





'''simple docstring'''


import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


lowerCAmelCase_ : List[str]	  =       Mapping[str, np.ndarray]
lowerCAmelCase_ : Dict	  =       Mapping[str, Any]  # Is a nested dict.
lowerCAmelCase_ : Dict	  =       0.01






@dataclasses.dataclass(frozen=a__  )
class    UpperCamelCase__	:
	lowerCAmelCase__       :      Union[str, Any]										=    42  # [num_res, num_atom_type, 3]

	# Amino-acid type for each residue represented as an integer between 0 and
	# 20, where 20 is 'X'.
	lowerCAmelCase__       :      Optional[Any]										=    42  # [num_res]

	# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
	# is present and 0.0 if not. This should be used for loss masking.
	lowerCAmelCase__       :      str										=    42  # [num_res, num_atom_type]

	# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
	lowerCAmelCase__       :      Optional[int]										=    42  # [num_res]

	# B-factors, or temperature factors, of each residue (in sq. angstroms units),
	# representing the displacement of the residue from its ground truth mean
	# value.
	lowerCAmelCase__       :      Tuple										=    42  # [num_res, num_atom_type]

	# Chain indices for multi-chain predictions
	lowerCAmelCase__       :      List[Any]										=    None

	# Optional remark about the protein. Included as a comment in output PDB
	# files
	lowerCAmelCase__       :      Optional[int]										=    None

	# Templates used to generate this protein (prediction-only)
	lowerCAmelCase__       :      Optional[int]										=    None

	# Chain corresponding to each parent
	lowerCAmelCase__       :      Dict										=    None







def  _lowerCamelCase	(__lowerCamelCase	:  str  ) ->     Protein:
			a__	 =			r'(\[[A-Z]+\]\n)'
			a__	 =			[tag.strip() for tag in re.split(__snake_case       ,	__snake_case  ) if len(__snake_case  ) > 0]
			a__	 =			zip(tags[0::2]       ,	[l.split("\n"  ) for l in tags[1::2]]  )

			a__	 =			["N", "CA", "C"]
			a__	 =			None
			a__	 =			None
			a__	 =			None
			for g in groups:
						if "[PRIMARY]" == g[0]:
									a__	 =			g[1][0].strip()
									for i in range(len(__snake_case  )  ):
												if seq[i] not in residue_constants.restypes:
															a__	 =			'X'  # FIXME: strings are immutable
									a__	 =			np.array(
									    [residue_constants.restype_order.get(__snake_case       ,	residue_constants.restype_num  ) for res_symbol in seq]  )
						elif "[TERTIARY]" == g[0]:
									a__	 =			[]
									for axis in range(3  ):
												tertiary.append(list(map(__snake_case       ,	g[1][axis].split()  )  )  )
									a__	 =			np.array(__snake_case  )
									a__	 =			np.zeros((len(tertiary[0]  ) // 3, residue_constants.atom_type_num, 3)  ).astype(np.floataa  )
									for i, atom in enumerate(__snake_case  ):
												a__	 =			np.transpose(tertiary_np[:, i::3]  )
									atom_positions *= PICO_TO_ANGSTROM
						elif "[MASK]" == g[0]:
									a__	 =			np.array(list(map({"-": 0, "+": 1}.get       ,	g[1][0].strip()  )  )  )
									a__	 =			np.zeros(
									    (
									        len(__snake_case  ),
									        residue_constants.atom_type_num,
									    )  ).astype(np.floataa  )
									for i, atom in enumerate(__snake_case  ):
												a__	 =			1
									atom_mask *= mask[..., None]

			assert aatype is not None

			return Protein(
			    atom_positions=__snake_case       ,	atom_mask=__snake_case       ,	aatype=__snake_case       ,	residue_index=np.arange(len(__snake_case  )  )       ,	b_factors=__snake_case       ,	)







def  _lowerCamelCase	(__lowerCamelCase	:  Protein       ,	__lowerCamelCase	:  int = 0  ) ->     List[str]:
			a__	 =			[]

			a__	 =			prot.remark
			if remark is not None:
						pdb_headers.append(f'''REMARK {remark}'''  )

			a__	 =			prot.parents
			a__	 =			prot.parents_chain_index
			if parents is not None and parents_chain_index is not None:
						a__	 =			[p for i, p in zip(__snake_case       ,	__snake_case  ) if i == chain_id]

			if parents is None or len(__snake_case  ) == 0:
						a__	 =			['N/A']

			pdb_headers.append(f'''PARENT {" ".join(__snake_case  )}'''  )

			return pdb_headers







def  _lowerCamelCase	(__lowerCamelCase	:  Protein       ,	__lowerCamelCase	:  str  ) ->     str:
			a__	 =			[]
			a__	 =			pdb_str.split("\n"  )

			a__	 =			prot.remark
			if remark is not None:
						out_pdb_lines.append(f'''REMARK {remark}'''  )

			a__	 =			42
			if prot.parents is not None and len(prot.parents  ) > 0:
						a__	 =			[]
						if prot.parents_chain_index is not None:
									a__	 =			{}
									for p, i in zip(prot.parents       ,	prot.parents_chain_index  ):
												parent_dict.setdefault(str(__snake_case  )       ,	[]  )
												parent_dict[str(__snake_case  )].append(__snake_case  )

									a__	 =			max([int(__snake_case  ) for chain_idx in parent_dict]  )
									for i in range(max_idx + 1  ):
												a__	 =			parent_dict.get(str(__snake_case  )       ,	["N/A"]  )
												parents_per_chain.append(__snake_case  )
						else:
									parents_per_chain.append(list(prot.parents  )  )
			else:
						a__	 =			[['N/A']]

			def make_parent_line(__lowerCamelCase	:  Sequence[str]  ) -> str:
						return f'''PARENT {" ".join(__snake_case  )}'''

			out_pdb_lines.append(make_parent_line(parents_per_chain[0]  )  )

			a__	 =			0
			for i, l in enumerate(__snake_case  ):
						if "PARENT" not in l and "REMARK" not in l:
									out_pdb_lines.append(__snake_case  )
						if "TER" in l and "END" not in lines[i + 1]:
									chain_counter += 1
									if not chain_counter >= len(__snake_case  ):
												a__	 =			parents_per_chain[chain_counter]
									else:
												a__	 =			['N/A']

									out_pdb_lines.append(make_parent_line(__snake_case  )  )

			return "\n".join(__snake_case  )







def  _lowerCamelCase	(__lowerCamelCase	:  Protein  ) ->     str:
			a__	 =			residue_constants.restypes + ['X']

			def res_atoa(__lowerCamelCase	:  int  ) -> str:
						return residue_constants.restype_atoa.get(restypes[r]       ,	"UNK"  )

			a__	 =			residue_constants.atom_types

			a__	 =			[]

			a__	 =			prot.atom_mask
			a__	 =			prot.aatype
			a__	 =			prot.atom_positions
			a__	 =			prot.residue_index.astype(np.intaa  )
			a__	 =			prot.b_factors
			a__	 =			prot.chain_index

			if np.any(aatype > residue_constants.restype_num  ):
						raise ValueError("Invalid aatypes."  )

			a__	 =			get_pdb_headers(__snake_case  )
			if len(__snake_case  ) > 0:
						pdb_lines.extend(__snake_case  )

			a__	 =			aatype.shape[0]
			a__	 =			1
			a__	 =			0
			a__	 =			string.ascii_uppercase
			a__	 =			None
			# Add all atom sites.
			for i in range(__snake_case  ):
						a__	 =			res_atoa(aatype[i]  )
						for atom_name, pos, mask, b_factor in zip(__snake_case       ,	atom_positions[i]       ,	atom_mask[i]       ,	b_factors[i]  ):
									if mask < 0.5:
												continue

									a__	 =			'ATOM'
									a__	 =			atom_name if len(__snake_case  ) == 4 else f''' {atom_name}'''
									a__	 =			''
									a__	 =			''
									a__	 =			1.00
									a__	 =			atom_name[0]  # Protein supports only C, N, O, S, this works.
									a__	 =			''

									a__	 =			'A'
									if chain_index is not None:
												a__	 =			chain_tags[chain_index[i]]

									# PDB is a columnar format, every space matters here!
									a__	 =			(
									    f'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
									    f'''{res_name_a:>3} {chain_tag:>1}'''
									    f'''{residue_index[i]:>4}{insertion_code:>1}   '''
									    f'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
									    f'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
									    f'''{element:>2}{charge:>2}'''
									)
									pdb_lines.append(__snake_case  )
									atom_index += 1

						a__	 =			i == n - 1
						if chain_index is not None:
									if i != n - 1 and chain_index[i + 1] != prev_chain_index:
												a__	 =			True
												a__	 =			chain_index[i + 1]

						if should_terminate:
									# Close the chain.
									a__	 =			'TER'
									a__	 =			(
									    f'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]  ):>3} {chain_tag:>1}{residue_index[i]:>4}'''
									)
									pdb_lines.append(__snake_case  )
									atom_index += 1

									if i != n - 1:
												# "prev" is a misnomer here. This happens at the beginning of
												# each new chain.
												pdb_lines.extend(get_pdb_headers(__snake_case       ,	__snake_case  )  )

			pdb_lines.append("END"  )
			pdb_lines.append(""  )
			return "\n".join(__snake_case  )







def  _lowerCamelCase	(__lowerCamelCase	:  Protein  ) ->     np.ndarray:
			return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def  _lowerCamelCase	(__lowerCamelCase	:  FeatureDict       ,	__lowerCamelCase	:  ModelOutput       ,	__lowerCamelCase	:  Optional[np.ndarray] = None       ,	__lowerCamelCase	:  Optional[np.ndarray] = None       ,	__lowerCamelCase	:  Optional[str] = None       ,	__lowerCamelCase	:  Optional[Sequence[str]] = None       ,	__lowerCamelCase	:  Optional[Sequence[int]] = None       ,	) ->     Protein:
			return Protein(
			    aatype=features["aatype"]       ,	atom_positions=result["final_atom_positions"]       ,	atom_mask=result["final_atom_mask"]       ,	residue_index=features["residue_index"] + 1       ,	b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]  )       ,	chain_index=__snake_case       ,	remark=__snake_case       ,	parents=__snake_case       ,	parents_chain_index=__snake_case       ,	)










'''simple docstring'''




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : 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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowercase__       : Dict         =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Any					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					int						=			getattr(__snake_case					,				__snake_case     )

     if weight_type is not None:
          __A		:					Optional[int]						=			getattr(__snake_case					,				__snake_case     ).shape
     else:
          __A		:					List[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		:					Union[str, Any]						=			value
     elif weight_type == "weight_v":
          __A		:					Optional[Any]						=			value
     elif weight_type == "bias":
          __A		:					Optional[int]						=			value
     else:
          __A		:					Optional[int]						=			value

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






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					List[str]     )	->	List[Any]:
     __A		:					Optional[Any]						=			[]
     __A		:					Any						=			fairseq_model.state_dict()

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

     for name, value in fairseq_dict.items():
          __A		:					Union[str, Any]						=			False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case					,				__snake_case					,				__snake_case					,				__snake_case					,				hf_model.config.feat_extract_norm == 'group'					,				)
               __A		:					Optional[Any]						=			True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                         __A		:					int						=			True
                         if "*" in mapped_key:
                              __A		:					Any						=			name.split(__snake_case     )[0].split('.'     )[-2]
                              __A		:					List[Any]						=			mapped_key.replace('*'					,				__snake_case     )
                         if "weight_g" in name:
                              __A		:					Optional[Any]						=			'weight_g'
                         elif "weight_v" in name:
                              __A		:					Union[str, Any]						=			'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __A		:					Optional[Any]						=			'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __A		:					Tuple						=			'weight'
                         else:
                              __A		:					Dict						=			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 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[int]     )	->	int:
     __A		:					int						=			full_name.split('conv_layers.'     )[-1]
     __A		:					List[str]						=			name.split('.'     )
     __A		:					Optional[int]						=			int(items[0]     )
     __A		:					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.'
               )
               __A		:					Optional[int]						=			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		:					Union[str, Any]						=			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		:					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.'
               )
               __A		:					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     )






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Tuple=None     )	->	Any:
     # load the pre-trained checkpoints
     __A		:					List[str]						=			torch.load(__snake_case     )
     __A		:					Dict						=			WavLMConfigOrig(checkpoint['cfg']     )
     __A		:					Optional[int]						=			WavLMOrig(__snake_case     )
     model.load_state_dict(checkpoint['model']     )
     model.eval()

     if config_path is not None:
          __A		:					List[Any]						=			WavLMConfig.from_pretrained(__snake_case     )
     else:
          __A		:					Dict						=			WavLMConfig()

     __A		:					Optional[Any]						=			WavLMModel(__snake_case     )

     recursively_load_weights(__snake_case					,				__snake_case     )

     hf_wavlm.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Tuple         =       argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  lowercase__       : Any         =       parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)