




"""simple docstring"""

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowerCamelCase								=						logging.get_logger(__name__)

__lowerCamelCase								=						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}



def 				a (						__snake_case     :				str,    __snake_case     :				Union[str, Any],    __snake_case     :				int,    __snake_case     :				int,    __snake_case     :				List[Any]  ):



    '''simple docstring'''





    for attribute in key.split('''.'''  ):
        UpperCAmelCase_					:int      			=      getattr(__snake_case,    __snake_case  )

    if weight_type is not None:
        UpperCAmelCase_					:Optional[Any]      			=      getattr(__snake_case,    __snake_case  ).shape
    else:
        UpperCAmelCase_					:str      			=      hf_pointer.shape

    assert hf_shape == value.shape, (
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'
    )

    if weight_type == "weight":
        UpperCAmelCase_					:int      			=      value
    elif weight_type == "weight_g":
        UpperCAmelCase_					:Union[str, Any]      			=      value
    elif weight_type == "weight_v":
        UpperCAmelCase_					:str      			=      value
    elif weight_type == "bias":
        UpperCAmelCase_					:Optional[int]      			=      value
    else:
        UpperCAmelCase_					:int      			=      value

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'  )



def 				a (						__snake_case     :				Optional[int],    __snake_case     :				Union[str, Any]  ):



    '''simple docstring'''





    UpperCAmelCase_					:Dict      			=      []
    UpperCAmelCase_					:Union[str, Any]      			=      fairseq_model.state_dict()

    UpperCAmelCase_					:Optional[int]      			=      hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase_					:Dict      			=      False
        if "conv_layers" in name:
            load_conv_layer(
                __snake_case,    __snake_case,    __snake_case,    __snake_case,    hf_model.config.feat_extract_norm == '''group''',    )
            UpperCAmelCase_					:str      			=      True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase_					:List[str]      			=      mapped_key

                if key in name:
                    UpperCAmelCase_					:str      			=      True
                    if "*" in mapped_key:
                        UpperCAmelCase_					:Optional[int]      			=      name.split(__snake_case  )[0].split('''.'''  )[-2]
                        UpperCAmelCase_					:Tuple      			=      mapped_key.replace('''*''',    __snake_case  )
                    if "weight_g" in name:
                        UpperCAmelCase_					:Tuple      			=      'weight_g'
                    elif "weight_v" in name:
                        UpperCAmelCase_					:Union[str, Any]      			=      'weight_v'
                    elif "weight" in name:
                        UpperCAmelCase_					:Any      			=      'weight'
                    elif "bias" in name:
                        UpperCAmelCase_					:List[str]      			=      'bias'
                    else:
                        UpperCAmelCase_					:Optional[int]      			=      None
                    set_recursively(__snake_case,    __snake_case,    __snake_case,    __snake_case,    __snake_case  )
                continue
        if not is_used:
            unused_weights.append(__snake_case  )

    logger.warning(f'Unused weights: {unused_weights}'  )



def 				a (						__snake_case     :				List[Any],    __snake_case     :				List[str],    __snake_case     :				Tuple,    __snake_case     :				Optional[Any],    __snake_case     :				str  ):



    '''simple docstring'''





    UpperCAmelCase_					:int      			=      full_name.split('''conv_layers.'''  )[-1]
    UpperCAmelCase_					:int      			=      name.split('''.'''  )
    UpperCAmelCase_					:Tuple      			=      int(items[0]  )
    UpperCAmelCase_					:str      			=      int(items[1]  )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            UpperCAmelCase_					:Union[str, Any]      			=      value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'  )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
            )
            UpperCAmelCase_					:List[str]      			=      value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'  )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                " found."
            )
            UpperCAmelCase_					:Dict      			=      value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            UpperCAmelCase_					:Any      			=      value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
    else:
        unused_weights.append(__snake_case  )



def 				a (						__snake_case     :				List[str]  ):



    '''simple docstring'''





    UpperCAmelCase_					:List[str]      			=      HubertConfig()
    UpperCAmelCase_					:Dict      			=      model.config

    UpperCAmelCase_					:Union[str, Any]      			=      fs_config.activation_dropout
    UpperCAmelCase_					:Any      			=      False
    UpperCAmelCase_					:str      			=      fs_config.attention_dropout
    UpperCAmelCase_					:List[str]      			=      False
    UpperCAmelCase_					:Optional[Any]      			=      eval(fs_config.extractor_conv_feature_layers  )
    UpperCAmelCase_					:Tuple      			=      [x[0] for x in conv_layers]
    UpperCAmelCase_					:Tuple      			=      [x[1] for x in conv_layers]
    UpperCAmelCase_					:Optional[Any]      			=      [x[2] for x in conv_layers]
    UpperCAmelCase_					:Any      			=      'gelu'
    UpperCAmelCase_					:Any      			=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
    UpperCAmelCase_					:List[str]      			=      False
    UpperCAmelCase_					:List[str]      			=      0.0
    UpperCAmelCase_					:int      			=      0.0
    UpperCAmelCase_					:List[str]      			=      fs_config.activation_fn
    UpperCAmelCase_					:List[Any]      			=      fs_config.dropout
    UpperCAmelCase_					:str      			=      fs_config.encoder_embed_dim
    UpperCAmelCase_					:Optional[int]      			=      0.02
    UpperCAmelCase_					:Union[str, Any]      			=      fs_config.encoder_ffn_embed_dim
    UpperCAmelCase_					:Optional[int]      			=      1E-5
    UpperCAmelCase_					:Tuple      			=      0.0
    UpperCAmelCase_					:int      			=      fs_config.encoder_attention_heads
    UpperCAmelCase_					:Tuple      			=      fs_config.conv_pos_groups
    UpperCAmelCase_					:Optional[int]      			=      fs_config.conv_pos
    UpperCAmelCase_					:Optional[Any]      			=      len(__snake_case  )
    UpperCAmelCase_					:int      			=      fs_config.encoder_layers

    return config



@torch.no_grad()
def 				a (						__snake_case     :				Any,    __snake_case     :				List[str]=None  ):



    '''simple docstring'''





    UpperCAmelCase_					:Any      			=      distilhubert().model.model

    if config_path is not None:
        UpperCAmelCase_					:Any      			=      HubertConfig.from_pretrained(__snake_case  )
    else:
        UpperCAmelCase_					:Union[str, Any]      			=      convert_config(__snake_case  )
    UpperCAmelCase_					:Dict      			=      model.eval()

    UpperCAmelCase_					:Optional[Any]      			=      WavaVecaFeatureExtractor(
        feature_size=1,    sampling_rate=16000,    padding_value=0,    do_normalize=__snake_case,    return_attention_mask=__snake_case,    )
    UpperCAmelCase_					:Tuple      			=      HubertModel(__snake_case  )

    recursively_load_weights(__snake_case,    __snake_case  )

    feature_extractor.save_pretrained(__snake_case  )
    hf_model.save_pretrained(__snake_case  )


if __name__ == "__main__":
      __lowerCamelCase								=						argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      __lowerCamelCase								=						parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








"""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 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				None  ):



				'''simple docstring'''





				...



@overload
def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor  ):



				'''simple docstring'''





				...



def 				a (						__snake_case     :				List[str],    __snake_case     :				Optional[int],    __snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      aatype == rc.restype_order['''G''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CA''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CB''']
				UpperCAmelCase_					: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:
								UpperCAmelCase_					:Any      			=      torch.where(
								    __snake_case,    all_atom_masks[..., ca_idx],    all_atom_masks[..., cb_idx],    )
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta



def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      batched_gather(
				    __snake_case,    batch['''residx_atom37_to_atom14'''],    dim=-2,    no_batch_dims=len(atomaa.shape[:-2]  ),    )

				UpperCAmelCase_					:Optional[Any]      			=      atomaa_data * batch['''atom37_atom_exists'''][..., None]

				return atomaa_data



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      template_feats['''template_aatype''']
				UpperCAmelCase_					:Union[str, Any]      			=      template_feats['''template_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_alt_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_torsion_angles_mask''']
				UpperCAmelCase_					:int      			=      torch.cat(
				    [
				        nn.functional.one_hot(__snake_case,    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 				a (						__snake_case     :				Dict[str, torch.Tensor],    __snake_case     :				torch.types.Number,    __snake_case     :				torch.types.Number,    __snake_case     :				int,    __snake_case     :				bool = False,    __snake_case     :				float = 1E-20,    __snake_case     :				float = 1E8,    ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      batch['''template_pseudo_beta_mask''']
				UpperCAmelCase_					:Any      			=      template_mask[..., None] * template_mask[..., None, :]

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

				UpperCAmelCase_					:List[str]      			=      [dgram, template_mask_ad[..., None]]

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

				UpperCAmelCase_					:Optional[Any]      			=      batch['''template_aatype'''].shape[-1]
				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2],    __snake_case,    -1,    -1  )  )
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2],    -1,    __snake_case,    -1  )  )

				UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
				UpperCAmelCase_					:str      			=      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=__snake_case,    )
				UpperCAmelCase_					:Dict      			=      rigids.get_trans()[..., None, :, :]
				UpperCAmelCase_					:Dict      			=      rigids[..., None].invert_apply(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      torch.rsqrt(eps + torch.sum(rigid_vec**2,    dim=-1  )  )

				UpperCAmelCase_					:Optional[int]      			=      batch['''template_all_atom_mask''']
				UpperCAmelCase_					:Tuple      			=      t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
				UpperCAmelCase_					:int      			=      template_mask[..., None] * template_mask[..., None, :]

				UpperCAmelCase_					:str      			=      inv_distance_scalar * template_mask_ad
				UpperCAmelCase_					:List[Any]      			=      rigid_vec * inv_distance_scalar[..., None]

				if not use_unit_vector:
								UpperCAmelCase_					:List[Any]      			=      unit_vector * 0.0

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

				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case,    dim=-1  )
				UpperCAmelCase_					:Optional[Any]      			=      act * template_mask_ad[..., None]

				return act



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(batch['''extra_msa'''],    23  )
				UpperCAmelCase_					:Union[str, Any]      			=      [
				    msa_ahot,
				    batch['''extra_has_deletion'''].unsqueeze(-1  ),
				    batch['''extra_deletion_value'''].unsqueeze(-1  ),
				]
				return torch.cat(__snake_case,    dim=-1  )



def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      rrgdf[aatype, ...]

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

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

				# [*, N, 8, 2]
				UpperCAmelCase_					: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.

				UpperCAmelCase_					:Any      			=      alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
				UpperCAmelCase_					:Optional[Any]      			=      1
				UpperCAmelCase_					:int      			=      alpha[..., 1]
				UpperCAmelCase_					:Optional[int]      			=      -alpha[..., 0]
				UpperCAmelCase_					:int      			=      alpha

				UpperCAmelCase_					:List[str]      			=      default_r.compose(Rigid(Rotation(rot_mats=__snake_case  ),    __snake_case  )  )

				UpperCAmelCase_					:List[str]      			=      all_frames[..., 5]
				UpperCAmelCase_					:str      			=      all_frames[..., 6]
				UpperCAmelCase_					:List[Any]      			=      all_frames[..., 7]

				UpperCAmelCase_					:Tuple      			=      all_frames[..., 4]
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      chia_frame_to_bb.compose(__snake_case  )

				UpperCAmelCase_					:List[str]      			=      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,    )

				UpperCAmelCase_					:Any      			=      r[..., None].compose(__snake_case  )

				return all_frames_to_global





def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      group_idx[aatype, ...]

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

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

				# [*, N, 14]
				UpperCAmelCase_					:List[Any]      			=      t_atoms_to_global.map_tensor_fn(lambda __snake_case  : torch.sum(__snake_case,    dim=-1  )  )

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

				# [*, N, 14, 3]
				UpperCAmelCase_					:List[Any]      			=      lit_positions[aatype, ...]
				UpperCAmelCase_					:Optional[int]      			=      t_atoms_to_global.apply(__snake_case  )
				UpperCAmelCase_					:int      			=      pred_positions * atom_mask

				return pred_positions


