



'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase_  :							Optional[Any]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							Tuple             =    {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}
class 						lowerCamelCase_				(       __A     ,					__A      ):
						_lowerCAmelCase  : Tuple						  =					'swin'

						_lowerCAmelCase  : str						  =					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(	self :   Union[str, Any]					,     lowerCAmelCase__ :   Tuple=2_24					,     lowerCAmelCase__ :   Optional[Any]=4					,     lowerCAmelCase__ :   Optional[Any]=3					,     lowerCAmelCase__ :   Optional[Any]=96					,     lowerCAmelCase__ :   Union[str, Any]=[2, 2, 6, 2]					,     lowerCAmelCase__ :   Tuple=[3, 6, 12, 24]					,     lowerCAmelCase__ :   Union[str, Any]=7					,     lowerCAmelCase__ :   List[str]=4.0					,     lowerCAmelCase__ :   Optional[Any]=True					,     lowerCAmelCase__ :   Union[str, Any]=0.0					,     lowerCAmelCase__ :   Optional[int]=0.0					,     lowerCAmelCase__ :   Union[str, Any]=0.1					,     lowerCAmelCase__ :   List[Any]="gelu"					,     lowerCAmelCase__ :   Union[str, Any]=False					,     lowerCAmelCase__ :   Any=0.02					,     lowerCAmelCase__ :   str=1e-5					,     lowerCAmelCase__ :   Any=32					,     lowerCAmelCase__ :   Optional[int]=None					,     lowerCAmelCase__ :   str=None					,     **lowerCAmelCase__ :   Dict					,     ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   int         =      image_size
													SCREAMING_SNAKE_CASE       :   Any         =      patch_size
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      num_channels
													SCREAMING_SNAKE_CASE       :   Any         =      embed_dim
													SCREAMING_SNAKE_CASE       :   List[str]         =      depths
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      len(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Dict         =      num_heads
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      window_size
													SCREAMING_SNAKE_CASE       :   List[Any]         =      mlp_ratio
													SCREAMING_SNAKE_CASE       :   int         =      qkv_bias
													SCREAMING_SNAKE_CASE       :   List[Any]         =      hidden_dropout_prob
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE       :   List[Any]         =      drop_path_rate
													SCREAMING_SNAKE_CASE       :   int         =      hidden_act
													SCREAMING_SNAKE_CASE       :   int         =      use_absolute_embeddings
													SCREAMING_SNAKE_CASE       :   Tuple         =      layer_norm_eps
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      initializer_range
													SCREAMING_SNAKE_CASE       :   Tuple         =      encoder_stride
													# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE       :   int         =      int(embed_dim * 2 ** (len(lowerCAmelCase__					) - 1)					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ["stem"] + [F"""stage{idx}""" for idx in range(1					,     len(lowerCAmelCase__					) + 1					)]
													SCREAMING_SNAKE_CASE       :   Any         =      get_aligned_output_features_output_indices(
													    out_features=lowerCAmelCase__					,     out_indices=lowerCAmelCase__					,     stage_names=self.stage_names					)


class 						lowerCamelCase_				(       __A      ):
						_lowerCAmelCase  : Optional[Any]						  =					version.parse('1.11'      )







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													    ]					)







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return 1e-4







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