



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  ):




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




     @property
     def 		snake_case_		(					self):
         A__					=       ort.SessionOptions()
         A__					=       False
         return options




     def 		snake_case_		(					self):
         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=a__			,    feature_extractor=a__			,    provider=self.gpu_provider			,    sess_options=self.gpu_options			,    )
         pipe.set_progress_bar_config(disable=a__)

         A__					=       '''A red cat sitting on a park bench'''

         A__					=       np.random.RandomState(0)
         A__					=       pipe(
             prompt=a__			,    image=a__			,    mask_image=a__			,    strength=0.7_5			,    guidance_scale=7.5			,    num_inference_steps=1_5			,    generator=a__			,    output_type='''np'''			,    )

         A__					=       output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image).max() < 1e-2





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
