

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__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  pass






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






  """simple docstring"""


  @property
  def 				lowercase_		(							self						:		List[Any] )		->							List[Any]:
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )






  @property
  def 				lowercase_		(							self						:		Any )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   ort.SessionOptions()
       SCREAMING_SNAKE_CASE__   =   False
       return options






  def 				lowercase_		(							self						:		Any )		->							str:
       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=__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     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__lowerCamelCase     ,       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 				lowercase_		(							self						:		Dict )		->							List[Any]:
       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=__lowerCamelCase     ,       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     ,       guidance_scale=7.5     ,       num_inference_steps=20     ,       generator=__lowerCamelCase     ,       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





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


