





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     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   # FIXME: add fast tests
   pass






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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       List[Any]:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Tuple:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowercase__           =  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	)

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

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

     assert images.shape == (1, 512, 512, 3)
     lowercase__           =  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      :    Dict	)->       Tuple:





     """simple docstring"""







     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowercase__           =  LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'      ,					subfolder='scheduler'      ,					revision='onnx'	)
     lowercase__           =  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	)

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

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

     assert images.shape == (1, 512, 512, 3)
     lowercase__           =  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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

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


