


"""simple docstring"""

import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
       import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase__						(		unittest.TestCase    ):
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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






       '''simple docstring'''


       __lowercase									=	ort.SessionOptions()
       __lowercase									=	False
       return options






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/in_paint/overture-creations-5sI6fQgYIuo.png"""							)
       __lowercase									=	load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""							)
       __lowercase									=	load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""							)

       # using the PNDM scheduler by default
       __lowercase									=	OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""      ,       revision="""onnx"""      ,       safety_checker=__UpperCamelCase      ,       feature_extractor=__UpperCamelCase      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
       pipe.set_progress_bar_config(disable=__UpperCamelCase							)

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

       __lowercase									=	np.random.RandomState(0							)
       __lowercase									=	pipe(
           prompt=__UpperCamelCase      ,       image=__UpperCamelCase      ,       mask_image=__UpperCamelCase      ,       strength=0.75      ,       guidance_scale=7.5      ,       num_inference_steps=1_5      ,       generator=__UpperCamelCase      ,       output_type="""np"""      ,       )

       __lowercase									=	output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image							).max() < 1e-2




"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	(0, 0, 0, 0)
