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


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

@dataclasses.dataclass(frozen=__a				)
class        UpperCamelCase						:
						a__		: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'.
						a__		: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.
						a__		:np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		R"""(\[[A-Z]+\]\n)"""
						UpperCamelCase_		:							List[str]  =		[tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) if len(_SCREAMING_SNAKE_CASE			) > 0]
						UpperCamelCase_		:							Iterator[Tuple[str, List[str]]]  =		zip(tags[0::2]		, [l.split("""\n"""			) for l in tags[1::2]]			)

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

						assert aatype is not None

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Protein		, _SCREAMING_SNAKE_CASE     :				int = 0			):
						UpperCamelCase_		:							List[str]  =		[]

						UpperCamelCase_		:							Union[str, Any]  =		prot.remark
						if remark is not None:
												pdb_headers.append(f'''REMARK {remark}'''			)

						UpperCamelCase_		:							Tuple  =		prot.parents
						UpperCamelCase_		:							List[str]  =		prot.parents_chain_index
						if parents is not None and parents_chain_index is not None:
												UpperCamelCase_		:							Optional[int]  =		[p for i, p in zip(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) if i == chain_id]

						if parents is None or len(_SCREAMING_SNAKE_CASE			) == 0:
												UpperCamelCase_		:							Optional[Any]  =		["""N/A"""]

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

						return pdb_headers






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Protein		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							List[str]  =		[]
						UpperCamelCase_		:							Tuple  =		pdb_str.split("""\n"""			)

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

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

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

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

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

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

																		out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE			)			)

						return "\n".join(_SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Protein			):
						UpperCamelCase_		:							str  =		residue_constants.restypes + ["""X"""]

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

						UpperCamelCase_		:							int  =		residue_constants.atom_types

						UpperCamelCase_		:							List[str]  =		[]

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

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

						UpperCamelCase_		:							Optional[Any]  =		get_pdb_headers(_SCREAMING_SNAKE_CASE			)
						if len(_SCREAMING_SNAKE_CASE			) > 0:
												pdb_lines.extend(_SCREAMING_SNAKE_CASE			)

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

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

																		UpperCamelCase_		:							Union[str, Any]  =		"""A"""
																		if chain_index is not None:
																								UpperCamelCase_		:							Optional[int]  =		chain_tags[chain_index[i]]

																		# PDB is a columnar format, every space matters here!
																		UpperCamelCase_		:							List[Any]  =		(
																		    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(_SCREAMING_SNAKE_CASE			)
																		atom_index += 1

												UpperCamelCase_		:							Union[str, Any]  =		i == n - 1
												if chain_index is not None:
																		if i != n - 1 and chain_index[i + 1] != prev_chain_index:
																								UpperCamelCase_		:							Union[str, Any]  =		True
																								UpperCamelCase_		:							Any  =		chain_index[i + 1]

												if should_terminate:
																		# Close the chain.
																		UpperCamelCase_		:							Union[str, Any]  =		"""TER"""
																		UpperCamelCase_		:							Tuple  =		(
																		    f'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]			):>3} {chain_tag:>1}{residue_index[i]:>4}'''
																		)
																		pdb_lines.append(_SCREAMING_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(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)			)

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Protein			):
						return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







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







import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

SCREAMING_SNAKE_CASE		:     List[str]         =	"\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class        UpperCamelCase						(				datasets.Metric				):





						def        A_							(self						)				->				str:
												return datasets.MetricInfo(
												    description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
												        {
												            """X""": datasets.Sequence(datasets.Value("""float"""			,     id="""sequence"""						)			,     id="""X"""						),
												        }						)			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		np.array(__UpperCamelCase						)

												# Assert that arrays are 2D
												if len(X.shape						) != 2:
																		raise ValueError("""Expected `X` to be a 2D vector"""						)
												if len(reference_distribution.shape						) != 2:
																		raise ValueError("""Expected `reference_distribution` to be a 2D vector"""						)
												if reference_distribution.shape[0] < 2:
																		raise ValueError(
																		    """Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"""						)

												# Get mahalanobis distance for each prediction
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






