
"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    Dict						      =			{
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    List[Any]						      =			[
		    '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XmodForCausalLM''',
		    '''XmodForMaskedLM''',
		    '''XmodForMultipleChoice''',
		    '''XmodForQuestionAnswering''',
		    '''XmodForSequenceClassification''',
		    '''XmodForTokenClassification''',
		    '''XmodModel''',
		    '''XmodPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xmod import (
				    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XmodForCausalLM,
				    XmodForMaskedLM,
				    XmodForMultipleChoice,
				    XmodForQuestionAnswering,
				    XmodForSequenceClassification,
				    XmodForTokenClassification,
				    XmodModel,
				    XmodPreTrainedModel,
				)

else:
		import sys

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






"""simple docstring"""





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 __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





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




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







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




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







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								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=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

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

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

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

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






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								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=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

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

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

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

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




