code: 
  
  
  
  
  
  
  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   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Tuple:
  				...
  
  
  
  @overload
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Tuple:
  				...
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[Any]:
  				A__     :							Tuple   				=aatype == rc.restype_order['''G''']
  				A__     :							List[Any]   				=rc.atom_order['''CA''']
  				A__     :							str   				=rc.atom_order['''CB''']
  				A__     :							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:
  								A__     :							Optional[int]   				=torch.where(
  								    UpperCamelCase_,	all_atom_masks[..., ca_idx],	all_atom_masks[..., cb_idx],	)
  								return pseudo_beta, pseudo_beta_mask
  				else:
  								return pseudo_beta
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							List[Any]:
  				A__     :							Dict   				=batched_gather(
  				    UpperCamelCase_,	batch['''residx_atom37_to_atom14'''],	dim=-2,	no_batch_dims=len(atomaa.shape[:-2]	),	)
  
  				A__     :							Any   				=atomaa_data * batch['''atom37_atom_exists'''][..., None]
  
  				return atomaa_data
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							List[str]:
  				A__     :							List[Any]   				=template_feats['''template_aatype''']
  				A__     :							int   				=template_feats['''template_torsion_angles_sin_cos''']
  				A__     :							Optional[Any]   				=template_feats['''template_alt_torsion_angles_sin_cos''']
  				A__     :							int   				=template_feats['''template_torsion_angles_mask''']
  				A__     :							int   				=torch.cat(
  				    [
  				        nn.functional.one_hot(UpperCamelCase_,	2_2	),
  				        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2],	1_4	),
  				        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2],	1_4	),
  				        torsion_angles_mask,
  				    ],	dim=-1,	)
  
  				return template_angle_feat
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ = False,	snake_case_ = 1e-20,	snake_case_ = 1e8,	)			->							Tuple:
  				A__     :							Optional[int]   				=batch['''template_pseudo_beta_mask''']
  				A__     :							Dict   				=template_mask[..., None] * template_mask[..., None, :]
  
  				# Compute distogram (this seems to differ slightly from Alg. 5)
  				A__     :							Dict   				=batch['''template_pseudo_beta''']
  				A__     :							Optional[int]   				=torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2,	dim=-1,	keepdim=UpperCamelCase_	)
  				A__     :							Tuple   				=torch.linspace(UpperCamelCase_,	UpperCamelCase_,	UpperCamelCase_,	device=tpb.device	) ** 2
  				A__     :							Union[str, Any]   				=torch.cat([lower[1:], lower.new_tensor([inf]	)],	dim=-1	)
  				A__     :							List[str]   				=((dgram > lower) * (dgram < upper)).type(dgram.dtype	)
  
  				A__     :							List[str]   				=[dgram, template_mask_ad[..., None]]
  
  				A__     :							Any   				=nn.functional.one_hot(
  				    batch['''template_aatype'''],	rc.restype_num + 2,	)
  
  				A__     :							Any   				=batch['''template_aatype'''].shape[-1]
  				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2],	UpperCamelCase_,	-1,	-1	)	)
  				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2],	-1,	UpperCamelCase_,	-1	)	)
  
  				A__				,  A__				,  A__     :							Tuple   				=[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
  				A__     :							Union[str, Any]   				=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=UpperCamelCase_,	)
  				A__     :							Optional[int]   				=rigids.get_trans()[..., None, :, :]
  				A__     :							Any   				=rigids[..., None].invert_apply(UpperCamelCase_	)
  
  				A__     :							Optional[Any]   				=torch.rsqrt(eps + torch.sum(rigid_vec**2,	dim=-1	)	)
  
  				A__     :							Dict   				=batch['''template_all_atom_mask''']
  				A__     :							int   				=t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
  				A__     :							Dict   				=template_mask[..., None] * template_mask[..., None, :]
  
  				A__     :							List[str]   				=inv_distance_scalar * template_mask_ad
  				A__     :							Optional[int]   				=rigid_vec * inv_distance_scalar[..., None]
  
  				if not use_unit_vector:
  								A__     :							Optional[Any]   				=unit_vector * 0.0
  
  				to_concat.extend(torch.unbind(unit_vector[..., None, :],	dim=-1	)	)
  				to_concat.append(template_mask_ad[..., None]	)
  
  				A__     :							int   				=torch.cat(UpperCamelCase_,	dim=-1	)
  				A__     :							List[Any]   				=act * template_mask_ad[..., None]
  
  				return act
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Union[str, Any]:
  				A__     :							Dict   				=nn.functional.one_hot(batch['''extra_msa'''],	2_3	)
  				A__     :							str   				=[
  				    msa_ahot,
  				    batch['''extra_has_deletion'''].unsqueeze(-1	),
  				    batch['''extra_deletion_value'''].unsqueeze(-1	),
  				]
  				return torch.cat(UpperCamelCase_,	dim=-1	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_,	)			->							Tuple:
  				A__     :							Dict   				=rrgdf[aatype, ...]
  
  				# [*, N, 8] transformations, i.e.
  				#   One [*, N, 8, 3, 3] rotation matrix and
  				#   One [*, N, 8, 3]    translation matrix
  				A__     :							int   				=r.from_tensor_axa(UpperCamelCase_	)
  
  				A__     :							Union[str, Any]   				=alpha.new_zeros((*((1,) * len(alpha.shape[:-1]	)), 2)	)
  				A__     :							List[str]   				=1
  
  				# [*, N, 8, 2]
  				A__     :							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.
  
  				A__     :							Union[str, Any]   				=alpha.new_zeros(default_r.get_rots().get_rot_mats().shape	)
  				A__     :							Union[str, Any]   				=1
  				A__     :							str   				=alpha[..., 1]
  				A__     :							Optional[Any]   				=-alpha[..., 0]
  				A__     :							str   				=alpha
  
  				A__     :							List[Any]   				=default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase_	),	UpperCamelCase_	)	)
  
  				A__     :							str   				=all_frames[..., 5]
  				A__     :							int   				=all_frames[..., 6]
  				A__     :							Optional[int]   				=all_frames[..., 7]
  
  				A__     :							Tuple   				=all_frames[..., 4]
  				A__     :							List[str]   				=chia_frame_to_bb.compose(UpperCamelCase_	)
  				A__     :							int   				=chia_frame_to_bb.compose(UpperCamelCase_	)
  				A__     :							Union[str, Any]   				=chia_frame_to_bb.compose(UpperCamelCase_	)
  
  				A__     :							Tuple   				=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,	)
  
  				A__     :							int   				=r[..., None].compose(UpperCamelCase_	)
  
  				return all_frames_to_global
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_,	)			->							str:
  				A__     :							List[str]   				=group_idx[aatype, ...]
  
  				# [*, N, 14, 8]
  				A__     :							int   				=nn.functional.one_hot(
  				    UpperCamelCase_,	num_classes=default_frames.shape[-3],	)
  
  				# [*, N, 14, 8]
  				A__     :							str   				=r[..., None, :] * group_mask_one_hot
  
  				# [*, N, 14]
  				A__     :							str   				=t_atoms_to_global.map_tensor_fn(lambda snake_case_	: torch.sum(UpperCamelCase_,	dim=-1	)	)
  
  				# [*, N, 14, 1]
  				A__     :							str   				=atom_mask[aatype, ...].unsqueeze(-1	)
  
  				# [*, N, 14, 3]
  				A__     :							Any   				=lit_positions[aatype, ...]
  				A__     :							Tuple   				=t_atoms_to_global.apply(UpperCamelCase_	)
  				A__     :							int   				=pred_positions * atom_mask
  
  				return pred_positions
  
  
  
  
style_context: 
  
  
  import argparse
  import collections
  from pathlib import Path
  
  import torch
  from torch.serialization import default_restore_location
  
  from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
  
  
  __lowerCamelCase       :       Optional[Any]				   =			collections.namedtuple(
      "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
  )
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							CheckpointState:
  				print(f'Reading saved model from {model_file}'	)
  				A__     :							Optional[int]   				=torch.load(snake_case_,	map_location=lambda snake_case_,	snake_case_	: default_restore_location(snake_case_,	'''cpu'''	)	)
  				return CheckpointState(**snake_case_	)
  
  
  
  class 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=src_file
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						raise NotImplementedError
  
  
  
  
  
  
  		@staticmethod
  		def       lowerCAmelCase_  (  __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			"DPRState":
  						'''simple docstring'''
  
  
  						if comp_type.startswith('''c'''							):
  										return DPRContextEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						if comp_type.startswith('''q'''							):
  										return DPRQuestionEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						if comp_type.startswith('''r'''							):
  										return DPRReaderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						else:
  										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR biencoder from {self.src_file}'							)
  						A__     :							Optional[int]   				=load_states_from_checkpoint(self.src_file							)
  						A__				,  A__     :							List[str]   				=model.ctx_encoder, '''ctx_model.'''
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[int]   				={'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith(__UpperCamelCase							):
  														A__     :							int   				=key[len(__UpperCamelCase							) :]
  														if not key.startswith('''encode_proj.'''							):
  																		A__     :							Any   				='''bert_model.''' + key
  														A__     :							List[str]   				=value
  						encoder.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR biencoder from {self.src_file}'							)
  						A__     :							List[str]   				=load_states_from_checkpoint(self.src_file							)
  						A__				,  A__     :							Optional[int]   				=model.question_encoder, '''question_model.'''
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[Any]   				={'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith(__UpperCamelCase							):
  														A__     :							List[Any]   				=key[len(__UpperCamelCase							) :]
  														if not key.startswith('''encode_proj.'''							):
  																		A__     :							Tuple   				='''bert_model.''' + key
  														A__     :							str   				=value
  						encoder.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR reader from {self.src_file}'							)
  						A__     :							Dict   				=load_states_from_checkpoint(self.src_file							)
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[Any]   				={
  						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
  						}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith('''encoder.'''							) and not key.startswith('''encoder.encode_proj'''							):
  														A__     :							List[Any]   				='''encoder.bert_model.''' + key[len('''encoder.'''							) :]
  										A__     :							Optional[int]   				=value
  						model.span_predictor.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
  				A__     :							Optional[int]   				=Path(snake_case_	)
  				dest_dir.mkdir(exist_ok=snake_case_	)
  
  				A__     :							Tuple   				=DPRState.from_type(snake_case_,	src_file=snake_case_	)
  				A__     :							str   				=dpr_state.load_dpr_model()
  				model.save_pretrained(snake_case_	)
  				model.from_pretrained(snake_case_	)  # sanity check
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Optional[Any]				   =			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  						)
  						parser.add_argument(
  						    "--src",
  						    type=str,
  						    help=(
  						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
  						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
  						        " 'retriever' checkpoints."
  						    ),
  						)
  						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
  						__lowerCamelCase       :       Tuple				   =			parser.parse_args()
  
  						__lowerCamelCase       :       Optional[Any]				   =			Path(args.src)
  						__lowerCamelCase       :       Optional[int]				   =			F"converted-{src_file.name}" if args.dest is None else args.dest
  						__lowerCamelCase       :       Optional[Any]				   =			Path(dest_dir)
  						assert src_file.exists()
  						assert (
  						    args.type is not None
  						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  						convert(args.type, src_file, dest_dir)
  
  
  
  