
"""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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> torch.Tensor:
	...



@overload
def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Tuple[torch.Tensor, torch.Tensor]:
	...



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Dict:
	_lowerCAmelCase								=aatype == rc.restype_order["""G"""]
	_lowerCAmelCase								=rc.atom_order["""CA"""]
	_lowerCAmelCase								=rc.atom_order["""CB"""]
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=torch.where(
		    __UpperCamelCase    , all_atom_masks[..., ca_idx]    , all_atom_masks[..., cb_idx]    , )
		return pseudo_beta, pseudo_beta_mask
	else:
		return pseudo_beta



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> torch.Tensor:
	_lowerCAmelCase								=batched_gather(
	    __UpperCamelCase    , batch["""residx_atom37_to_atom14"""]    , dim=-2    , no_batch_dims=len(atomaa.shape[:-2]       )    , )

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

	return atomaa_data



def     _lowerCamelCase(__UpperCamelCase       )				-> torch.Tensor:
	_lowerCAmelCase								=template_feats["""template_aatype"""]
	_lowerCAmelCase								=template_feats["""template_torsion_angles_sin_cos"""]
	_lowerCAmelCase								=template_feats["""template_alt_torsion_angles_sin_cos"""]
	_lowerCAmelCase								=template_feats["""template_torsion_angles_mask"""]
	_lowerCAmelCase								=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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase = False    , __UpperCamelCase = 1E-2_0    , __UpperCamelCase = 1E8    , )				-> torch.Tensor:
	_lowerCAmelCase								=batch["""template_pseudo_beta_mask"""]
	_lowerCAmelCase								=template_mask[..., None] * template_mask[..., None, :]

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

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

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

	_lowerCAmelCase								=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       )       )

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

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

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

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

	if not use_unit_vector:
		_lowerCAmelCase								=unit_vector * 0.0

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

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

	return act



def     _lowerCamelCase(__UpperCamelCase       )				-> torch.Tensor:
	_lowerCAmelCase								=nn.functional.one_hot(batch["""extra_msa"""]    , 23       )
	_lowerCAmelCase								=[
	    msa_ahot,
	    batch["""extra_has_deletion"""].unsqueeze(-1       ),
	    batch["""extra_deletion_value"""].unsqueeze(-1       ),
	]
	return torch.cat(__UpperCamelCase    , dim=-1       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , )				-> Rigid:
	# [*, N, 8, 4, 4]
	_lowerCAmelCase								=rrgdf[aatype, ...]

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

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

	# [*, N, 8, 2]
	_lowerCAmelCase								=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.

	_lowerCAmelCase								=alpha.new_zeros(default_r.get_rots().get_rot_mats().shape       )
	_lowerCAmelCase								=1
	_lowerCAmelCase								=alpha[..., 1]
	_lowerCAmelCase								=-alpha[..., 0]
	_lowerCAmelCase								=alpha

	_lowerCAmelCase								=default_r.compose(Rigid(Rotation(rot_mats=__UpperCamelCase       )    , __UpperCamelCase       )       )

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

	_lowerCAmelCase								=all_frames[..., 4]
	_lowerCAmelCase								=chia_frame_to_bb.compose(__UpperCamelCase       )
	_lowerCAmelCase								=chia_frame_to_bb.compose(__UpperCamelCase       )
	_lowerCAmelCase								=chia_frame_to_bb.compose(__UpperCamelCase       )

	_lowerCAmelCase								=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    , )

	_lowerCAmelCase								=r[..., None].compose(__UpperCamelCase       )

	return all_frames_to_global



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , )				-> torch.Tensor:
	# [*, N, 14]
	_lowerCAmelCase								=group_idx[aatype, ...]

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

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

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

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

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

	return pred_positions









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

