




"""simple docstring"""

import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__lowerCamelCase								=						logging.get_logger(__name__)  # pylint: disable=invalid-name


__lowerCamelCase								=						"lfs-multipart-upload"



class  _snake_case				(							A__       ):

				'''simple docstring'''




				@staticmethod
				def 				snake_case_ (							snake_case      :  ArgumentParser     ):
								UpperCAmelCase_					:Optional[Any]      			=      parser.add_parser(
								    '''lfs-enable-largefiles'''					,							help=(
								        '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
								    )					,							)
								enable_parser.add_argument('''path'''					,							type=snake_case					,							help='''Local path to repository you want to configure.'''     )
								enable_parser.set_defaults(func=lambda snake_case     : LfsEnableCommand(snake_case     )     )

								UpperCAmelCase_					:Union[str, Any]      			=      parser.add_parser(
								    snake_case					,							help=(
								        '''Deprecated: use `huggingface-cli` instead. '''
								        '''Command will get called by git-lfs, do not call it directly.'''
								    )					,							)
								upload_parser.set_defaults(func=lambda snake_case     : LfsUploadCommand(snake_case     )     )



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  Optional[int]					,							snake_case      :  Any     ):
								UpperCAmelCase_					:Any      			=      args





				def 				snake_case_ (							self      :  Union[str, Any]     ):
								warnings.warn(
								    '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''     )
								UpperCAmelCase_					:Optional[Any]      			=      os.path.abspath(self.args.path     )
								if not os.path.isdir(snake_case     ):
												print('''This does not look like a valid git repo.'''     )
												exit(1     )
								subprocess.run(
								    '''git config lfs.customtransfer.multipart.path transformers-cli'''.split()					,							check=snake_case					,							cwd=snake_case     )
								subprocess.run(
								    f'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()					,							check=snake_case					,							cwd=snake_case					,							)
								print('''Local repo set up for largefiles'''     )



def 				a (						__snake_case     :				Dict  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      json.dumps(__snake_case  ) + '''\n'''
				sys.stdout.write(__snake_case  )
				sys.stdout.flush()



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      json.loads(sys.stdin.readline().strip()  )

				if "terminate" in (msg.get('''type'''  ), msg.get('''event'''  )):
								# terminate message received
								return None

				if msg.get('''event'''  ) not in ("download", "upload"):
								logger.critical('''Received unexpected message'''  )
								sys.exit(1  )

				return msg



class  _snake_case				(							A__       ):

				'''simple docstring'''




				def __init__(							self      :  int					,							snake_case      :  str					,							snake_case      :  int					,							snake_case      :  int     ):
								UpperCAmelCase_					:List[Any]      			=      filepath
								UpperCAmelCase_					:Union[str, Any]      			=      seek_from
								UpperCAmelCase_					:Any      			=      read_limit
								UpperCAmelCase_					:Optional[int]      			=      0





				def __enter__(							self      :  Optional[Any]     ):
								UpperCAmelCase_					:List[str]      			=      open(self.filepath					,							'''rb'''     )
								self.f.seek(self.seek_from     )
								return self





				def __len__(							self      :  List[str]     ):
								UpperCAmelCase_					:Tuple      			=      os.fstat(self.f.fileno()     ).st_size
								return min(self.read_limit					,							total_length - self.seek_from     )





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  List[Any]=-1     ):
								if self.n_seen >= self.read_limit:
												return b""
								UpperCAmelCase_					:Union[str, Any]      			=      self.read_limit - self.n_seen
								UpperCAmelCase_					:List[Any]      			=      self.f.read(remaining_amount if n < 0 else min(snake_case					,							snake_case     )     )
								self.n_seen += len(snake_case     )
								return data





				def __iter__(							self      :  List[Any]     ):
								yield self.read(n=4 * 1_024 * 1_024     )





				def __exit__(							self      :  Optional[Any]					,							*snake_case      :  List[str]     ):
								self.f.close()



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  Dict					,							snake_case      :  Any     ):
								UpperCAmelCase_					:Union[str, Any]      			=      args





				def 				snake_case_ (							self      :  Dict     ):
								# Immediately after invoking a custom transfer process, git-lfs
								# sends initiation data to the process over stdin.
								# This tells the process useful information about the configuration.
								UpperCAmelCase_					:Any      			=      json.loads(sys.stdin.readline().strip()     )
								if not (init_msg.get('''event'''     ) == "init" and init_msg.get('''operation'''     ) == "upload"):
												write_msg({'''error''': {'''code''': 32, '''message''': '''Wrong lfs init operation'''}}     )
												sys.exit(1     )

								# The transfer process should use the information it needs from the
								# initiation structure, and also perform any one-off setup tasks it
								# needs to do. It should then respond on stdout with a simple empty
								# confirmation structure, as follows:
								write_msg({}     )

								# After the initiation exchange, git-lfs will send any number of
								# transfer requests to the stdin of the transfer process, in a serial sequence.
								while True:
												UpperCAmelCase_					:Dict      			=      read_msg()
												if msg is None:
																# When all transfers have been processed, git-lfs will send
																# a terminate event to the stdin of the transfer process.
																# On receiving this message the transfer process should
																# clean up and terminate. No response is expected.
																sys.exit(0     )

												UpperCAmelCase_					:List[Any]      			=      msg['''oid''']
												UpperCAmelCase_					:Optional[int]      			=      msg['''path''']
												UpperCAmelCase_					:List[str]      			=      msg['''action''']['''href''']
												UpperCAmelCase_					:Optional[Any]      			=      msg['''action''']['''header''']
												UpperCAmelCase_					:Any      			=      int(header.pop('''chunk_size'''     )     )
												UpperCAmelCase_					:List[str]      			=      list(header.values()     )

												UpperCAmelCase_					:Optional[int]      			=      []
												for i, presigned_url in enumerate(snake_case     ):
																with FileSlice(snake_case					,							seek_from=i * chunk_size					,							read_limit=snake_case     ) as data:
																				UpperCAmelCase_					:Optional[int]      			=      requests.put(snake_case					,							data=snake_case     )
																				r.raise_for_status()
																				parts.append(
																				    {
																				        '''etag''': r.headers.get('''etag'''     ),
																				        '''partNumber''': i + 1,
																				    }     )
																				# In order to support progress reporting while data is uploading / downloading,
																				# the transfer process should post messages to stdout
																				write_msg(
																				    {
																				        '''event''': '''progress''',
																				        '''oid''': oid,
																				        '''bytesSoFar''': (i + 1) * chunk_size,
																				        '''bytesSinceLast''': chunk_size,
																				    }     )
																				# Not precise but that's ok.

												UpperCAmelCase_					:Tuple      			=      requests.post(
												    snake_case					,							json={
												        '''oid''': oid,
												        '''parts''': parts,
												    }					,							)
												r.raise_for_status()

												write_msg({'''event''': '''complete''', '''oid''': oid}     )








"""simple docstring"""

from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather



@overload
def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				None  ):



				'''simple docstring'''





				...



@overload
def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor  ):



				'''simple docstring'''





				...



def 				a (						__snake_case     :				List[str],    __snake_case     :				Optional[int],    __snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      aatype == rc.restype_order['''G''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CA''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CB''']
				UpperCAmelCase_					:Optional[Any]      			=      torch.where(
				    is_gly[..., None].expand(*((-1,) * len(is_gly.shape  )),    3  ),    all_atom_positions[..., ca_idx, :],    all_atom_positions[..., cb_idx, :],    )

				if all_atom_masks is not None:
								UpperCAmelCase_					:Any      			=      torch.where(
								    __snake_case,    all_atom_masks[..., ca_idx],    all_atom_masks[..., cb_idx],    )
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta



def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      batched_gather(
				    __snake_case,    batch['''residx_atom37_to_atom14'''],    dim=-2,    no_batch_dims=len(atomaa.shape[:-2]  ),    )

				UpperCAmelCase_					:Optional[Any]      			=      atomaa_data * batch['''atom37_atom_exists'''][..., None]

				return atomaa_data



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      template_feats['''template_aatype''']
				UpperCAmelCase_					:Union[str, Any]      			=      template_feats['''template_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_alt_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_torsion_angles_mask''']
				UpperCAmelCase_					:int      			=      torch.cat(
				    [
				        nn.functional.one_hot(__snake_case,    22  ),
				        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2],    14  ),
				        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2],    14  ),
				        torsion_angles_mask,
				    ],    dim=-1,    )

				return template_angle_feat



def 				a (						__snake_case     :				Dict[str, torch.Tensor],    __snake_case     :				torch.types.Number,    __snake_case     :				torch.types.Number,    __snake_case     :				int,    __snake_case     :				bool = False,    __snake_case     :				float = 1E-20,    __snake_case     :				float = 1E8,    ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      batch['''template_pseudo_beta_mask''']
				UpperCAmelCase_					:Any      			=      template_mask[..., None] * template_mask[..., None, :]

				# Compute distogram (this seems to differ slightly from Alg. 5)
				UpperCAmelCase_					:List[str]      			=      batch['''template_pseudo_beta''']
				UpperCAmelCase_					:Any      			=      torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2,    dim=-1,    keepdim=__snake_case  )
				UpperCAmelCase_					:Any      			=      torch.linspace(__snake_case,    __snake_case,    __snake_case,    device=tpb.device  ) ** 2
				UpperCAmelCase_					:Union[str, Any]      			=      torch.cat([lower[1:], lower.new_tensor([inf]  )],    dim=-1  )
				UpperCAmelCase_					:Tuple      			=      ((dgram > lower) * (dgram < upper)).type(dgram.dtype  )

				UpperCAmelCase_					:List[str]      			=      [dgram, template_mask_ad[..., None]]

				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(
				    batch['''template_aatype'''],    rc.restype_num + 2,    )

				UpperCAmelCase_					:Optional[Any]      			=      batch['''template_aatype'''].shape[-1]
				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2],    __snake_case,    -1,    -1  )  )
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2],    -1,    __snake_case,    -1  )  )

				UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
				UpperCAmelCase_					:str      			=      Rigid.make_transform_from_reference(
				    n_xyz=batch['''template_all_atom_positions'''][..., n, :],    ca_xyz=batch['''template_all_atom_positions'''][..., ca, :],    c_xyz=batch['''template_all_atom_positions'''][..., c, :],    eps=__snake_case,    )
				UpperCAmelCase_					:Dict      			=      rigids.get_trans()[..., None, :, :]
				UpperCAmelCase_					:Dict      			=      rigids[..., None].invert_apply(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      torch.rsqrt(eps + torch.sum(rigid_vec**2,    dim=-1  )  )

				UpperCAmelCase_					:Optional[int]      			=      batch['''template_all_atom_mask''']
				UpperCAmelCase_					:Tuple      			=      t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
				UpperCAmelCase_					:int      			=      template_mask[..., None] * template_mask[..., None, :]

				UpperCAmelCase_					:str      			=      inv_distance_scalar * template_mask_ad
				UpperCAmelCase_					:List[Any]      			=      rigid_vec * inv_distance_scalar[..., None]

				if not use_unit_vector:
								UpperCAmelCase_					:List[Any]      			=      unit_vector * 0.0

				to_concat.extend(torch.unbind(unit_vector[..., None, :],    dim=-1  )  )
				to_concat.append(template_mask_ad[..., None]  )

				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case,    dim=-1  )
				UpperCAmelCase_					:Optional[Any]      			=      act * template_mask_ad[..., None]

				return act



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(batch['''extra_msa'''],    23  )
				UpperCAmelCase_					:Union[str, Any]      			=      [
				    msa_ahot,
				    batch['''extra_has_deletion'''].unsqueeze(-1  ),
				    batch['''extra_deletion_value'''].unsqueeze(-1  ),
				]
				return torch.cat(__snake_case,    dim=-1  )



def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      rrgdf[aatype, ...]

				# [*, N, 8] transformations, i.e.
				#   One [*, N, 8, 3, 3] rotation matrix and
				#   One [*, N, 8, 3]    translation matrix
				UpperCAmelCase_					:Optional[int]      			=      r.from_tensor_axa(__snake_case  )

				UpperCAmelCase_					:List[Any]      			=      alpha.new_zeros((*((1,) * len(alpha.shape[:-1]  )), 2)  )
				UpperCAmelCase_					:List[Any]      			=      1

				# [*, N, 8, 2]
				UpperCAmelCase_					:Tuple      			=      torch.cat([bb_rot.expand(*alpha.shape[:-2],    -1,    -1  ), alpha],    dim=-2  )

				# [*, N, 8, 3, 3]
				# Produces rotation matrices of the form:
				# [
				#   [1, 0  , 0  ],
				#   [0, a_2,-a_1],
				#   [0, a_1, a_2]
				# ]
				# This follows the original code rather than the supplement, which uses
				# different indices.

				UpperCAmelCase_					:Any      			=      alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
				UpperCAmelCase_					:Optional[Any]      			=      1
				UpperCAmelCase_					:int      			=      alpha[..., 1]
				UpperCAmelCase_					:Optional[int]      			=      -alpha[..., 0]
				UpperCAmelCase_					:int      			=      alpha

				UpperCAmelCase_					:List[str]      			=      default_r.compose(Rigid(Rotation(rot_mats=__snake_case  ),    __snake_case  )  )

				UpperCAmelCase_					:List[str]      			=      all_frames[..., 5]
				UpperCAmelCase_					:str      			=      all_frames[..., 6]
				UpperCAmelCase_					:List[Any]      			=      all_frames[..., 7]

				UpperCAmelCase_					:Tuple      			=      all_frames[..., 4]
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      chia_frame_to_bb.compose(__snake_case  )

				UpperCAmelCase_					:List[str]      			=      Rigid.cat(
				    [
				        all_frames[..., :5],
				        chia_frame_to_bb.unsqueeze(-1  ),
				        chia_frame_to_bb.unsqueeze(-1  ),
				        chia_frame_to_bb.unsqueeze(-1  ),
				    ],    dim=-1,    )

				UpperCAmelCase_					:Any      			=      r[..., None].compose(__snake_case  )

				return all_frames_to_global





def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      group_idx[aatype, ...]

				# [*, N, 14, 8]
				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(
				    __snake_case,    num_classes=default_frames.shape[-3],    )

				# [*, N, 14, 8]
				UpperCAmelCase_					:Dict      			=      r[..., None, :] * group_mask_one_hot

				# [*, N, 14]
				UpperCAmelCase_					:List[Any]      			=      t_atoms_to_global.map_tensor_fn(lambda __snake_case  : torch.sum(__snake_case,    dim=-1  )  )

				# [*, N, 14, 1]
				UpperCAmelCase_					:List[Any]      			=      atom_mask[aatype, ...].unsqueeze(-1  )

				# [*, N, 14, 3]
				UpperCAmelCase_					:List[Any]      			=      lit_positions[aatype, ...]
				UpperCAmelCase_					:Optional[int]      			=      t_atoms_to_global.apply(__snake_case  )
				UpperCAmelCase_					:int      			=      pred_positions * atom_mask

				return pred_positions


