




"""simple docstring"""

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     Optional[int]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     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 						lowercase_ ( _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	for attribute in key.split('''.'''		):
		A_					:			Tuple						=						getattr(__lowercase					, __lowercase		)

	if weight_type is not None:
		A_					:			Optional[int]						=						getattr(__lowercase					, __lowercase		).shape
	else:
		A_					:			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":
		A_					:			Tuple						=						value
	elif weight_type == "weight_g":
		A_					:			Any						=						value
	elif weight_type == "weight_v":
		A_					:			Tuple						=						value
	elif weight_type == "bias":
		A_					:			List[Any]						=						value
	else:
		A_					:			int						=						value

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

def 						lowercase_ ( _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			List[Any]						=						[]
	A_					:			Optional[Any]						=						fairseq_model.state_dict()

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

	for name, value in fairseq_dict.items():
		A_					:			List[Any]						=						False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowercase					, __lowercase					, __lowercase					, __lowercase					, hf_model.config.feat_extract_norm == '''group'''					, )
			A_					:			Optional[Any]						=						True
		else:
			for key, mapped_key in MAPPING.items():
				A_					:			Union[str, Any]						=						mapped_key

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

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

def 						lowercase_ ( _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Tuple						=						full_name.split('''conv_layers.'''		)[-1]
	A_					:			Dict						=						name.split('''.'''		)
	A_					:			Any						=						int(items[0]		)
	A_					:			int						=						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."""
			)
			A_					:			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."""
			)
			A_					:			Dict						=						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."
			)
			A_					:			Union[str, Any]						=						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."""
			)
			A_					:			List[str]						=						value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
	else:
		unused_weights.append(__lowercase		)

def 						lowercase_ ( _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Any						=						HubertConfig()
	A_					:			List[Any]						=						model.config

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

	return config

@torch.no_grad()
def 						lowercase_ ( _UpperCAmelCase					, _UpperCAmelCase=None		):




	"""simple docstring"""






	A_					:			Tuple						=						distilhubert().model.model

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

	A_					:			Optional[int]						=						WavaVecaFeatureExtractor(
	    feature_size=1					, sampling_rate=1_6000					, padding_value=0					, do_normalize=__lowercase					, return_attention_mask=__lowercase					, )
	A_					:			Optional[Any]						=						HubertModel(__lowercase		)

	recursively_load_weights(__lowercase					, __lowercase		)

	feature_extractor.save_pretrained(__lowercase		)
	hf_model.save_pretrained(__lowercase		)


if __name__ == "__main__":
		_lowerCamelCase					:     Dict				       =    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					:     Optional[Any]				       =    parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)











"""simple docstring"""

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





