



"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 					a	(  unittest.TestCase       ):



  @property
  def 	lowerCamelCase__       (						self :					Any		)     ->					str:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=UNetaDModel(
         block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,		)
     return model





  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.dummy_uncond_unet
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=ScoreSdeVeScheduler()

     SCREAMING_SNAKE_CASE_: Any     			=ScoreSdeVePipeline(unet=lowerCAmelCase       ,		scheduler=lowerCAmelCase		)
     sde_ve.to(lowerCAmelCase		)
     sde_ve.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sde_ve(num_inference_steps=2       ,		output_type="""numpy"""       ,		generator=lowerCAmelCase		).images

     SCREAMING_SNAKE_CASE_: Any     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sde_ve(num_inference_steps=2       ,		output_type="""numpy"""       ,		generator=lowerCAmelCase       ,		return_dict=lowerCAmelCase		)[
         0
     ]

     SCREAMING_SNAKE_CASE_: Dict     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE_: str     			=np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

     assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""google/ncsnpp-church-256"""
     SCREAMING_SNAKE_CASE_: List[Any]     			=UNetaDModel.from_pretrained(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ScoreSdeVePipeline(unet=lowerCAmelCase       ,		scheduler=lowerCAmelCase		)
     sde_ve.to(lowerCAmelCase		)
     sde_ve.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=sde_ve(num_inference_steps=10       ,		output_type="""numpy"""       ,		generator=lowerCAmelCase		).images

     SCREAMING_SNAKE_CASE_: List[Any]     			=image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)

     SCREAMING_SNAKE_CASE_: List[Any]     			=np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]		)

     assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2








"""simple docstring"""
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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: Any     			=[]

   for rt in rc.restypes:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=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_: Any     			={name: i for i, name in enumerate(lowercase )}
      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(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor(
       lowercase							,			dtype=torch.floataa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=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_: List[str]     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Any     			=restype_atomaa_mask[protein_aatype]

   SCREAMING_SNAKE_CASE_: Tuple     			=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_: Optional[int]     			=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_: int     			=rc.restype_atoa[restype_letter]
      SCREAMING_SNAKE_CASE_: Any     			=rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=rc.atom_order[atom_name]
         SCREAMING_SNAKE_CASE_: Dict     			=1

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

   return protein





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=tree_map(lambda lowercase : torch.tensor(lowercase							,			device=batch["""aatype"""].device )							,			lowercase							,			np.ndarray )
   SCREAMING_SNAKE_CASE_: int     			=tensor_tree_map(lambda lowercase : np.array(lowercase )							,			make_atomaa_masks(lowercase ) )
   return out



