

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__							(		SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							None )     -> torch.Tensor:




				'''simple docstring'''





				...



@overload
def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor )     -> Tuple[torch.Tensor, torch.Tensor]:




				'''simple docstring'''





				...



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Tuple )     -> int:




				'''simple docstring'''





				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(
								    SCREAMING_SNAKE_CASE_		,    all_atom_masks[..., ca_idx]		,    all_atom_masks[..., cb_idx]		,    )
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							Dict[str, torch.Tensor] )     -> torch.Tensor:




				'''simple docstring'''





				A__            =     batched_gather(
				    SCREAMING_SNAKE_CASE_		,    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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict[str, torch.Tensor] )     -> torch.Tensor:




				'''simple docstring'''





				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(SCREAMING_SNAKE_CASE_		,    2_2 ),
				        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]		,    1_4 ),
				        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]		,    1_4 ),
				        torsion_angles_mask,
				    ]		,    dim=-1		,    )

				return template_angle_feat



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict[str, torch.Tensor]		,    SCREAMING_SNAKE_CASE_:							torch.types.Number		,    SCREAMING_SNAKE_CASE_:							torch.types.Number		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							bool = False		,    SCREAMING_SNAKE_CASE_:							float = 1e-20		,    SCREAMING_SNAKE_CASE_:							float = 1e8		,    )     -> torch.Tensor:




				'''simple docstring'''





				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=SCREAMING_SNAKE_CASE_ )
				A__            =     torch.linspace(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    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]		,    SCREAMING_SNAKE_CASE_		,    -1		,    -1 ) )
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]		,    -1		,    SCREAMING_SNAKE_CASE_		,    -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=SCREAMING_SNAKE_CASE_		,    )
				A__            =     rigids.get_trans()[..., None, :, :]
				A__            =     rigids[..., None].invert_apply(SCREAMING_SNAKE_CASE_ )

				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(SCREAMING_SNAKE_CASE_		,    dim=-1 )
				A__            =     act * template_mask_ad[..., None]

				return act



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict[str, torch.Tensor] )     -> torch.Tensor:




				'''simple docstring'''





				A__            =     nn.functional.one_hot(batch["extra_msa"]		,    2_3 )
				A__            =     [
				    msa_ahot,
				    batch["extra_has_deletion"].unsqueeze(-1 ),
				    batch["extra_deletion_value"].unsqueeze(-1 ),
				]
				return torch.cat(SCREAMING_SNAKE_CASE_		,    dim=-1 )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Rigid		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    )     -> Rigid:




				'''simple docstring'''





				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(SCREAMING_SNAKE_CASE_ )

				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=SCREAMING_SNAKE_CASE_ )		,    SCREAMING_SNAKE_CASE_ ) )

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

				A__            =     all_frames[..., 4]
				A__            =     chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_ )
				A__            =     chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_ )
				A__            =     chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_ )

				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(SCREAMING_SNAKE_CASE_ )

				return all_frames_to_global





def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Rigid		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    SCREAMING_SNAKE_CASE_:							torch.Tensor		,    )     -> torch.Tensor:




				'''simple docstring'''





				A__            =     group_idx[aatype, ...]

				# [*, N, 14, 8]
				A__            =     nn.functional.one_hot(
				    SCREAMING_SNAKE_CASE_		,    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 SCREAMING_SNAKE_CASE_ : torch.sum(SCREAMING_SNAKE_CASE_		,    dim=-1 ) )

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

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

				return pred_positions




import unittest

from cer import CER


lowerCAmelCase__						=	CER()

class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["White House"]
							A__            =     ["white house"]
							# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.18_1818_1818    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["were wolf"]
							A__            =     ["werewolf"]
							# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.111_1111    ) < 1e-6    )

							A__            =     ["werewolf"]
							A__            =     ["weae     wolf"]
							# S = 1, D = 1, I = 0, N = 8, CER = 0.25
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

							# consecutive whitespaces case 1
							A__            =     ["were wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

							# consecutive whitespaces case 2
							A__            =     ["were   wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["weaewolf"]
							# S = 1, D = 0, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereawolf"]
							# S = 0, D = 1, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereolf"]
							# S = 0, D = 0, I = 1, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["werewolf", "I am your father"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )

							A__            =     ["werewolf", "I am your father", "doge"]
							A__            =     ["werxwolf", "I       am your father", "doge"]
							# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0357_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["My hovercraft", "is full of eels"]
							A__            =     ["My hovercraft is full", " of eels"]
							# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
							# whitespace at the front of " of eels" will be strip during preporcessing
							# so need to insert 2 whitespaces
							A__            =     cer.compute(predictions=lowercase , references=lowercase , concatenate_texts=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.07_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     [" 能吞虾玻璃而 不霜身体啦"]
							# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.45_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃", "而不伤身体"]
							A__            =     ["我    能 吞 下 玻 璃", "而不伤身体"]
							# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.4545_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertFalse(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     [""]
							A__            =     ["Hypothesis"]
							with self.assertRaises(lowercase    ):
											cer.compute(predictions=lowercase , references=lowercase    )


if __name__ == "__main__":
	unittest.main()

