


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=7     ):
							__snake_case		:      str										=       None
							if token is not None:
														__snake_case		:      str										=       {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

							# The id of a workflow (not of a workflow run)
							__snake_case		:      Optional[Any]										=       "636036"

							__snake_case		:      Dict										=       F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'

							__snake_case		:      str										=       requests.get(__lowerCamelCase							,     headers=__lowerCamelCase     ).json()

							return result["workflow_runs"]







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Optional[int]										=       get_daily_ci_runs(__lowerCamelCase     )
							__snake_case		:      int										=       None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					__snake_case		:      int										=       workflow_run["id"]
																					break

							return workflow_run_id







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[int]										=       get_last_daily_ci_runs(__lowerCamelCase     )
							if workflow_run_id is not None:
														__snake_case		:      Optional[Any]										=       get_artifacts_links(worflow_run_id=__lowerCamelCase							,     token=__lowerCamelCase     )
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												__snake_case		:      str										=       artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=__lowerCamelCase							,     artifact_url=__lowerCamelCase							,     output_dir=__lowerCamelCase							,     token=__lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							get_last_daily_ci_artifacts(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )

							__snake_case		:      int										=       {}
							for artifact_name in artifact_names:
														__snake_case		:      str										=       os.path.join(__lowerCamelCase							,     F'{artifact_name}.zip'     )
														if os.path.isfile(__lowerCamelCase     ):
																					__snake_case		:      Dict										=       {}
																					with zipfile.ZipFile(__lowerCamelCase     ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(__lowerCamelCase     ):
																																										# read the file
																																										with z.open(__lowerCamelCase     ) as f:
																																																	__snake_case		:      Optional[int]										=       f.read().decode("UTF-8"     )

							return results





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
														__snake_case		:      Tuple										=       {name: i for i, name in enumerate(__lowerCamelCase     )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       protein["aatype"].to(torch.long     )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							__snake_case		:      Dict										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Dict										=       residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

