





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__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  torch.Tensor:
		...


@overload
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Tuple[torch.Tensor, torch.Tensor]:
		...


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[Any]:
		lowerCamelCase :			Optional[Any]    		=aatype == rc.restype_order['''G''']
		lowerCamelCase :			str    		=rc.atom_order['''CA''']
		lowerCamelCase :			List[Any]    		=rc.atom_order['''CB''']
		lowerCamelCase :			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:
				lowerCamelCase :			str    		=torch.where(
				    SCREAMING_SNAKE_CASE_				,							all_atom_masks[..., ca_idx]				,							all_atom_masks[..., cb_idx]				,							)
				return pseudo_beta, pseudo_beta_mask
		else:
				return pseudo_beta


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  torch.Tensor:
		lowerCamelCase :			List[Any]    		=batched_gather(
		    SCREAMING_SNAKE_CASE_				,							batch['''residx_atom37_to_atom14''']				,							dim=-2				,							no_batch_dims=len(atomaa.shape[:-2]		)				,							)

		lowerCamelCase :			str    		=atomaa_data * batch['''atom37_atom_exists'''][..., None]

		return atomaa_data


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  torch.Tensor:
		lowerCamelCase :			List[Any]    		=template_feats['''template_aatype''']
		lowerCamelCase :			Union[str, Any]    		=template_feats['''template_torsion_angles_sin_cos''']
		lowerCamelCase :			List[Any]    		=template_feats['''template_alt_torsion_angles_sin_cos''']
		lowerCamelCase :			Tuple    		=template_feats['''template_torsion_angles_mask''']
		lowerCamelCase :			Tuple    		=torch.cat(
		    [
		        nn.functional.one_hot(SCREAMING_SNAKE_CASE_				,							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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ = False				,							SCREAMING_SNAKE_CASE_ = 1E-2_0				,							SCREAMING_SNAKE_CASE_ = 1E8				,							) ->  torch.Tensor:
		lowerCamelCase :			str    		=batch['''template_pseudo_beta_mask''']
		lowerCamelCase :			str    		=template_mask[..., None] * template_mask[..., None, :]

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

		lowerCamelCase :			Optional[int]    		=[dgram, template_mask_ad[..., None]]

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

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

		lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			str    		=[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
		lowerCamelCase :			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=SCREAMING_SNAKE_CASE_				,							)
		lowerCamelCase :			List[str]    		=rigids.get_trans()[..., None, :, :]
		lowerCamelCase :			str    		=rigids[..., None].invert_apply(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Union[str, Any]    		=torch.rsqrt(eps + torch.sum(rigid_vec**2				,							dim=-1		)		)

		lowerCamelCase :			Union[str, Any]    		=batch['''template_all_atom_mask''']
		lowerCamelCase :			Any    		=t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
		lowerCamelCase :			Optional[Any]    		=template_mask[..., None] * template_mask[..., None, :]

		lowerCamelCase :			List[Any]    		=inv_distance_scalar * template_mask_ad
		lowerCamelCase :			Tuple    		=rigid_vec * inv_distance_scalar[..., None]

		if not use_unit_vector:
				lowerCamelCase :			int    		=unit_vector * 0.0

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

		lowerCamelCase :			Optional[Any]    		=torch.cat(SCREAMING_SNAKE_CASE_				,							dim=-1		)
		lowerCamelCase :			Optional[Any]    		=act * template_mask_ad[..., None]

		return act


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  torch.Tensor:
		lowerCamelCase :			torch.LongTensor    		=nn.functional.one_hot(batch['''extra_msa''']				,							2_3		)
		lowerCamelCase :			Any    		=[
		    msa_ahot,
		    batch['''extra_has_deletion'''].unsqueeze(-1		),
		    batch['''extra_deletion_value'''].unsqueeze(-1		),
		]
		return torch.cat(SCREAMING_SNAKE_CASE_				,							dim=-1		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							) ->  Rigid:
		# [*, N, 8, 4, 4]
		lowerCamelCase :			Optional[int]    		=rrgdf[aatype, ...]

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

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

		# [*, N, 8, 2]
		lowerCamelCase :			Union[str, Any]    		=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 :			Tuple    		=alpha.new_zeros(default_r.get_rots().get_rot_mats().shape		)
		lowerCamelCase :			List[Any]    		=1
		lowerCamelCase :			Tuple    		=alpha[..., 1]
		lowerCamelCase :			List[Any]    		=-alpha[..., 0]
		lowerCamelCase :			Dict    		=alpha

		lowerCamelCase :			List[str]    		=default_r.compose(Rigid(Rotation(rot_mats=SCREAMING_SNAKE_CASE_		)				,							SCREAMING_SNAKE_CASE_		)		)

		lowerCamelCase :			Dict    		=all_frames[..., 5]
		lowerCamelCase :			Tuple    		=all_frames[..., 6]
		lowerCamelCase :			Any    		=all_frames[..., 7]

		lowerCamelCase :			Optional[int]    		=all_frames[..., 4]
		lowerCamelCase :			Union[str, Any]    		=chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[Any]    		=chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=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 :			Tuple    		=r[..., None].compose(SCREAMING_SNAKE_CASE_		)

		return all_frames_to_global



def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							) ->  torch.Tensor:
		# [*, N, 14]
		lowerCamelCase :			Any    		=group_idx[aatype, ...]

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

		# [*, N, 14, 8]
		lowerCamelCase :			Optional[Any]    		=r[..., None, :] * group_mask_one_hot

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

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

		# [*, N, 14, 3]
		lowerCamelCase :			Any    		=lit_positions[aatype, ...]
		lowerCamelCase :			int    		=t_atoms_to_global.apply(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Tuple    		=pred_positions * atom_mask

		return pred_positions








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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

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

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

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


if __name__ == "__main__":
				snake_case_				=      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.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

