


"""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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




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






    @property
    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			ort.SessionOptions()
      a						:		str      							=			False
      return options






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
      a						:		List[Any]      							=			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						:		List[Any]      							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
          "CompVis/stable-diffusion-v1-4"				, revision="onnx"				, safety_checker=__UpperCAmelCase				, feature_extractor=__UpperCAmelCase				, provider=self.gpu_provider				, sess_options=self.gpu_options				, )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		int      							=			"A red cat sitting on a park bench"

      a						:		Optional[Any]      							=			np.random.RandomState(0)
      a						:		Optional[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, strength=0.75				, guidance_scale=7.5				, num_inference_steps=15				, generator=__UpperCAmelCase				, output_type="np"				, )

      a						:		str      							=			output.images[0]

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










"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

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






