
"""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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						...

@overload
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						...

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       aatype == rc.restype_order["""G"""]
						__lowerCAmelCase        =       rc.atom_order["""CA"""]
						__lowerCAmelCase        =       rc.atom_order["""CB"""]
						__lowerCAmelCase        =       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:
												__lowerCAmelCase        =       torch.where(
												    _lowerCAmelCase				,  all_atom_masks[..., ca_idx]				,  all_atom_masks[..., cb_idx]				,  )
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       batched_gather(
						    _lowerCAmelCase				,  batch["""residx_atom37_to_atom14"""]				,  dim=-2				,  no_batch_dims=len(atomaa.shape[:-2]		)				,  )

						__lowerCAmelCase        =       atomaa_data * batch["""atom37_atom_exists"""][..., None]

						return atomaa_data

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       template_feats["""template_aatype"""]
						__lowerCAmelCase        =       template_feats["""template_torsion_angles_sin_cos"""]
						__lowerCAmelCase        =       template_feats["""template_alt_torsion_angles_sin_cos"""]
						__lowerCAmelCase        =       template_feats["""template_torsion_angles_mask"""]
						__lowerCAmelCase        =       torch.cat(
						    [
						        nn.functional.one_hot(_lowerCAmelCase				,  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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = False				,  _lowerCAmelCase = 1E-20				,  _lowerCAmelCase = 1E8				,  ):
						__lowerCAmelCase        =       batch["""template_pseudo_beta_mask"""]
						__lowerCAmelCase        =       template_mask[..., None] * template_mask[..., None, :]

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

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

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

						__lowerCAmelCase        =       batch["""template_aatype"""].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]				,  _lowerCAmelCase				,  -1				,  -1		)		)
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]				,  -1				,  _lowerCAmelCase				,  -1		)		)

						__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       [rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
						__lowerCAmelCase        =       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=_lowerCAmelCase				,  )
						__lowerCAmelCase        =       rigids.get_trans()[..., None, :, :]
						__lowerCAmelCase        =       rigids[..., None].invert_apply(_lowerCAmelCase		)

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

						__lowerCAmelCase        =       batch["""template_all_atom_mask"""]
						__lowerCAmelCase        =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						__lowerCAmelCase        =       template_mask[..., None] * template_mask[..., None, :]

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

						if not use_unit_vector:
												__lowerCAmelCase        =       unit_vector * 0.0

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

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

						return act

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       nn.functional.one_hot(batch["""extra_msa"""]				,  23		)
						__lowerCAmelCase        =       [
						    msa_ahot,
						    batch["""extra_has_deletion"""].unsqueeze(-1		),
						    batch["""extra_deletion_value"""].unsqueeze(-1		),
						]
						return torch.cat(_lowerCAmelCase				,  dim=-1		)

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  ):
						# [*, N, 8, 4, 4]
						__lowerCAmelCase        =       rrgdf[aatype, ...]

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

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

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

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

						__lowerCAmelCase        =       default_r.compose(Rigid(Rotation(rot_mats=_lowerCAmelCase		)				,  _lowerCAmelCase		)		)

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

						__lowerCAmelCase        =       all_frames[..., 4]
						__lowerCAmelCase        =       chia_frame_to_bb.compose(_lowerCAmelCase		)
						__lowerCAmelCase        =       chia_frame_to_bb.compose(_lowerCAmelCase		)
						__lowerCAmelCase        =       chia_frame_to_bb.compose(_lowerCAmelCase		)

						__lowerCAmelCase        =       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				,  )

						__lowerCAmelCase        =       r[..., None].compose(_lowerCAmelCase		)

						return all_frames_to_global



def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  ):
						# [*, N, 14]
						__lowerCAmelCase        =       group_idx[aatype, ...]

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

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

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

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

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

						return pred_positions


"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
