners.SymmetryConditioner(\n",
" G=symmetry_group, num_chain_neighbors=knbr\n",
")\n",
"symmetric_protein, trajectories = chroma.sample(\n",
" chain_lengths=[subunit_size],\n",
" conditioner=conditioner,\n",
" langevin_factor=8,\n",
" inverse_temperature=8,\n",
" sde_func=\"langevin\",\n",
" potts_symmetry_order=conditioner.potts_symmetry_order,\n",
" full_output=True,\n",
")\n",
"render(symmetric_protein, trajectories, output=\"symmetric_protein.cif\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "BCTxghg19meL"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "29438c75064c436aa3cffff6876f9bf3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Split protein by plane, masking 52.38 percent of residues.\n",
"Error initializing conditioner! Falling back to masking 50% of residues.\n"
]
},
{
"ename": "RuntimeError",
"evalue": "expected scalar type Double but found Float",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 24\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 24\u001b[0m conditioner \u001b[38;5;241m=\u001b[39m \u001b[43mconditioners\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSubstructureConditioner\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mprotein\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackbone_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchroma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackbone_network\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselection_string\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/conditioners.py:881\u001b[0m, in \u001b[0;36mSubstructureConditioner.__init__\u001b[0;34m(self, protein, backbone_model, selection, rg, weight, tspan, weight_max, gamma, center_init)\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnoise_schedule \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackbone_model\u001b[38;5;241m.\u001b[39mnoise_perturb\u001b[38;5;241m.\u001b[39mnoise_schedule\n\u001b[0;32m--> 881\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional_distribution \u001b[38;5;241m=\u001b[39m \u001b[43mmvn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mConditionalBackboneMVNGlobular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[43mcovariance_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcovariance_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[43mcomplex_scaling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomplex_scaling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 884\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 885\u001b[0m \u001b[43m \u001b[49m\u001b[43mC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mC\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 886\u001b[0m \u001b[43m \u001b[49m\u001b[43mD\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 887\u001b[0m \u001b[43m \u001b[49m\u001b[43mgamma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgamma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 888\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 889\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional_distribution\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;241m1\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/mvn.py:559\u001b[0m, in \u001b[0;36mConditionalBackboneMVNGlobular.__init__\u001b[0;34m(self, covariance_model, complex_scaling, sigma_translation, X, C, D, gamma, **kwargs)\u001b[0m\n\u001b[1;32m 558\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_C(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC\u001b[38;5;241m.\u001b[39mabs())\n\u001b[0;32m--> 559\u001b[0m R, RRt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_materialize_RRt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mC\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR\u001b[39m\u001b[38;5;124m\"\u001b[39m, R)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/mvn.py:648\u001b[0m, in \u001b[0;36mConditionalBackboneMVNGlobular._materialize_RRt\u001b[0;34m(self, C)\u001b[0m\n\u001b[1;32m 646\u001b[0m R_init \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_R_init(C_atom, b)\n\u001b[0;32m--> 648\u001b[0m R \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mR_center\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mR_sum\u001b[49m \u001b[38;5;241m@\u001b[39m R_init\n\u001b[1;32m 649\u001b[0m RRt \u001b[38;5;241m=\u001b[39m R \u001b[38;5;241m@\u001b[39m R\u001b[38;5;241m.\u001b[39mt()\n",
"\u001b[0;31mRuntimeError\u001b[0m: expected scalar type Double but found Float",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 30\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError initializing conditioner! Falling back to masking 50\u001b[39m\u001b[38;5;132;01m% o\u001b[39;00m\u001b[38;5;124mf residues.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 29\u001b[0m selection_string \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnamesel infilling_selection\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 30\u001b[0m conditioner \u001b[38;5;241m=\u001b[39m \u001b[43mconditioners\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSubstructureConditioner\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43mprotein\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackbone_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchroma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackbone_network\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselection_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mrg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 37\u001b[0m infilled_protein, trajectories \u001b[38;5;241m=\u001b[39m chroma\u001b[38;5;241m.\u001b[39msample(\n\u001b[1;32m 38\u001b[0m protein_init\u001b[38;5;241m=\u001b[39mprotein,\n\u001b[1;32m 39\u001b[0m conditioner\u001b[38;5;241m=\u001b[39mconditioner,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 45\u001b[0m full_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 46\u001b[0m )\n\u001b[1;32m 47\u001b[0m render(infilled_protein, trajectories, output\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfilled_protein.cif\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/conditioners.py:881\u001b[0m, in \u001b[0;36mSubstructureConditioner.__init__\u001b[0;34m(self, protein, backbone_model, selection, rg, weight, tspan, weight_max, gamma, center_init)\u001b[0m\n\u001b[1;32m 879\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_distribution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackbone_model\u001b[38;5;241m.\u001b[39mnoise_perturb\u001b[38;5;241m.\u001b[39mbase_gaussian\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnoise_schedule \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackbone_model\u001b[38;5;241m.\u001b[39mnoise_perturb\u001b[38;5;241m.\u001b[39mnoise_schedule\n\u001b[0;32m--> 881\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional_distribution \u001b[38;5;241m=\u001b[39m \u001b[43mmvn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mConditionalBackboneMVNGlobular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[43mcovariance_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcovariance_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[43mcomplex_scaling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomplex_scaling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 884\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 885\u001b[0m \u001b[43m \u001b[49m\u001b[43mC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mC\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 886\u001b[0m \u001b[43m \u001b[49m\u001b[43mD\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 887\u001b[0m \u001b[43m \u001b[49m\u001b[43mgamma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgamma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 888\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 889\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconditional_distribution\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 890\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtspan \u001b[38;5;241m=\u001b[39m tspan\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/mvn.py:559\u001b[0m, in \u001b[0;36mConditionalBackboneMVNGlobular.__init__\u001b[0;34m(self, covariance_model, complex_scaling, sigma_translation, X, C, D, gamma, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m\"\u001b[39m, D\u001b[38;5;241m.\u001b[39mfloat())\n\u001b[1;32m 558\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_C(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC\u001b[38;5;241m.\u001b[39mabs())\n\u001b[0;32m--> 559\u001b[0m R, RRt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_materialize_RRt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mC\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR\u001b[39m\u001b[38;5;124m\"\u001b[39m, R)\n\u001b[1;32m 561\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRRt\u001b[39m\u001b[38;5;124m\"\u001b[39m, RRt)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/layers/structure/mvn.py:648\u001b[0m, in \u001b[0;36mConditionalBackboneMVNGlobular._materialize_RRt\u001b[0;34m(self, C)\u001b[0m\n\u001b[1;32m 645\u001b[0m R_sum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_R_sum(C_atom, b)\n\u001b[1;32m 646\u001b[0m R_init \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_R_init(C_atom, b)\n\u001b[0;32m--> 648\u001b[0m R \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mR_center\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mR_sum\u001b[49m \u001b[38;5;241m@\u001b[39m R_init\n\u001b[1;32m 649\u001b[0m RRt \u001b[38;5;241m=\u001b[39m R \u001b[38;5;241m@\u001b[39m R\u001b[38;5;241m.\u001b[39mt()\n\u001b[1;32m 650\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m R, RRt\n",
"\u001b[0;31mRuntimeError\u001b[0m: expected scalar type Double but found Float"
]
}
],
"source": [
"# @title Substructure <a name=\"substructure\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Enter a PDB ID and a selection string corresponding to designable positions.\n",
"# @markdown Using a substructure conditioner, Chroma can design at these positions while holding the rest of the structure fixed.\n",
"# @markdown The default selection cuts the protein in half and fills it in.\n",
"# @markdown Other selections, by position or proximity, are also allowed.\n",
"\n",
"pdb_id = \"5SV5\" # @param ['5SV5', '6QAZ', '3BDI'] {allow-input:true}\n",
"\n",
"try:\n",
" protein = Protein.from_PDBID(pdb_id, canonicalize=True, device=device)\n",
" display(protein)\n",
"except FileNotFoundError:\n",
" print(\"Invalid PDB ID! Using 3BDI\")\n",
" pdb_id = \"3BDI\"\n",
" protein = Protein.from_PDBID(pdb_id, canonicalize=True, device=device)\n",
"\n",
"X, C, _ = protein.to_XCS()\n",
"selection_string = \"namesel infilling_selection\" # @param ['namesel infilling_selection', 'z > 16', '(resid 50) around 10'] {allow-input:true}\n",
"residues_to_design = plane_split_protein(X, C, protein, 0.5).nonzero()[:, 1].tolist()\n",
"protein.sys.save_selection(gti=residues_to_design, selname=\"infilling_selection\")\n",
"\n",
"try:\n",
" conditioner = conditioners.SubstructureConditioner(\n",
" protein, backbone_model=chroma.backbone_network, selection=selection_string\n",
" ).to(device)\n",
"except Exception:\n",
" print(\"Error initializing conditioner! Falling back to masking 50% of residues.\")\n",
" selection_string = \"namesel infilling_selection\"\n",
" conditioner = conditioners.SubstructureConditioner(\n",
" protein,\n",
" backbone_model=chroma.backbone_network,\n",
" selection=selection_string,\n",
" rg=True,\n",
" ).to(device)\n",
"\n",
"infilled_protein, trajectories = chroma.sample(\n",
" protein_init=protein,\n",
" conditioner=conditioner,\n",
" langevin_factor=4.0,\n",
" langevin_isothermal=True,\n",
" inverse_temperature=8.0,\n",
" steps=500,\n",
" sde_func=\"langevin\",\n",
" full_output=True,\n",
")\n",
"render(infilled_protein, trajectories, output=\"infilled_protein.cif\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "h5C-SrJEBqIs"
},
"outputs": [],
"source": [
"# @title Shape <a name=\"shape\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Create a protein in the shape of a desired character of arbitrary length.\n",
"\n",
"character = \"G\" # @param {type:\"string\"}\n",
"if len(character) > 1:\n",
" character = character[:1]\n",
" print(f\"Keeping only first character ({character})!\")\n",
"length = 1000 # @param {type:\"slider\", min:100, max:1500, step:100}\n",
"\n",
"letter_point_cloud = letter_to_point_cloud(character)\n",
"conditioner = conditioners.ShapeConditioner(\n",
" letter_point_cloud,\n",
" chroma.backbone_network.noise_schedule,\n",
" autoscale_num_residues=length,\n",
").to(device)\n",
"\n",
"shaped_protein, trajectories = chroma.sample(\n",
" chain_lengths=[length], conditioner=conditioner, full_output=True\n",
")\n",
"\n",
"render(shaped_protein, trajectories, output=\"shaped_protein.cif\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "M-5X_saooA6J"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cached data from /tmp/chroma_weights/3262b44702040b1dcfccd71ebbcf451d/weights.pt\n",
"Loaded from cache\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9264434606184578a8bda0e8dcd33c0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Integrating SDE: 0%| | 0/500 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80717e8b2395434d8af0d2667bf9005a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Potts Sampling: 0%| | 0/500 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "743e20adbb814b01a94d92498c3f9a1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Sequential decoding: 0%| | 0/130 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46c25647fdc947128e4df00f6c343fd0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Protein: system\n",
"> Chain A (130 residues)\n",
"MIPPFIPKKLLDELKKLAEKYGATIEFMPFEEAAQKHLSPEALARPIRDLLKELEDKINEAINEFYSLLPKDIEVKPVTLSIVFPEMPEEELKRFIDEIKTLINKVIDEYKSLPKEERQKEALELIKELF\n",
"\n",
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e40f62022b143ba83ee29b13831ceb7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='Download sample', style=ButtonStyle())"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "542ef56229e8423dbb0069bddcb2fccd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='Download trajectory', style=ButtonStyle())"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# @title Fold <a name=\"proclass-chain\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Input a [CATH number](https://cathdb.info/browse) to get chain-level conditioning, e.g. `3.40.50` for a Rossmann fold or `2` for mainly beta.\n",
"\n",
"CATH = \"3.40.50\" # @param {type:\"string\"}\n",
"length = 130 # @param {type:\"slider\", min:50, max:250, step:10}\n",
"\n",
"proclass_model = graph_classifier.load_model(\"named:public\", device=device)\n",
"conditioner = conditioners.ProClassConditioner(\"cath\", CATH, model=proclass_model)\n",
"cath_conditioned_protein, trajectories = chroma.sample(\n",
" conditioner=conditioner, chain_lengths=[length], full_output=True\n",
")\n",
"render(cath_conditioned_protein, trajectories, output=\"cath_conditioned_protein.cif\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A2012-PnoTHf"
},
"outputs": [],
"source": [
"# @title Secondary structure <a name=\"proclass-residue\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown Enter a string to specify residue-level secondary structure conditioning: H = helix, E = strand, T = turn.\n",
"\n",
"SS = \"HHHHHHHTTTHHHHHHHTTTEEEEEETTTEEEEEEEETTTTHHHHHHHH\" # @param {type:\"string\"}\n",
"\n",
"proclass_model = graph_classifier.load_model(\"named:public\", device=device)\n",
"conditioner = conditioners.ProClassConditioner(\n",
" \"secondary_structure\", SS, max_norm=None, model=proclass_model\n",
")\n",
"ss_conditioned_protein, trajectories = chroma.sample(\n",
" steps=500, conditioner=conditioner, chain_lengths=[len(SS)], full_output=True\n",
")\n",
"render(ss_conditioned_protein, trajectories, output=\"ss_conditioned_protein.cif\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "ix41mhyEbLTF"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cached data from /tmp/chroma_weights/87243729397de5f93afc4f392662d1b5/weights.pt\n"
]
},
{
"ename": "OSError",
"evalue": "We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like EleutherAI/gpt-neo-125m is not the path to a directory containing a file named config.json.\nCheckout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connection.py:174\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 174\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mextra_kw\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/util/connection.py:95\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m socket\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetaddrinfo returns an empty list\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 84\u001b[0m sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 85\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sock\n",
"\u001b[0;31mOSError\u001b[0m: [Errno 101] Network is unreachable",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 717\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 718\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 719\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m 727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connectionpool.py:404\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 404\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 406\u001b[0m \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connectionpool.py:1058\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m 1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m): \u001b[38;5;66;03m# AppEngine might not have `.sock`\u001b[39;00m\n\u001b[0;32m-> 1058\u001b[0m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1060\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connection.py:363\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 362\u001b[0m \u001b[38;5;66;03m# Add certificate verification\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 364\u001b[0m hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connection.py:186\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NewConnectionError(\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to establish a new connection: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m e\n\u001b[1;32m 188\u001b[0m )\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m conn\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPSConnection object at 0x7f7d6e2d6c70>: Failed to establish a new connection: [Errno 101] Network is unreachable",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 487\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 488\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 498\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/connectionpool.py:799\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 797\u001b[0m e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[0;32m--> 799\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 800\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 801\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 802\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/urllib3/util/retry.py:592\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_retry\u001b[38;5;241m.\u001b[39mis_exhausted():\n\u001b[0;32m--> 592\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause))\n\u001b[1;32m 594\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /EleutherAI/gpt-neo-125m/resolve/main/config.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d6e2d6c70>: Failed to establish a new connection: [Errno 101] Network is unreachable'))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mConnectionError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/file_download.py:1238\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m 1237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1238\u001b[0m metadata \u001b[38;5;241m=\u001b[39m \u001b[43mget_hf_file_metadata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1240\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1241\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1242\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43metag_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1243\u001b[0m \u001b[43m \u001b[49m\u001b[43mlibrary_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlibrary_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1244\u001b[0m \u001b[43m \u001b[49m\u001b[43mlibrary_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlibrary_version\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1245\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1246\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m http_error:\n\u001b[1;32m 1248\u001b[0m \u001b[38;5;66;03m# Cache the non-existence of the file and raise\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/file_download.py:1631\u001b[0m, in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m 1630\u001b[0m \u001b[38;5;66;03m# Retrieve metadata\u001b[39;00m\n\u001b[0;32m-> 1631\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43m_request_wrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1632\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHEAD\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1633\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1634\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1635\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1636\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_relative_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1637\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1638\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1639\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1640\u001b[0m hf_raise_for_status(r)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/file_download.py:385\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m follow_relative_redirects:\n\u001b[0;32m--> 385\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43m_request_wrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_relative_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;66;03m# If redirection, we redirect only relative paths.\u001b[39;00m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;66;03m# This is useful in case of a renamed repository.\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/file_download.py:408\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;66;03m# Perform request and return if status_code is not in the retry list.\u001b[39;00m\n\u001b[0;32m--> 408\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mget_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 409\u001b[0m hf_raise_for_status(response)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/utils/_http.py:67\u001b[0m, in \u001b[0;36mUniqueRequestIdAdapter.send\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/requests/adapters.py:519\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SSLError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m--> 519\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ClosedPoolError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"\u001b[0;31mConnectionError\u001b[0m: (MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /EleutherAI/gpt-neo-125m/resolve/main/config.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d6e2d6c70>: Failed to establish a new connection: [Errno 101] Network is unreachable'))\"), '(Request ID: ecd4f2fa-3a5d-4a13-b144-91df9440bd15)')",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mLocalEntryNotFoundError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/utils/hub.py:409\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 408\u001b[0m \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 409\u001b[0m resolved_file \u001b[38;5;241m=\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 410\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/huggingface_hub/file_download.py:1371\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1370\u001b[0m \u001b[38;5;66;03m# Otherwise: most likely a connection issue or Hub downtime => let's warn the user\u001b[39;00m\n\u001b[0;32m-> 1371\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LocalEntryNotFoundError(\n\u001b[1;32m 1372\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error happened while trying to locate the file on the Hub and we cannot find the requested files\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1373\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in the local cache. Please check your connection and try again or make sure your Internet connection\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is on.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1375\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhead_call_error\u001b[39;00m\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;66;03m# From now on, etag and commit_hash are not None.\u001b[39;00m\n",
"\u001b[0;31mLocalEntryNotFoundError\u001b[0m: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m length \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m110\u001b[39m \u001b[38;5;66;03m# @param {type:\"slider\", min:50, max:250, step:10}\u001b[39;00m\n\u001b[1;32m 6\u001b[0m caption \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCrystal structure of SH2 domain\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# @param {type:\"string\"}\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m procap_model \u001b[38;5;241m=\u001b[39m \u001b[43mprocap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnamed:public\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m conditioner \u001b[38;5;241m=\u001b[39m conditioners\u001b[38;5;241m.\u001b[39mProCapConditioner(caption, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, model\u001b[38;5;241m=\u001b[39mprocap_model)\n\u001b[1;32m 10\u001b[0m caption_conditioned_protein, trajectories \u001b[38;5;241m=\u001b[39m chroma\u001b[38;5;241m.\u001b[39msample(\n\u001b[1;32m 11\u001b[0m steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m, chain_lengths\u001b[38;5;241m=\u001b[39m[length], conditioner\u001b[38;5;241m=\u001b[39mconditioner, full_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 12\u001b[0m )\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/models/procap.py:729\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(weight_file, device, strict, strict_unexpected)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model\u001b[39m(\n\u001b[1;32m 705\u001b[0m weight_file: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 706\u001b[0m device: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 707\u001b[0m strict: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 708\u001b[0m strict_unexpected: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 709\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ProteinCaption:\n\u001b[1;32m 710\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Loads a ProCap model.\u001b[39;00m\n\u001b[1;32m 711\u001b[0m \n\u001b[1;32m 712\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[38;5;124;03m with `model.eval()`.\u001b[39;00m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mutility_load_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 730\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 731\u001b[0m \u001b[43m \u001b[49m\u001b[43mProteinCaption\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 732\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 733\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict_unexpected\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict_unexpected\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/utility/model.py:107\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(weights, model_class, device, strict, strict_unexpected, verbose)\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;66;03m# load model weights\u001b[39;00m\n\u001b[1;32m 106\u001b[0m params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(weights, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 107\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minit_kwargs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 108\u001b[0m missing_keys, unexpected_keys \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mload_state_dict(\n\u001b[1;32m 109\u001b[0m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_state_dict\u001b[39m\u001b[38;5;124m\"\u001b[39m], strict\u001b[38;5;241m=\u001b[39mstrict\n\u001b[1;32m 110\u001b[0m )\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m strict_unexpected \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unexpected_keys) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/chroma/models/procap.py:198\u001b[0m, in \u001b[0;36mProteinCaption.__init__\u001b[0;34m(self, lm_id, gnn_dim_edges, context_size, context_per_chain, gnn_num_neighbors, gnn_num_layers, only_encode_caption_chain, gnn_embed_ratio, graph_criterion, node_mlp_layers, node_mlp_dim, noise_schedule, covariance_model, noise_complex_scaling, noiseless, normalize_context_embeddings, standardize_context_embeddings, time_feature_type, time_log_feature_scaling, use_transformer, classifier_checkpoint, direct_gnn, classifier_kwargs)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize_context_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;66;03m# Use Pretrained Tokenizer From Hugging Face\u001b[39;00m\n\u001b[0;32m--> 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer \u001b[38;5;241m=\u001b[39m \u001b[43mtransformers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAutoTokenizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mlm_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[43m \u001b[49m\u001b[43madditional_special_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m<|pdb|>\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m<|unconditioned|>\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 201\u001b[0m \u001b[43m \u001b[49m\u001b[43meos_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m<|endoftext|>\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 202\u001b[0m \u001b[43m \u001b[49m\u001b[43mpad_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m<|pad|>\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;66;03m# Use Pretrained Language Model From Hugging Face\u001b[39;00m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlanguage_model \u001b[38;5;241m=\u001b[39m transformers\u001b[38;5;241m.\u001b[39mAutoModelForCausalLM\u001b[38;5;241m.\u001b[39mfrom_pretrained(lm_id)\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py:575\u001b[0m, in \u001b[0;36mAutoTokenizer.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config_tokenizer_class \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 574\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(config, PretrainedConfig):\n\u001b[0;32m--> 575\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mAutoConfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrust_remote_code\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 577\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 578\u001b[0m config_tokenizer_class \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mtokenizer_class\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(config, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutoTokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config\u001b[38;5;241m.\u001b[39mauto_map:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/models/auto/configuration_auto.py:776\u001b[0m, in \u001b[0;36mAutoConfig.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 774\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname_or_path\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pretrained_model_name_or_path\n\u001b[1;32m 775\u001b[0m trust_remote_code \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrust_remote_code\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 776\u001b[0m config_dict, unused_kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mPretrainedConfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_config_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 777\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutoConfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m trust_remote_code:\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/configuration_utils.py:559\u001b[0m, in \u001b[0;36mPretrainedConfig.get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 557\u001b[0m original_kwargs \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(kwargs)\n\u001b[1;32m 558\u001b[0m \u001b[38;5;66;03m# Get config dict associated with the base config file\u001b[39;00m\n\u001b[0;32m--> 559\u001b[0m config_dict, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_config_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict:\n\u001b[1;32m 561\u001b[0m original_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/configuration_utils.py:614\u001b[0m, in \u001b[0;36mPretrainedConfig._get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 610\u001b[0m configuration_file \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_configuration_file\u001b[39m\u001b[38;5;124m\"\u001b[39m, CONFIG_NAME)\n\u001b[1;32m 612\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 613\u001b[0m \u001b[38;5;66;03m# Load from local folder or from cache or download from model Hub and cache\u001b[39;00m\n\u001b[0;32m--> 614\u001b[0m resolved_config_file \u001b[38;5;241m=\u001b[39m \u001b[43mcached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 615\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 616\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfiguration_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 617\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 618\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 619\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 623\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 624\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 625\u001b[0m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 626\u001b[0m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 627\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 628\u001b[0m commit_hash \u001b[38;5;241m=\u001b[39m extract_commit_hash(resolved_config_file, commit_hash)\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m:\n\u001b[1;32m 630\u001b[0m \u001b[38;5;66;03m# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to\u001b[39;00m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;66;03m# the original exception.\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/mlfold/lib/python3.8/site-packages/transformers/utils/hub.py:443\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _raise_exceptions_for_missing_entries \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _raise_exceptions_for_connection_errors:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 443\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m 444\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWe couldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt connect to \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mHUGGINGFACE_CO_RESOLVE_ENDPOINT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to load this file, couldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find it in the\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 445\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m cached files and it looks like \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not the path to a directory containing a file named\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfull_filename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mCheckout your internet connection or see how to run the library in offline mode at\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/docs/transformers/installation#offline-mode\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 448\u001b[0m )\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError:\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _raise_exceptions_for_missing_entries:\n",
"\u001b[0;31mOSError\u001b[0m: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like EleutherAI/gpt-neo-125m is not the path to a directory containing a file named config.json.\nCheckout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'."
]
}
],
"source": [
"# @title Natural text <a name=\"procap\"></a> {display-mode: \"form\"}\n",
"\n",
"# @markdown ProCap uses natural language captions to condition samples.\n",
"\n",
"length = 110 # @param {type:\"slider\", min:50, max:250, step:10}\n",
"caption = \"Crystal structure of SH2 domain\" # @param {type:\"string\"}\n",
"\n",
"procap_model = procap.load_model(\"named:public\", device=device)\n",
"conditioner = conditioners.ProCapConditioner(caption, -1, model=procap_model)\n",
"caption_conditioned_protein, trajectories = chroma.sample(\n",
" steps=200, chain_lengths=[length], conditioner=conditioner, full_output=True\n",
")\n",
"render(\n",
" caption_conditioned_protein, trajectories, output=\"caption_conditioned_protein.cif\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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|