

from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[]
						SCREAMING_SNAKE_CASE_:			str  		=	[]
						SCREAMING_SNAKE_CASE_:			Tuple  		=	[]

						for rt in rc.restypes:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]   )
												SCREAMING_SNAKE_CASE_:			str  		=	{name: i for i, name in enumerate(_UpperCAmelCase   )}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]   )

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]   )

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 14   )
						restype_atomaa_to_atomaa_list.append([0] * 37   )
						restype_atomaa_mask_list.append([0.0] * 14   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor(
						    _UpperCAmelCase    ,     dtype=torch.intaa    ,     device=protein["aatype"].device    ,     )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor(
						    _UpperCAmelCase    ,     dtype=torch.intaa    ,     device=protein["aatype"].device    ,     )
						SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor(
						    _UpperCAmelCase    ,     dtype=torch.floataa    ,     device=protein["aatype"].device    ,     )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	protein["aatype"].to(torch.long   )

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						SCREAMING_SNAKE_CASE_:			int  		=	restype_atomaa_to_atomaa[protein_aatype]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	restype_atomaa_mask[protein_aatype]

						SCREAMING_SNAKE_CASE_:			List[str]  		=	residx_atomaa_mask
						SCREAMING_SNAKE_CASE_:			Dict  		=	residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						SCREAMING_SNAKE_CASE_:			Dict  		=	restype_atomaa_to_atomaa[protein_aatype]
						SCREAMING_SNAKE_CASE_:			Any  		=	residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.zeros([21, 37]    ,     dtype=torch.floataa    ,     device=protein["aatype"].device   )
						for restype, restype_letter in enumerate(rc.restypes   ):
												SCREAMING_SNAKE_CASE_:			str  		=	rc.restype_atoa[restype_letter]
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	rc.atom_order[atom_name]
																		SCREAMING_SNAKE_CASE_:			Dict  		=	1

						SCREAMING_SNAKE_CASE_:			List[str]  		=	restype_atomaa_mask[protein_aatype]
						SCREAMING_SNAKE_CASE_:			List[str]  		=	residx_atomaa_mask

						return protein







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	tree_map(lambda _UpperCAmelCase   : torch.tensor(_UpperCAmelCase    ,     device=batch["aatype"].device   )    ,     _UpperCAmelCase    ,     np.ndarray   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tensor_tree_map(lambda _UpperCAmelCase   : np.array(_UpperCAmelCase   )    ,     make_atomaa_masks(_UpperCAmelCase   )   )
						return out









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






