



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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ort.SessionOptions()
						a					     =   False
						return options




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
						a					     =   load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
						a					     =   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
						a					     =   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_		)

						a					     =   "A red cat sitting on a park bench"

						a					     =   np.random.RandomState(0		)
						a					     =   pipe(
						    prompt=lowerCamelCase_		,       image=lowerCamelCase_		,       mask_image=lowerCamelCase_		,       strength=0.75		,       guidance_scale=7.5		,       num_inference_steps=15		,       generator=lowerCamelCase_		,       output_type="np"		,       )

						a					     =   output.images[0]

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








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			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				lowercase__    =			[]
				lowercase__    =			[]

				for rt in rc.restypes:
								lowercase__    =			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]				)
								lowercase__    =			{name: i for i, name in enumerate(lowerCamelCase_				)}
								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				)

				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.floataa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			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
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			restype_atomaa_mask[protein_aatype]

				lowercase__    =			residx_atomaa_mask
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			residx_atomaa_to_atomaa.long()

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

				lowercase__    =			restype_atomaa_mask[protein_aatype]
				lowercase__    =			residx_atomaa_mask

				return protein






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			tree_map(lambda lowerCamelCase_				: torch.tensor(lowerCamelCase_		,   device=batch['''aatype'''].device				)		,   lowerCamelCase_		,   np.ndarray				)
				lowercase__    =			tensor_tree_map(lambda lowerCamelCase_				: np.array(lowerCamelCase_				)		,   make_atomaa_masks(lowerCamelCase_				)				)
				return out




