



"""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  lowerCamelCase_						(UpperCamelCase__		:		Dict[str, torch.Tensor]							):
    _UpperCAmelCase							:  Any											=    []
    _UpperCAmelCase							:  Optional[Any]											=    []
    _UpperCAmelCase							:  Union[str, Any]											=    []

    for rt in rc.restypes:
        _UpperCAmelCase							:  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]							)
        _UpperCAmelCase							:  Any											=    {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							)

    _UpperCAmelCase							:  int											=    torch.tensor(
        __UpperCAmelCase     ,       dtype=torch.intaa     ,       device=protein['''aatype'''].device     ,       )
    _UpperCAmelCase							:  List[Any]											=    torch.tensor(
        __UpperCAmelCase     ,       dtype=torch.intaa     ,       device=protein['''aatype'''].device     ,       )
    _UpperCAmelCase							:  Optional[Any]											=    torch.tensor(
        __UpperCAmelCase     ,       dtype=torch.floataa     ,       device=protein['''aatype'''].device     ,       )
    _UpperCAmelCase							:  Union[str, 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
    _UpperCAmelCase							:  List[Any]											=    restype_atomaa_to_atomaa[protein_aatype]
    _UpperCAmelCase							:  Dict											=    restype_atomaa_mask[protein_aatype]

    _UpperCAmelCase							:  Union[str, Any]											=    residx_atomaa_mask
    _UpperCAmelCase							:  Any											=    residx_atomaa_to_atomaa.long()

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

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

    _UpperCAmelCase							:  str											=    restype_atomaa_mask[protein_aatype]
    _UpperCAmelCase							:  str											=    residx_atomaa_mask

    return protein





def  lowerCamelCase_						(UpperCamelCase__		:		Dict[str, torch.Tensor]							):
    _UpperCAmelCase							:  Union[str, Any]											=    tree_map(lambda UpperCamelCase__							: torch.tensor(__UpperCAmelCase     ,       device=batch['''aatype'''].device							)     ,       __UpperCAmelCase     ,       np.ndarray							)
    _UpperCAmelCase							:  List[Any]											=    tensor_tree_map(lambda UpperCamelCase__							: np.array(__UpperCAmelCase							)     ,       make_atomaa_masks(__UpperCAmelCase							)							)
    return out








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2