
'''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


_lowercase			:  str   							=	Mapping[str, np.ndarray]
_lowercase			:  str   							=	Mapping[str, Any]  # Is a nested dict.
_lowercase			:  Optional[Any]   							=	0.0_1






@dataclasses.dataclass(frozen=lowerCamelCase_					)
class 			lowerCAmelCase__       :
					lowerCAmelCase_					       =					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_					       =					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_					       =					42  # [num_res, num_atom_type]

					# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
					lowerCAmelCase_					       =					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_					       =					42  # [num_res, num_atom_type]

					# Chain indices for multi-chain predictions
					lowerCAmelCase_					       =					None

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

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

					# Chain corresponding to each parent
					lowerCAmelCase_					       =					None







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





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

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



						"""simple docstring"""





						lowercase_     :					List[str]													=	[]

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

						lowercase_     :					int													=	prot.parents
						lowercase_     :					int													=	prot.parents_chain_index
						if parents is not None and parents_chain_index is not None:
												lowercase_     :					Union[str, Any]													=	[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:
												lowercase_     :					Union[str, Any]													=	['''N/A''']

						pdb_headers.append(F'''PARENT {' '.join(__SCREAMING_SNAKE_CASE							)}'''							)

						return pdb_headers
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Protein				,				__SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	[]
						lowercase_     :					Optional[Any]													=	pdb_str.split('''\n'''							)

						lowercase_     :					Union[str, Any]													=	prot.remark
						if remark is not None:
												out_pdb_lines.append(F'''REMARK {remark}'''							)

						lowercase_     :					List[List[str]]
						if prot.parents is not None and len(prot.parents							) > 0:
												lowercase_     :					int													=	[]
												if prot.parents_chain_index is not None:
																		lowercase_     :					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							)

																		lowercase_     :					Union[str, Any]													=	max([int(__SCREAMING_SNAKE_CASE							) for chain_idx in parent_dict]							)
																		for i in range(max_idx + 1							):
																								lowercase_     :					Dict													=	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:
												lowercase_     :					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]							)							)

						lowercase_     :					List[str]													=	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							):
																								lowercase_     :					int													=	parents_per_chain[chain_counter]
																		else:
																								lowercase_     :					List[Any]													=	['''N/A''']

																		out_pdb_lines.append(make_parent_line(__SCREAMING_SNAKE_CASE							)							)

						return "\n".join(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Protein							):



						"""simple docstring"""





						lowercase_     :					Any													=	residue_constants.restypes + ['''X''']

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

						lowercase_     :					Union[str, Any]													=	residue_constants.atom_types

						lowercase_     :					List[str]													=	[]

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

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

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

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

																		lowercase_     :					List[str]													=	'''ATOM'''
																		lowercase_     :					Tuple													=	atom_name if len(__SCREAMING_SNAKE_CASE							) == 4 else F''' {atom_name}'''
																		lowercase_     :					int													=	''''''
																		lowercase_     :					Dict													=	''''''
																		lowercase_     :					Dict													=	1.00
																		lowercase_     :					Tuple													=	atom_name[0]  # Protein supports only C, N, O, S, this works.
																		lowercase_     :					int													=	''''''

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

																		# PDB is a columnar format, every space matters here!
																		lowercase_     :					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

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

												if should_terminate:
																		# Close the chain.
																		lowercase_     :					List[str]													=	'''TER'''
																		lowercase_     :					Union[str, Any]													=	(
																		    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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Protein							):



						"""simple docstring"""





						return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def  snake_case_		(    __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				,				):



						"""simple docstring"""





						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				,				)





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					int													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''`custom_timesteps` must be in descending order.'''		):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																	scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __SCREAMING_SNAKE_CASE    ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,							):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)



