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        lowercase_   (					__snake_case						,			unittest.TestCase		):
							# FIXME: add fast tests
							pass




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



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



							@property
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   ort.SessionOptions()
													_snake_case						:					List[Any]    =   False
													return options



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
													_snake_case						:					str    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
													_snake_case						:					Any    =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    revision="onnx"				,    safety_checker=lowercase_				,    feature_extractor=lowercase_				,    provider=self.gpu_provider				,    sess_options=self.gpu_options				,    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Optional[int]    =   "A red cat sitting on a park bench"

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

													assert images.shape == (1, 512, 512, 3)
													_snake_case						:					int    =   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    ):
													_snake_case						:					Optional[Any]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
													_snake_case						:					List[Any]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
													_snake_case						:					Any    =   LMSDiscreteScheduler.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    subfolder="scheduler"				,    revision="onnx"    )
													_snake_case						:					int    =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    revision="onnx"				,    scheduler=lowercase_				,    safety_checker=lowercase_				,    feature_extractor=lowercase_				,    provider=self.gpu_provider				,    sess_options=self.gpu_options				,    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					str    =   "A red cat sitting on a park bench"

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

													assert images.shape == (1, 512, 512, 3)
													_snake_case						:					Tuple    =   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
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs