



from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather

@overload
def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     None	)							-> torch.Tensor:







			"""simple docstring"""
			...

@overload
def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor	)							-> Tuple[torch.Tensor, torch.Tensor]:







			"""simple docstring"""
			...

def 			lowerCAmelCase_  (						_lowercase       :     Union[str, Any]		,     _lowercase       :     Tuple		,     _lowercase       :     Any	)							-> Tuple:







			"""simple docstring"""
			a__     :		List[Any]		 =						aatype == rc.restype_order["""G"""]
			a__     :		Optional[int]		 =						rc.atom_order["""CA"""]
			a__     :		List[str]		 =						rc.atom_order["""CB"""]
			a__     :		int		 =						torch.where(
			    is_gly[..., None].expand(*((-1,) * len(is_gly.shape	))		,     3	)		,     all_atom_positions[..., ca_idx, :]		,     all_atom_positions[..., cb_idx, :]		,     )

			if all_atom_masks is not None:
						a__     :		Union[str, Any]		 =						torch.where(
						    _lowercase		,     all_atom_masks[..., ca_idx]		,     all_atom_masks[..., cb_idx]		,     )
						return pseudo_beta, pseudo_beta_mask
			else:
						return pseudo_beta

def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     Dict[str, torch.Tensor]	)							-> torch.Tensor:







			"""simple docstring"""
			a__     :		int		 =						batched_gather(
			    _lowercase		,     batch["""residx_atom37_to_atom14"""]		,     dim=-2		,     no_batch_dims=len(atomaa.shape[:-2]	)		,     )

			a__     :		Tuple		 =						atomaa_data * batch["""atom37_atom_exists"""][..., None]

			return atomaa_data

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor]	)							-> torch.Tensor:







			"""simple docstring"""
			a__     :		List[str]		 =						template_feats["""template_aatype"""]
			a__     :		str		 =						template_feats["""template_torsion_angles_sin_cos"""]
			a__     :		Optional[int]		 =						template_feats["""template_alt_torsion_angles_sin_cos"""]
			a__     :		Any		 =						template_feats["""template_torsion_angles_mask"""]
			a__     :		Optional[Any]		 =						torch.cat(
			    [
			        nn.functional.one_hot(_lowercase		,     22	),
			        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]		,     14	),
			        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]		,     14	),
			        torsion_angles_mask,
			    ]		,     dim=-1		,     )

			return template_angle_feat

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor]		,     _lowercase       :     torch.types.Number		,     _lowercase       :     torch.types.Number		,     _lowercase       :     int		,     _lowercase       :     bool = False		,     _lowercase       :     float = 1e-2_0		,     _lowercase       :     float = 1e8		,     )							-> torch.Tensor:







			"""simple docstring"""
			a__     :		List[Any]		 =						batch["""template_pseudo_beta_mask"""]
			a__     :		Union[str, Any]		 =						template_mask[..., None] * template_mask[..., None, :]

			# Compute distogram (this seems to differ slightly from Alg. 5)
			a__     :		Dict		 =						batch["""template_pseudo_beta"""]
			a__     :		Tuple		 =						torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2		,     dim=-1		,     keepdim=_lowercase	)
			a__     :		Tuple		 =						torch.linspace(_lowercase		,     _lowercase		,     _lowercase		,     device=tpb.device	) ** 2
			a__     :		List[str]		 =						torch.cat([lower[1:], lower.new_tensor([inf]	)]		,     dim=-1	)
			a__     :		List[Any]		 =						((dgram > lower) * (dgram < upper)).type(dgram.dtype	)

			a__     :		Union[str, Any]		 =						[dgram, template_mask_ad[..., None]]

			a__     :		torch.LongTensor		 =						nn.functional.one_hot(
			    batch["""template_aatype"""]		,     rc.restype_num + 2		,     )

			a__     :		Any		 =						batch["""template_aatype"""].shape[-1]
			to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]		,     _lowercase		,     -1		,     -1	)	)
			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]		,     -1		,     _lowercase		,     -1	)	)

			a__     :		Optional[int]		 =						[rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
			a__     :		Union[str, Any]		 =						Rigid.make_transform_from_reference(
			    n_xyz=batch["""template_all_atom_positions"""][..., n, :]		,     ca_xyz=batch["""template_all_atom_positions"""][..., ca, :]		,     c_xyz=batch["""template_all_atom_positions"""][..., c, :]		,     eps=_lowercase		,     )
			a__     :		int		 =						rigids.get_trans()[..., None, :, :]
			a__     :		Tuple		 =						rigids[..., None].invert_apply(_lowercase	)

			a__     :		Union[str, Any]		 =						torch.rsqrt(eps + torch.sum(rigid_vec**2		,     dim=-1	)	)

			a__     :		Tuple		 =						batch["""template_all_atom_mask"""]
			a__     :		Tuple		 =						t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
			a__     :		Optional[int]		 =						template_mask[..., None] * template_mask[..., None, :]

			a__     :		int		 =						inv_distance_scalar * template_mask_ad
			a__     :		Dict		 =						rigid_vec * inv_distance_scalar[..., None]

			if not use_unit_vector:
						a__     :		Dict		 =						unit_vector * 0.0

			to_concat.extend(torch.unbind(unit_vector[..., None, :]		,     dim=-1	)	)
			to_concat.append(template_mask_ad[..., None]	)

			a__     :		int		 =						torch.cat(_lowercase		,     dim=-1	)
			a__     :		Dict		 =						act * template_mask_ad[..., None]

			return act

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor]	)							-> torch.Tensor:







			"""simple docstring"""
			a__     :		torch.LongTensor		 =						nn.functional.one_hot(batch["""extra_msa"""]		,     23	)
			a__     :		Dict		 =						[
			    msa_ahot,
			    batch["""extra_has_deletion"""].unsqueeze(-1	),
			    batch["""extra_deletion_value"""].unsqueeze(-1	),
			]
			return torch.cat(_lowercase		,     dim=-1	)

def 			lowerCAmelCase_  (						_lowercase       :     Rigid		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     )							-> Rigid:







			"""simple docstring"""
			a__     :		Any		 =						rrgdf[aatype, ...]

			# [*, N, 8] transformations, i.e.
			#   One [*, N, 8, 3, 3] rotation matrix and
			#   One [*, N, 8, 3]    translation matrix
			a__     :		Any		 =						r.from_tensor_axa(_lowercase	)

			a__     :		List[Any]		 =						alpha.new_zeros((*((1,) * len(alpha.shape[:-1]	)), 2)	)
			a__     :		str		 =						1

			# [*, N, 8, 2]
			a__     :		int		 =						torch.cat([bb_rot.expand(*alpha.shape[:-2]		,     -1		,     -1	), alpha]		,     dim=-2	)

			# [*, N, 8, 3, 3]
			# Produces rotation matrices of the form:
			# [
			#   [1, 0  , 0  ],
			#   [0, a_2,-a_1],
			#   [0, a_1, a_2]
			# ]
			# This follows the original code rather than the supplement, which uses
			# different indices.

			a__     :		Dict		 =						alpha.new_zeros(default_r.get_rots().get_rot_mats().shape	)
			a__     :		Union[str, Any]		 =						1
			a__     :		Union[str, Any]		 =						alpha[..., 1]
			a__     :		int		 =						-alpha[..., 0]
			a__     :		Union[str, Any]		 =						alpha

			a__     :		str		 =						default_r.compose(Rigid(Rotation(rot_mats=_lowercase	)		,     _lowercase	)	)

			a__     :		Tuple		 =						all_frames[..., 5]
			a__     :		Any		 =						all_frames[..., 6]
			a__     :		Optional[int]		 =						all_frames[..., 7]

			a__     :		List[Any]		 =						all_frames[..., 4]
			a__     :		str		 =						chia_frame_to_bb.compose(_lowercase	)
			a__     :		List[Any]		 =						chia_frame_to_bb.compose(_lowercase	)
			a__     :		List[Any]		 =						chia_frame_to_bb.compose(_lowercase	)

			a__     :		Tuple		 =						Rigid.cat(
			    [
			        all_frames[..., :5],
			        chia_frame_to_bb.unsqueeze(-1	),
			        chia_frame_to_bb.unsqueeze(-1	),
			        chia_frame_to_bb.unsqueeze(-1	),
			    ]		,     dim=-1		,     )

			a__     :		str		 =						r[..., None].compose(_lowercase	)

			return all_frames_to_global





def 			lowerCAmelCase_  (						_lowercase       :     Rigid		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     )							-> torch.Tensor:







			"""simple docstring"""
			a__     :		List[str]		 =						group_idx[aatype, ...]

			# [*, N, 14, 8]
			a__     :		torch.LongTensor		 =						nn.functional.one_hot(
			    _lowercase		,     num_classes=default_frames.shape[-3]		,     )

			# [*, N, 14, 8]
			a__     :		int		 =						r[..., None, :] * group_mask_one_hot

			# [*, N, 14]
			a__     :		Tuple		 =						t_atoms_to_global.map_tensor_fn(lambda _lowercase	: torch.sum(_lowercase		,     dim=-1	)	)

			# [*, N, 14, 1]
			a__     :		Dict		 =						atom_mask[aatype, ...].unsqueeze(-1	)

			# [*, N, 14, 3]
			a__     :		int		 =						lit_positions[aatype, ...]
			a__     :		List[str]		 =						t_atoms_to_global.apply(_lowercase	)
			a__     :		Dict		 =						pred_positions * atom_mask

			return pred_positions









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_lowercase	:   Optional[int]    	=None


_lowercase	:   Union[str, Any]    	=logging.get_logger(__name__)


_lowercase	:   Any    	="▁"

_lowercase	:   Union[str, Any]    	={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase	:   List[str]    	={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase	:   List[str]    	={
    "google/reformer-crime-and-punishment": 52_4288,
}

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Union[str, Any]			      =  VOCAB_FILES_NAMES
						__lowerCAmelCase  :int			      =  PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase  :List[Any]			      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase  :Any			      =  ["input_ids", "attention_mask"]
						__lowerCAmelCase  :Tuple			      =  ReformerTokenizer

						def __init__(     self      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase="</s>"      ,     __lowercase="<unk>"      ,     __lowercase=[]      ,     **__lowercase      ,     )      ->   int:
									"""simple docstring"""




									super().__init__(
									    __lowercase      ,     tokenizer_file=__lowercase      ,     eos_token=__lowercase      ,     unk_token=__lowercase      ,     additional_special_tokens=__lowercase      ,     **__lowercase      ,     )

									a__     :		Union[str, Any]		 =						vocab_file
									a__     :		str		 =						False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   Tuple[str]:
									"""simple docstring"""




									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""						)

									if not os.path.isdir(__lowercase						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__     :		Tuple		 =						os.path.join(
									    __lowercase      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
												copyfile(self.vocab_file      ,     __lowercase						)

									return (out_vocab_file,)




