

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


_UpperCAmelCase		:   Union[str, Any]										=       logging.get_logger(__name__)

_UpperCAmelCase		:   Tuple										=       {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}







class  lowercase				(       lowerCAmelCase_	,  lowerCAmelCase_			):
				__SCREAMING_SNAKE_CASE					: int       			=			'''bit'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			['''preactivation''', '''bottleneck''']
				__SCREAMING_SNAKE_CASE					: Tuple       			=			['''SAME''', '''VALID''']




				def __init__(					self      ,   snake_case=3      ,   snake_case=64      ,   snake_case=[256, 512, 1024, 2048]      ,   snake_case=[3, 4, 6, 3]      ,   snake_case="preactivation"      ,   snake_case="relu"      ,   snake_case=None      ,   snake_case=32      ,   snake_case=0.0      ,   snake_case=False      ,   snake_case=32      ,   snake_case=1      ,   snake_case=None      ,   snake_case=None      ,   **snake_case      ,   ):
											super().__init__(**__a       )
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types       )}'''       )
											if global_padding is not None:
																		if global_padding.upper() in self.supported_padding:
																									snake_case_							=    global_padding.upper()
																		else:
																									raise ValueError(F'''Padding strategy {global_padding} not supported'''       )
											snake_case_							=    num_channels
											snake_case_							=    embedding_size
											snake_case_							=    hidden_sizes
											snake_case_							=    depths
											snake_case_							=    layer_type
											snake_case_							=    hidden_act
											snake_case_							=    global_padding
											snake_case_							=    num_groups
											snake_case_							=    drop_path_rate
											snake_case_							=    embedding_dynamic_padding
											snake_case_							=    output_stride
											snake_case_							=    width_factor

											snake_case_							=    ['stem'] + [F'''stage{idx}''' for idx in range(1      ,   len(__a       ) + 1       )]
											snake_case_							=    get_aligned_output_features_output_indices(
											    out_features=__a      ,   out_indices=__a      ,   stage_names=self.stage_names       )








'''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 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  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
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

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



