'''simple docstring'''





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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @property
    def       _UpperCamelCase    (       self )							-> Dict:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






    @property
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       ort.SessionOptions()
         SCREAMING_SNAKE_CASE					:			Dict    	=       False
         return options






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
         SCREAMING_SNAKE_CASE					:			str    	=       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					:			int    	=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
             'CompVis/stable-diffusion-v1-4'    ,							revision='onnx'    ,							safety_checker=lowercase__    ,							feature_extractor=lowercase__    ,							provider=self.gpu_provider    ,							sess_options=self.gpu_options    ,							)
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'A red cat sitting on a park bench'

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       np.random.RandomState(0 )
         SCREAMING_SNAKE_CASE					:			str    	=       pipe(
             prompt=lowercase__    ,							image=lowercase__    ,							mask_image=lowercase__    ,							strength=0.7_5    ,							guidance_scale=7.5    ,							num_inference_steps=15    ,							generator=lowercase__    ,							output_type='np'    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       output.images[0]

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




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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



