






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


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






@dataclasses.dataclass(frozen=__snake_case							)
class 						UpperCAmelCase__ :
		__snake_case      :       np.ndarray  # [num_res, num_atom_type, 3]

		# Amino-acid type for each residue represented as an integer between 0 and
		# 20, where 20 is 'X'.
		__snake_case      :       np.ndarray  # [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.
		__snake_case      :       np.ndarray  # [num_res, num_atom_type]

		# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
		__snake_case      :       np.ndarray  # [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.
		__snake_case      :       np.ndarray  # [num_res, num_atom_type]

		# Chain indices for multi-chain predictions
		__snake_case      :       Optional[np.ndarray]     =   None

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

		# Templates used to generate this protein (prediction-only)
		__snake_case      :       Optional[Sequence[str]]     =   None

		# Chain corresponding to each parent
		__snake_case      :       Optional[Sequence[int]]     =   None



def  a__ (__lowercase							:str   )		-> Protein:
				_A	:				List[str]   =      R'''(\[[A-Z]+\]\n)'''
				_A	:				List[str]   =      [tag.strip() for tag in re.split(__lowercase					,  __lowercase   ) if len(__lowercase   ) > 0]
				_A	:				Iterator[Tuple[str, List[str]]]   =      zip(tags[0::2]					,  [l.split('''\n'''   ) for l in tags[1::2]]   )

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

				assert aatype is not None

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



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

				_A	:				str   =      prot.remark
				if remark is not None:
								pdb_headers.append(f"""REMARK {remark}"""   )

				_A	:				str   =      prot.parents
				_A	:				List[str]   =      prot.parents_chain_index
				if parents is not None and parents_chain_index is not None:
								_A	:				str   =      [p for i, p in zip(__lowercase					,  __lowercase   ) if i == chain_id]

				if parents is None or len(__lowercase   ) == 0:
								_A	:				Any   =      ['''N/A''']

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

				return pdb_headers



def  a__ (__lowercase							:Protein					,  __lowercase							:str   )		-> str:
				_A	:				List[str]   =      []
				_A	:				List[str]   =      pdb_str.split('''\n'''   )

				_A	:				Optional[Any]   =      prot.remark
				if remark is not None:
								out_pdb_lines.append(f"""REMARK {remark}"""   )

				_A	:				List[List[str]]
				if prot.parents is not None and len(prot.parents   ) > 0:
								_A	:				Union[str, Any]   =      []
								if prot.parents_chain_index is not None:
												_A	:				Dict[str, List[str]]   =      {}
												for p, i in zip(prot.parents					,  prot.parents_chain_index   ):
																parent_dict.setdefault(str(__lowercase   )					,  []   )
																parent_dict[str(__lowercase   )].append(__lowercase   )

												_A	:				Optional[int]   =      max([int(__lowercase   ) for chain_idx in parent_dict]   )
												for i in range(max_idx + 1   ):
																_A	:				Optional[Any]   =      parent_dict.get(str(__lowercase   )					,  ['''N/A''']   )
																parents_per_chain.append(__lowercase   )
								else:
												parents_per_chain.append(list(prot.parents   )   )
				else:
								_A	:				Union[str, Any]   =      [['''N/A''']]

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

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

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

												out_pdb_lines.append(make_parent_line(__lowercase   )   )

				return "\n".join(__lowercase   )



def  a__ (__lowercase							:Protein   )		-> str:
				_A	:				List[Any]   =      residue_constants.restypes + ['''X''']

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

				_A	:				int   =      residue_constants.atom_types

				_A	:				List[str]   =      []

				_A	:				Dict   =      prot.atom_mask
				_A	:				str   =      prot.aatype
				_A	:				Optional[int]   =      prot.atom_positions
				_A	:				List[Any]   =      prot.residue_index.astype(np.intaa   )
				_A	:				Tuple   =      prot.b_factors
				_A	:				int   =      prot.chain_index

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

				_A	:				Dict   =      get_pdb_headers(__lowercase   )
				if len(__lowercase   ) > 0:
								pdb_lines.extend(__lowercase   )

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

												_A	:				Optional[int]   =      '''ATOM'''
												_A	:				int   =      atom_name if len(__lowercase   ) == 4 else f""" {atom_name}"""
												_A	:				int   =      ''''''
												_A	:				Optional[int]   =      ''''''
												_A	:				str   =      1.00
												_A	:				Any   =      atom_name[0]  # Protein supports only C, N, O, S, this works.
												_A	:				Optional[Any]   =      ''''''

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

												# PDB is a columnar format, every space matters here!
												_A	:				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(__lowercase   )
												atom_index += 1

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

								if should_terminate:
												# Close the chain.
												_A	:				Optional[Any]   =      '''TER'''
												_A	:				int   =      (
												    f"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]   ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
												)
												pdb_lines.append(__lowercase   )
												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(__lowercase					,  __lowercase   )   )

				pdb_lines.append('''END'''   )
				pdb_lines.append(''''''   )
				return "\n".join(__lowercase   )



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






def  a__ (__lowercase							:FeatureDict					,  __lowercase							:ModelOutput					,  __lowercase							:Optional[np.ndarray] = None					,  __lowercase							:Optional[np.ndarray] = None					,  __lowercase							:Optional[str] = None					,  __lowercase							:Optional[Sequence[str]] = None					,  __lowercase							: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=__lowercase					,  remark=__lowercase					,  parents=__lowercase					,  parents_chain_index=__lowercase					,  )











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



