



"""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


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''poolformer'''



       def __init__(							self      :  Optional[int]		,    _UpperCAmelCase      :  Union[str, Any]=3		,    _UpperCAmelCase      :  Union[str, Any]=16		,    _UpperCAmelCase      :  List[str]=16		,    _UpperCAmelCase      :  int=3		,    _UpperCAmelCase      :  Union[str, Any]=4.0		,    _UpperCAmelCase      :  Tuple=[2, 2, 6, 2]		,    _UpperCAmelCase      :  str=[64, 128, 320, 512]		,    _UpperCAmelCase      :  int=[7, 3, 3, 3]		,    _UpperCAmelCase      :  Optional[Any]=[4, 2, 2, 2]		,    _UpperCAmelCase      :  Tuple=[2, 1, 1, 1]		,    _UpperCAmelCase      :  Optional[Any]=4		,    _UpperCAmelCase      :  str=0.0		,    _UpperCAmelCase      :  int="gelu"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  Optional[int]=1e-5		,    _UpperCAmelCase      :  Optional[int]=0.02		,    **_UpperCAmelCase      :  Optional[int]		,    )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							num_channels
             UpperCAmelCase_      			=							patch_size
             UpperCAmelCase_      			=							stride
             UpperCAmelCase_      			=							padding
             UpperCAmelCase_      			=							pool_size
             UpperCAmelCase_      			=							hidden_sizes
             UpperCAmelCase_      			=							mlp_ratio
             UpperCAmelCase_      			=							depths
             UpperCAmelCase_      			=							patch_sizes
             UpperCAmelCase_      			=							strides
             UpperCAmelCase_      			=							num_encoder_blocks
             UpperCAmelCase_      			=							drop_path_rate
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							use_layer_scale
             UpperCAmelCase_      			=							layer_scale_init_value
             UpperCAmelCase_      			=							initializer_range
             super().__init__(**_UpperCAmelCase   )



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					version.parse('''1.11'''				)



       @property
       def 					lowercase__    (							self      :  Optional[Any]   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




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







       @property
       def 					lowercase__    (							self      :  Any   )			->						float:


             '''simple docstring'''




             return 2e-3









"""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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




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







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




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

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

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image   ).max() < 1e-2




