





"""simple docstring"""
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  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							...






@overload
def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							...






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  aatype == rc.restype_order["G"]
							A		=  rc.atom_order["CA"]
							A		=  rc.atom_order["CB"]
							A		=  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		=  torch.where(
														    UpperCamelCase ,  all_atom_masks[..., ca_idx] ,  all_atom_masks[..., cb_idx] ,  )
														return pseudo_beta, pseudo_beta_mask
							else:
														return pseudo_beta






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  batched_gather(
							    UpperCamelCase ,  batch["residx_atom37_to_atom14"] ,  dim=-2 ,  no_batch_dims=len(atomaa.shape[:-2]	) ,  )

							A		=  atomaa_data * batch["atom37_atom_exists"][..., None]

							return atomaa_data






def  A__    (      UpperCamelCase	):
							A		=  template_feats["template_aatype"]
							A		=  template_feats["template_torsion_angles_sin_cos"]
							A		=  template_feats["template_alt_torsion_angles_sin_cos"]
							A		=  template_feats["template_torsion_angles_mask"]
							A		=  torch.cat(
							    [
							        nn.functional.one_hot(UpperCamelCase ,  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  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase = False ,  UpperCamelCase = 1E-20 ,  UpperCamelCase = 1E8 ,  ):
							A		=  batch["template_pseudo_beta_mask"]
							A		=  template_mask[..., None] * template_mask[..., None, :]

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

							A		=  [dgram, template_mask_ad[..., None]]

							A		=  nn.functional.one_hot(
							    batch["template_aatype"] ,  rc.restype_num + 2 ,  )

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

							A,     A,     A		=  [rc.atom_order[a] for a in ["N", "CA", "C"]]
							A		=  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=UpperCamelCase ,  )
							A		=  rigids.get_trans()[..., None, :, :]
							A		=  rigids[..., None].invert_apply(UpperCamelCase	)

							A		=  torch.rsqrt(eps + torch.sum(rigid_vec**2 ,  dim=-1	)	)

							A		=  batch["template_all_atom_mask"]
							A		=  t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
							A		=  template_mask[..., None] * template_mask[..., None, :]

							A		=  inv_distance_scalar * template_mask_ad
							A		=  rigid_vec * inv_distance_scalar[..., None]

							if not use_unit_vector:
														A		=  unit_vector * 0.0

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

							A		=  torch.cat(UpperCamelCase ,  dim=-1	)
							A		=  act * template_mask_ad[..., None]

							return act






def  A__    (      UpperCamelCase	):
							A		=  nn.functional.one_hot(batch["extra_msa"] ,  23	)
							A		=  [
							    msa_ahot,
							    batch["extra_has_deletion"].unsqueeze(-1	),
							    batch["extra_deletion_value"].unsqueeze(-1	),
							]
							return torch.cat(UpperCamelCase ,  dim=-1	)






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  ):
							# [*, N, 8, 4, 4]
							A		=  rrgdf[aatype, ...]

							# [*, N, 8] transformations, i.e.
							#   One [*, N, 8, 3, 3] rotation matrix and
							#   One [*, N, 8, 3]    translation matrix
							A		=  r.from_tensor_axa(UpperCamelCase	)

							A		=  alpha.new_zeros((*((1,) * len(alpha.shape[:-1]	)), 2)	)
							A		=  1

							# [*, N, 8, 2]
							A		=  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		=  alpha.new_zeros(default_r.get_rots().get_rot_mats().shape	)
							A		=  1
							A		=  alpha[..., 1]
							A		=  -alpha[..., 0]
							A		=  alpha

							A		=  default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase	) ,  UpperCamelCase	)	)

							A		=  all_frames[..., 5]
							A		=  all_frames[..., 6]
							A		=  all_frames[..., 7]

							A		=  all_frames[..., 4]
							A		=  chia_frame_to_bb.compose(UpperCamelCase	)
							A		=  chia_frame_to_bb.compose(UpperCamelCase	)
							A		=  chia_frame_to_bb.compose(UpperCamelCase	)

							A		=  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		=  r[..., None].compose(UpperCamelCase	)

							return all_frames_to_global






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  ):
							# [*, N, 14]
							A		=  group_idx[aatype, ...]

							# [*, N, 14, 8]
							A		=  nn.functional.one_hot(
							    UpperCamelCase ,  num_classes=default_frames.shape[-3] ,  )

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

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

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

							# [*, N, 14, 3]
							A		=  lit_positions[aatype, ...]
							A		=  t_atoms_to_global.apply(UpperCamelCase	)
							A		=  pred_positions * atom_mask

							return pred_positions









"""simple docstring"""
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:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    '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'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												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(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

												return (out_vocab_file,)


