"""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_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					torch.Tensor:




						'''simple docstring'''



						...





@overload
def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple[torch.Tensor, torch.Tensor]:




						'''simple docstring'''



						...





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					torch.Tensor:




						'''simple docstring'''



						lowerCamelCase__					=batched_gather(
						    __lowerCAmelCase   ,    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_ (					__lowerCAmelCase				)							->					torch.Tensor:




						'''simple docstring'''



						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(__lowerCAmelCase   ,    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_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase = False   ,    __lowerCAmelCase = 1e-20   ,    __lowerCAmelCase = 1e8   ,    )							->					torch.Tensor:




						'''simple docstring'''



						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=__lowerCAmelCase				)
						lowerCamelCase__					=torch.linspace(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    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]   ,    __lowerCAmelCase   ,    -1   ,    -1				)				)
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]   ,    -1   ,    __lowerCAmelCase   ,    -1				)				)

						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=__lowerCAmelCase   ,    )
						lowerCamelCase__					=rigids.get_trans()[..., None, :, :]
						lowerCamelCase__					=rigids[..., None].invert_apply(__lowerCAmelCase				)

						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(__lowerCAmelCase   ,    dim=-1				)
						lowerCamelCase__					=act * template_mask_ad[..., None]

						return act





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					torch.Tensor:




						'''simple docstring'''



						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(__lowerCAmelCase   ,    dim=-1				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    )							->					Rigid:




						'''simple docstring'''



						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(__lowerCAmelCase				)

						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=__lowerCAmelCase				)   ,    __lowerCAmelCase				)				)

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

						lowerCamelCase__					=all_frames[..., 4]
						lowerCamelCase__					=chia_frame_to_bb.compose(__lowerCAmelCase				)
						lowerCamelCase__					=chia_frame_to_bb.compose(__lowerCAmelCase				)
						lowerCamelCase__					=chia_frame_to_bb.compose(__lowerCAmelCase				)

						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(__lowerCAmelCase				)

						return all_frames_to_global





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    )							->					torch.Tensor:




						'''simple docstring'''



						lowerCamelCase__					=group_idx[aatype, ...]

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

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

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

						return pred_positions










"""simple docstring"""



import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"runwayml/stable-diffusion-v1-5"
        UpperCAmelCase__		:  Optional[Any]				=				TextToVideoZeroPipeline.from_pretrained(snake_case__				,       torch_dtype=torch.floataa				).to("cuda"				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device="cuda"				).manual_seed(0				)

        UpperCAmelCase__		:  Any				=				"A bear is playing a guitar on Times Square"
        UpperCAmelCase__		:  Dict				=				pipe(prompt=snake_case__				,       generator=snake_case__				).images

        UpperCAmelCase__		:  List[Any]				=				load_pt(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"				)

        assert_mean_pixel_difference(snake_case__				,       snake_case__				)


