




'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:	Dict	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	List[str]	     =		{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   int										=      '''cvt'''


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=[7, 3, 3]     ,	__SCREAMING_SNAKE_CASE=[4, 2, 2]     ,	__SCREAMING_SNAKE_CASE=[2, 1, 1]     ,	__SCREAMING_SNAKE_CASE=[64, 192, 384]     ,	__SCREAMING_SNAKE_CASE=[1, 3, 6]     ,	__SCREAMING_SNAKE_CASE=[1, 2, 10]     ,	__SCREAMING_SNAKE_CASE=[4.0, 4.0, 4.0]     ,	__SCREAMING_SNAKE_CASE=[0.0, 0.0, 0.0]     ,	__SCREAMING_SNAKE_CASE=[0.0, 0.0, 0.0]     ,	__SCREAMING_SNAKE_CASE=[0.0, 0.0, 0.1]     ,	__SCREAMING_SNAKE_CASE=[True, True, True]     ,	__SCREAMING_SNAKE_CASE=[False, False, True]     ,	__SCREAMING_SNAKE_CASE=["dw_bn", "dw_bn", "dw_bn"]     ,	__SCREAMING_SNAKE_CASE=[3, 3, 3]     ,	__SCREAMING_SNAKE_CASE=[1, 1, 1]     ,	__SCREAMING_SNAKE_CASE=[2, 2, 2]     ,	__SCREAMING_SNAKE_CASE=[1, 1, 1]     ,	__SCREAMING_SNAKE_CASE=[1, 1, 1]     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-12     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	num_channels
            __snake_case            =	patch_sizes
            __snake_case            =	patch_stride
            __snake_case            =	patch_padding
            __snake_case            =	embed_dim
            __snake_case            =	num_heads
            __snake_case            =	depth
            __snake_case            =	mlp_ratio
            __snake_case            =	attention_drop_rate
            __snake_case            =	drop_rate
            __snake_case            =	drop_path_rate
            __snake_case            =	qkv_bias
            __snake_case            =	cls_token
            __snake_case            =	qkv_projection_method
            __snake_case            =	kernel_qkv
            __snake_case            =	padding_kv
            __snake_case            =	stride_kv
            __snake_case            =	padding_q
            __snake_case            =	stride_q
            __snake_case            =	initializer_range
            __snake_case            =	layer_norm_eps









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 ):
			'''simple docstring'''







			pass






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







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

				"""simple docstring"""

				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)
			@property
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							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						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   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