





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__   : Any					  = Mapping[str, Any]  # Is a nested dict.
UpperCamelCase__   : int					  = 0.01







@dataclasses.dataclass(frozen=A_  )
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     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'.
					lowerCamelCase     :     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.
					lowerCamelCase     :     np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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




def    A_(			A   ):
	UpperCAmelCase_								=     R"""(\[[A-Z]+\]\n)"""
	UpperCAmelCase_								=     [tag.strip() for tag in re.split(A				,      A   ) if len(A   ) > 0]
	UpperCAmelCase_								=     zip(tags[0::2]				,      [l.split("""\n"""   ) for l in tags[1::2]]   )

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

	assert aatype is not None

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




def    A_(			A				,      A = 0   ):
	UpperCAmelCase_								=     []

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

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

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

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

	return pdb_headers




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     pdb_str.split("""\n"""   )

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

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

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

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

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

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

			out_pdb_lines.append(make_parent_line(A   )   )

	return "\n".join(A   )




def    A_(			A   ):
	UpperCAmelCase_								=     residue_constants.restypes + ["""X"""]

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

	UpperCAmelCase_								=     residue_constants.atom_types

	UpperCAmelCase_								=     []

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

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

	UpperCAmelCase_								=     get_pdb_headers(A   )
	if len(A   ) > 0:
		pdb_lines.extend(A   )

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

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

			UpperCAmelCase_								=     """A"""
			if chain_index is not None:
				UpperCAmelCase_								=     chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			UpperCAmelCase_								=     (
			    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(A   )
			atom_index += 1

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

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

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




def    A_(			A   ):
	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def    A_(			A				,      A				,      A = None				,      A = None				,      A = None				,      A = None				,      A = None				,      ):
	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=A				,      remark=A				,      parents=A				,      parents_chain_index=A				,      )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__   : List[Any]					  = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : List[str]					  = [
				    """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SEWForCTC""",
				    """SEWForSequenceClassification""",
				    """SEWModel""",
				    """SEWPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_sew import (
								    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SEWForCTC,
								    SEWForSequenceClassification,
								    SEWModel,
								    SEWPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__   : str					  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


