

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     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       pass






@nightly
@require_onnxruntime
@require_torch_gpu
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       @property
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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


       @property
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/in_paint/overture-creations-5sI6fQgYIuo.png"""   )
              __snake_case       	=							load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""   )
              __snake_case       	=							OnnxStableDiffusionInpaintPipeline.from_pretrained(
                  """runwayml/stable-diffusion-inpainting"""      , revision="""onnx"""      , safety_checker=_UpperCamelCase      , feature_extractor=_UpperCamelCase      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

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

              __snake_case       	=							np.random.RandomState(0   )
              __snake_case       	=							pipe(
                  prompt=_UpperCamelCase      , image=_UpperCamelCase      , mask_image=_UpperCamelCase      , guidance_scale=7.5      , num_inference_steps=10      , generator=_UpperCamelCase      , output_type="""np"""      , )
              __snake_case       	=							output.images
              __snake_case       	=							images[0, 2_55:2_58, 2_55:2_58, -1]

              assert images.shape == (1, 5_12, 5_12, 3)
              __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 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/in_paint/overture-creations-5sI6fQgYIuo.png"""   )
              __snake_case       	=							load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""   )
              __snake_case       	=							LMSDiscreteScheduler.from_pretrained(
                  """runwayml/stable-diffusion-inpainting"""      , subfolder="""scheduler"""      , revision="""onnx"""   )
              __snake_case       	=							OnnxStableDiffusionInpaintPipeline.from_pretrained(
                  """runwayml/stable-diffusion-inpainting"""      , revision="""onnx"""      , scheduler=_UpperCamelCase      , safety_checker=_UpperCamelCase      , feature_extractor=_UpperCamelCase      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

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

              __snake_case       	=							np.random.RandomState(0   )
              __snake_case       	=							pipe(
                  prompt=_UpperCamelCase      , image=_UpperCamelCase      , mask_image=_UpperCamelCase      , guidance_scale=7.5      , num_inference_steps=20      , generator=_UpperCamelCase      , output_type="""np"""      , )
              __snake_case       	=							output.images
              __snake_case       	=							images[0, 2_55:2_58, 2_55:2_58, -1]

              assert images.shape == (1, 5_12, 5_12, 3)
              __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 . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

