
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        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):
			# FIXME: add fast tests
			pass




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

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

			@property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Optional[int]	=						ort.SessionOptions()
							UpperCamelCase_:							Any	=						False
							return options

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""      )
							UpperCamelCase_:							Optional[Any]	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""      )
							UpperCamelCase_:							List[Any]	=						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_      )

							UpperCamelCase_:							Tuple	=						"A red cat sitting on a park bench"

							UpperCamelCase_:							Optional[Any]	=						np.random.RandomState(0      )
							UpperCamelCase_:							List[str]	=						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"""						,	)
							UpperCamelCase_:							int	=						output.images
							UpperCamelCase_:							Optional[int]	=						images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:							Any	=						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       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Optional[Any]	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""      )
							UpperCamelCase_:							List[str]	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""      )
							UpperCamelCase_:							Optional[Any]	=						LMSDiscreteScheduler.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""						,	subfolder="""scheduler"""						,	revision="""onnx"""      )
							UpperCamelCase_:							Union[str, Any]	=						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_      )

							UpperCamelCase_:							Tuple	=						"A red cat sitting on a park bench"

							UpperCamelCase_:							Dict	=						np.random.RandomState(0      )
							UpperCamelCase_:							Optional[Any]	=						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"""						,	)
							UpperCamelCase_:							Tuple	=						output.images
							UpperCamelCase_:							str	=						images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:							List[Any]	=						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








# 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


lowerCamelCase_						: List[str]         =  {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MgpstrModel""",
							    """MgpstrPreTrainedModel""",
							    """MgpstrForSceneTextRecognition""",
							]

if TYPE_CHECKING:
							from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
							from .processing_mgp_str import MgpstrProcessor
							from .tokenization_mgp_str import MgpstrTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mgp_str import (
														    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MgpstrForSceneTextRecognition,
														    MgpstrModel,
														    MgpstrPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






