
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       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[int]      )					->						List[Any]:
				...




@overload
def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]      )					->						List[str]:
				...




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						List[Any]:
				__lowercase								= aatype == rc.restype_order['G']
				__lowercase								= rc.atom_order['CA']
				__lowercase								= rc.atom_order['CB']
				__lowercase								= 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:
								__lowercase								= torch.where(
								    __A   ,							all_atom_masks[..., ca_idx]   ,							all_atom_masks[..., cb_idx]   ,							)
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Union[str, Any]:
				__lowercase								= batched_gather(
				    __A   ,							batch['residx_atom37_to_atom14']   ,							dim=-2   ,							no_batch_dims=len(atomaa.shape[:-2]      )   ,							)

				__lowercase								= atomaa_data * batch['atom37_atom_exists'][..., None]

				return atomaa_data




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int      )					->						str:
				__lowercase								= template_feats['template_aatype']
				__lowercase								= template_feats['template_torsion_angles_sin_cos']
				__lowercase								= template_feats['template_alt_torsion_angles_sin_cos']
				__lowercase								= template_feats['template_torsion_angles_mask']
				__lowercase								= torch.cat(
				    [
				        nn.functional.one_hot(__A   ,							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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Dict = False   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any] = 1E-20   ,							SCREAMING_SNAKE_CASE	:				int = 1E8   ,							)					->						Any:
				__lowercase								= batch['template_pseudo_beta_mask']
				__lowercase								= template_mask[..., None] * template_mask[..., None, :]

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

				__lowercase								= [dgram, template_mask_ad[..., None]]

				__lowercase								= nn.functional.one_hot(
				    batch['template_aatype']   ,							rc.restype_num + 2   ,							)

				__lowercase								= batch['template_aatype'].shape[-1]
				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]   ,							__A   ,							-1   ,							-1      )      )
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]   ,							-1   ,							__A   ,							-1      )      )

				__lowercase		, __lowercase		, __lowercase								= [rc.atom_order[a] for a in ['N', 'CA', 'C']]
				__lowercase								= 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=__A   ,							)
				__lowercase								= rigids.get_trans()[..., None, :, :]
				__lowercase								= rigids[..., None].invert_apply(__A      )

				__lowercase								= torch.rsqrt(eps + torch.sum(rigid_vec**2   ,							dim=-1      )      )

				__lowercase								= batch['template_all_atom_mask']
				__lowercase								= t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
				__lowercase								= template_mask[..., None] * template_mask[..., None, :]

				__lowercase								= inv_distance_scalar * template_mask_ad
				__lowercase								= rigid_vec * inv_distance_scalar[..., None]

				if not use_unit_vector:
								__lowercase								= unit_vector * 0.0

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

				__lowercase								= torch.cat(__A   ,							dim=-1      )
				__lowercase								= act * template_mask_ad[..., None]

				return act




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						List[str]:
				__lowercase								= nn.functional.one_hot(batch['extra_msa']   ,							23      )
				__lowercase								= [
				    msa_ahot,
				    batch['extra_has_deletion'].unsqueeze(-1      ),
				    batch['extra_deletion_value'].unsqueeze(-1      ),
				]
				return torch.cat(__A   ,							dim=-1      )




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							)					->						Tuple:
				__lowercase								= rrgdf[aatype, ...]

				# [*, N, 8] transformations, i.e.
				#   One [*, N, 8, 3, 3] rotation matrix and
				#   One [*, N, 8, 3]    translation matrix
				__lowercase								= r.from_tensor_axa(__A      )

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

				# [*, N, 8, 2]
				__lowercase								= 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.

				__lowercase								= alpha.new_zeros(default_r.get_rots().get_rot_mats().shape      )
				__lowercase								= 1
				__lowercase								= alpha[..., 1]
				__lowercase								= -alpha[..., 0]
				__lowercase								= alpha

				__lowercase								= default_r.compose(Rigid(Rotation(rot_mats=__A      )   ,							__A      )      )

				__lowercase								= all_frames[..., 5]
				__lowercase								= all_frames[..., 6]
				__lowercase								= all_frames[..., 7]

				__lowercase								= all_frames[..., 4]
				__lowercase								= chia_frame_to_bb.compose(__A      )
				__lowercase								= chia_frame_to_bb.compose(__A      )
				__lowercase								= chia_frame_to_bb.compose(__A      )

				__lowercase								= 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   ,							)

				__lowercase								= r[..., None].compose(__A      )

				return all_frames_to_global




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							)					->						Any:
				__lowercase								= group_idx[aatype, ...]

				# [*, N, 14, 8]
				__lowercase								= nn.functional.one_hot(
				    __A   ,							num_classes=default_frames.shape[-3]   ,							)

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

				# [*, N, 14]
				__lowercase								= t_atoms_to_global.map_tensor_fn(lambda SCREAMING_SNAKE_CASE      : torch.sum(__A   ,							dim=-1      )      )

				# [*, N, 14, 1]
				__lowercase								= atom_mask[aatype, ...].unsqueeze(-1      )

				# [*, N, 14, 3]
				__lowercase								= lit_positions[aatype, ...]
				__lowercase								= t_atoms_to_global.apply(__A      )
				__lowercase								= pred_positions * atom_mask

				return pred_positions







import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[Any]:
				if isinstance(SCREAMING_SNAKE_CASE   ,							(list, tuple)      ):
								return honor_type(SCREAMING_SNAKE_CASE   ,							(move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for t in state)      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return type(SCREAMING_SNAKE_CASE      )({k: move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for k, v in state.items()}      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor      ):
								return state.to(SCREAMING_SNAKE_CASE      )
				return state
class      A__       (						torch.optim.Optimizer    ):





							def __init__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[int]=True					,			_UpperCAmelCase  :       int=None    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= optimizer
											__lowercase								= scaler
											__lowercase								= AcceleratorState()
											__lowercase								= GradientState()
											__lowercase								= device_placement
											__lowercase								= False
											__lowercase								= None

											# Handle device placement
											if device_placement:
															__lowercase								= self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															else:
																			__lowercase								= move_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															self.optimizer.load_state_dict(_UpperCAmelCase    )





							@property
							def       a__			(			self  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.optimizer.state





							@state.setter
							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= state





							@property
							def       a__			(			self  :       str    )						->		int:



											"""simple docstring"""


											return self.optimizer.param_groups





							@param_groups.setter
							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= param_groups





							@property
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											return self.optimizer.defaults





							@defaults.setter
							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int    )						->		Any:



											"""simple docstring"""


											__lowercase								= defaults





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[Any]    )						->		Any:



											"""simple docstring"""


											self.optimizer.add_param_group(_UpperCAmelCase    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
											self.optimizer.load_state_dict(_UpperCAmelCase    )





							def       a__			(			self  :       str    )						->		Any:



											"""simple docstring"""


											return self.optimizer.state_dict()





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[str]=None    )						->		Tuple:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															__lowercase								= 'set_to_none' in inspect.signature(self.optimizer.zero_grad    ).parameters
															if accept_arg:
																			if set_to_none is None:
																							__lowercase								= False
																			self.optimizer.zero_grad(set_to_none=_UpperCAmelCase    )
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'    )
																			self.optimizer.zero_grad()





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Optional[Any]=None    )						->		int:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			__lowercase								= {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer					,			optimizer_args=_UpperCAmelCase    )
															elif self.scaler is not None:
																			__lowercase								= False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							__lowercase								= self.scaler.get_scale()
																							__lowercase								= True
																			self.scaler.step(self.optimizer					,			_UpperCAmelCase    )
																			self.scaler.update()
																			__lowercase								= self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							__lowercase								= scale_after < self._last_scale
																			__lowercase								= scale_after

															else:
																			self.optimizer.step(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int    )						->		Tuple:



											"""simple docstring"""


											for param_group in self.optimizer.param_groups:
															__lowercase								= [parameters_map.get(_UpperCAmelCase					,			_UpperCAmelCase    ) for p in param_group['params']]





							@property
							def       a__			(			self  :       Optional[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'					,			_UpperCAmelCase					,			)
											return self._is_overflow





							@property
							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											return self._is_overflow





							def __getstate__(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											return self.__dict__.copy()





							def __setstate__(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Any:



											"""simple docstring"""


											self.__dict__.update(_UpperCAmelCase    )





