



"""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


__snake_case							:     int   =					Mapping[str, np.ndarray]
__snake_case							:     Union[str, Any]   =					Mapping[str, Any]  # Is a nested dict.
__snake_case							:     Any   =					0.01



@dataclasses.dataclass(frozen=__SCREAMING_SNAKE_CASE							)
class     A__     :




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			4_2  # [num_res, num_atom_type, 3]

 # Amino-acid type for each residue represented as an integer between 0 and
 # 20, where 20 is 'X'.
 SCREAMING_SNAKE_CASE					   =			4_2  # [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.
 SCREAMING_SNAKE_CASE					   =			4_2  # [num_res, num_atom_type]

 # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
 SCREAMING_SNAKE_CASE					   =			4_2  # [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.
 SCREAMING_SNAKE_CASE					   =			4_2  # [num_res, num_atom_type]

 # Chain indices for multi-chain predictions
 SCREAMING_SNAKE_CASE					   =			None

 # Optional remark about the protein. Included as a comment in output PDB
 # files
 SCREAMING_SNAKE_CASE					   =			None

 # Templates used to generate this protein (prediction-only)
 SCREAMING_SNAKE_CASE					   =			None

 # Chain corresponding to each parent
 SCREAMING_SNAKE_CASE					   =			None






def     _lowercase       (						__snake_case			)		->     Protein:
   __lowerCAmelCase   :						List[Any]				= r"(\[[A-Z]+\]\n)"
   __lowerCAmelCase   :						List[str]				= [tag.strip() for tag in re.split(__snake_case      ,__snake_case			) if len(__snake_case			) > 0]
   __lowerCAmelCase   :						Iterator[Tuple[str, List[str]]]				= zip(tags[0::2]      ,[l.split("\n"			) for l in tags[1::2]]			)

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

   __lowerCAmelCase   :						Optional[Any]				= prot.remark
   if remark is not None:
      pdb_headers.append(F"""REMARK {remark}"""			)

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

   if parents is None or len(__snake_case			) == 0:
      __lowerCAmelCase   :						Any				= ["N/A"]

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

   return pdb_headers


def     _lowercase       (						__snake_case      ,__snake_case			)		->     str:
   __lowerCAmelCase   :						List[str]				= []
   __lowerCAmelCase   :						Optional[int]				= pdb_str.split("\n"			)

   __lowerCAmelCase   :						Any				= prot.remark
   if remark is not None:
      out_pdb_lines.append(F"""REMARK {remark}"""			)

   __lowerCAmelCase   :						List[List[str]]
   if prot.parents is not None and len(prot.parents			) > 0:
      __lowerCAmelCase   :						List[str]				= []
      if prot.parents_chain_index is not None:
         __lowerCAmelCase   :						Dict[str, List[str]]				= {}
         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			)

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

   def make_parent_line(__snake_case			) -> str:
      return F"""PARENT {' '.join(__snake_case			)}"""

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

   __lowerCAmelCase   :						int				= 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			):
            __lowerCAmelCase   :						List[Any]				= parents_per_chain[chain_counter]
         else:
            __lowerCAmelCase   :						List[str]				= ["N/A"]

         out_pdb_lines.append(make_parent_line(__snake_case			)			)

   return "\n".join(__snake_case			)


def     _lowercase       (						__snake_case			)		->     str:
   __lowerCAmelCase   :						int				= residue_constants.restypes + ["X"]

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

   __lowerCAmelCase   :						Dict				= residue_constants.atom_types

   __lowerCAmelCase   :						List[str]				= []

   __lowerCAmelCase   :						List[str]				= prot.atom_mask
   __lowerCAmelCase   :						Dict				= prot.aatype
   __lowerCAmelCase   :						Tuple				= prot.atom_positions
   __lowerCAmelCase   :						List[Any]				= prot.residue_index.astype(np.intaa			)
   __lowerCAmelCase   :						List[Any]				= prot.b_factors
   __lowerCAmelCase   :						Union[str, Any]				= prot.chain_index

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

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

   __lowerCAmelCase   :						List[Any]				= aatype.shape[0]
   __lowerCAmelCase   :						Optional[Any]				= 1
   __lowerCAmelCase   :						Optional[int]				= 0
   __lowerCAmelCase   :						Dict				= string.ascii_uppercase
   __lowerCAmelCase   :						Any				= None
   # Add all atom sites.
   for i in range(__snake_case			):
      __lowerCAmelCase   :						Union[str, Any]				= 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

         __lowerCAmelCase   :						str				= "ATOM"
         __lowerCAmelCase   :						Tuple				= atom_name if len(__snake_case			) == 4 else F""" {atom_name}"""
         __lowerCAmelCase   :						Union[str, Any]				= ""
         __lowerCAmelCase   :						Any				= ""
         __lowerCAmelCase   :						Dict				= 1.00
         __lowerCAmelCase   :						Any				= atom_name[0]  # Protein supports only C, N, O, S, this works.
         __lowerCAmelCase   :						Optional[Any]				= ""

         __lowerCAmelCase   :						Optional[Any]				= "A"
         if chain_index is not None:
            __lowerCAmelCase   :						List[str]				= chain_tags[chain_index[i]]

         # PDB is a columnar format, every space matters here!
         __lowerCAmelCase   :						Tuple				= (
             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

      __lowerCAmelCase   :						List[str]				= i == n - 1
      if chain_index is not None:
         if i != n - 1 and chain_index[i + 1] != prev_chain_index:
            __lowerCAmelCase   :						Optional[Any]				= True
            __lowerCAmelCase   :						Optional[Any]				= chain_index[i + 1]

      if should_terminate:
         # Close the chain.
         __lowerCAmelCase   :						Optional[Any]				= "TER"
         __lowerCAmelCase   :						List[Any]				= (
             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     _lowercase       (						__snake_case			)		->     np.ndarray:
   return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case = None      ,__snake_case = None      ,__snake_case = None      ,__snake_case = None      ,__snake_case = 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
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)