




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__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				pass




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



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





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     int:
							a    =			ort.SessionOptions()
							a    =			False
							return options





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     List[str]:
							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    =			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							)

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

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

							assert images.shape == (1, 5_12, 5_12, 3)
							a    =			np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							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    =			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-inpainting"					,      subfolder="scheduler"					,      revision="onnx"							)
							a    =			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							)

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

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

							assert images.shape == (1, 5_12, 5_12, 3)
							a    =			np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase    :     Any   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
