





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort

class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     pass

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



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[Any]:




           '''simple docstring'''


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






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Tuple:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		str:




           '''simple docstring'''


           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		=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""						,revision="""onnx"""						,safety_checker=A__						,feature_extractor=A__						,provider=self.gpu_provider						,sess_options=self.gpu_options						,)
           pipe.set_progress_bar_config(disable=A__       )

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

           SCREAMING_SNAKE_CASE		=						np.random.RandomState(0       )
           SCREAMING_SNAKE_CASE		=						pipe(
               prompt=A__						,image=A__						,mask_image=A__						,guidance_scale=7.5						,num_inference_steps=10						,generator=A__						,output_type="""np"""						,)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						images[0, 255:258, 255:258, -1]

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		List[Any]:




           '''simple docstring'''


           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		=						LMSDiscreteScheduler.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""						,subfolder="""scheduler"""						,revision="""onnx"""       )
           SCREAMING_SNAKE_CASE		=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""						,revision="""onnx"""						,scheduler=A__						,safety_checker=A__						,feature_extractor=A__						,provider=self.gpu_provider						,sess_options=self.gpu_options						,)
           pipe.set_progress_bar_config(disable=A__       )

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

           SCREAMING_SNAKE_CASE		=						np.random.RandomState(0       )
           SCREAMING_SNAKE_CASE		=						pipe(
               prompt=A__						,image=A__						,mask_image=A__						,guidance_scale=7.5						,num_inference_steps=20						,generator=A__						,output_type="""np"""						,)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						images[0, 255:258, 255:258, -1]

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3





import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


A_       : Union[str, Any]         =			logging.get_logger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						warnings.warn(
						    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
						    """ use DonutImageProcessor instead."""    ,       A__    ,       )
						super().__init__(*A__    ,       **A__			)



