





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A__:  str       					=					logging.get_logger(__name__)

A__:  Optional[int]       					=					{
    '''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 							lowerCAmelCase_				(    A_  ,A_  ,A_  ,A_  ,A_):







    for attribute in key.split("."):
        UpperCamelCase__:					Optional[int]												=      getattr(__UpperCamelCase  ,__UpperCamelCase)

    if weight_type is not None:
        UpperCamelCase__:					Any												=      getattr(__UpperCamelCase  ,__UpperCamelCase).shape
    else:
        UpperCamelCase__:					Tuple												=      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__:					List[str]												=      value
    elif weight_type == "weight_g":
        UpperCamelCase__:					Dict												=      value
    elif weight_type == "weight_v":
        UpperCamelCase__:					Dict												=      value
    elif weight_type == "bias":
        UpperCamelCase__:					Optional[Any]												=      value
    else:
        UpperCamelCase__:					Dict												=      value

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



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					Tuple												=      []
    UpperCamelCase__:					Dict												=      fairseq_model.state_dict()

    UpperCamelCase__:					Union[str, Any]												=      hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase__:					int												=      False
        if "conv_layers" in name:
            load_conv_layer(
                __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,hf_model.config.feat_extract_norm == "group"  ,)
            UpperCamelCase__:					List[str]												=      True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase__:					Any												=      mapped_key

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

    logger.warning(F"Unused weights: {unused_weights}")



def 							lowerCAmelCase_				(    A_  ,A_  ,A_  ,A_  ,A_):
    UpperCamelCase__:					List[str]												=      full_name.split("conv_layers.")[-1]
    UpperCamelCase__:					Any												=      name.split(".")
    UpperCamelCase__:					Dict												=      int(items[0])
    UpperCamelCase__:					List[Any]												=      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__:					List[str]												=      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__:					int												=      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__:					List[str]												=      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__:					Optional[int]												=      value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
    else:
        unused_weights.append(__UpperCamelCase)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					List[str]												=      HubertConfig()
    UpperCamelCase__:					Optional[Any]												=      model.config

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

    return config



@torch.no_grad()
def 							lowerCAmelCase_				(    A_  ,A_=None):
    UpperCamelCase__:					Union[str, Any]												=      distilhubert().model.model

    if config_path is not None:
        UpperCamelCase__:					Tuple												=      HubertConfig.from_pretrained(__UpperCamelCase)
    else:
        UpperCamelCase__:					int												=      convert_config(__UpperCamelCase)
    UpperCamelCase__:					Optional[int]												=      model.eval()

    UpperCamelCase__:					Optional[Any]												=      WavaVecaFeatureExtractor(
        feature_size=1  ,sampling_rate=1_60_00  ,padding_value=0  ,do_normalize=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,)
    UpperCamelCase__:					List[str]												=      HubertModel(__UpperCamelCase)

    recursively_load_weights(__UpperCamelCase  ,__UpperCamelCase)

    feature_extractor.save_pretrained(__UpperCamelCase)
    hf_model.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
   A__:  int       					=					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''')
   A__:  Dict       					=					parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

