


'''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


lowerCAmelCase				:List[str]        =      Mapping[str, np.ndarray]
lowerCAmelCase				:List[Any]        =      Mapping[str, Any]  # Is a nested dict.
lowerCAmelCase				:Dict        =      0.01
@dataclasses.dataclass(frozen=lowercase__							)
class _lowerCamelCase	:



		'''simple docstring'''







		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      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







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

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

	assert aatype is not None

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





def lowerCamelCase      (      lowerCAmelCase    :     Protein		,		lowerCAmelCase    :     int = 0		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					[]

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

	__magic_name__	:   str					     =					prot.parents
	__magic_name__	:   Union[str, Any]					     =					prot.parents_chain_index
	if parents is not None and parents_chain_index is not None:
		__magic_name__	:   Optional[Any]					     =					[p for i, p in zip(lowerCAmelCase		,		lowerCAmelCase		) if i == chain_id]

	if parents is None or len(lowerCAmelCase		) == 0:
		__magic_name__	:   Tuple					     =					['N/A']

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

	return pdb_headers





def lowerCamelCase      (      lowerCAmelCase    :     Protein		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					[]
	__magic_name__	:   int					     =					pdb_str.split('\n'		)

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

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

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

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

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

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

			out_pdb_lines.append(make_parent_line(lowerCAmelCase		)		)

	return "\n".join(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     Protein		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					residue_constants.restypes + ['X']

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

	__magic_name__	:   Tuple					     =					residue_constants.atom_types

	__magic_name__	:   List[str]					     =					[]

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

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

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

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

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

			__magic_name__	:   List[str]					     =					'A'
			if chain_index is not None:
				__magic_name__	:   List[str]					     =					chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			__magic_name__	:   Optional[int]					     =					(
			    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(lowerCAmelCase		)
			atom_index += 1

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

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

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





def lowerCamelCase      (      lowerCAmelCase    :     Protein		):





	"""simple docstring"""







	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





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



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2