
'''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     snake_case__							(      _A: Tuple				,       _A: Dict				,       _A: Dict			)   ->     List[str]:

		'''simple docstring'''


		...






@overload
def     snake_case__							(      _A: List[str]				,       _A: str				,       _A: Union[str, Any]			)   ->     Any:

		'''simple docstring'''


		...






def     snake_case__							(      _A: Optional[int]				,       _A: Dict				,       _A: Tuple			)   ->     int:

		'''simple docstring'''


		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     snake_case__							(      _A: int				,       _A: Optional[int]			)   ->     Optional[int]:

		'''simple docstring'''


		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     snake_case__							(      _A: List[str]			)   ->     str:

		'''simple docstring'''


		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     snake_case__							(      _A: str				,       _A: Union[str, Any]				,       _A: List[Any]				,       _A: int				,       _A: int = False				,       _A: Union[str, Any] = 1e-20				,       _A: List[Any] = 1e8				,       )   ->     int:

		'''simple docstring'''


		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     snake_case__							(      _A: str			)   ->     Any:

		'''simple docstring'''


		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     snake_case__							(      _A: Optional[Any]				,       _A: Any				,       _A: List[str]				,       _A: Optional[Any]				,       )   ->     Dict:

		'''simple docstring'''


		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     snake_case__							(      _A: List[str]				,       _A: List[str]				,       _A: str				,       _A: Union[str, Any]				,       _A: List[Any]				,       _A: int				,       )   ->     int:

		'''simple docstring'''


		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 _A			: 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 torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




