
"""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 				A__ (			unittest.TestCase):

		@property
		def 							__lowerCamelCase							(   self				):
			return (
			    "CUDAExecutionProvider",
			    {
			        "gpu_mem_limit": "15000000000",  # 15GB
			        "arena_extend_strategy": "kSameAsRequested",
			    },
			)

		@property
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   ort.SessionOptions()
			__lowerCAmelCase    :		Optional[Any]		    =   False
			return options







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

			__lowerCAmelCase    :		Optional[Any]		    =   'A red cat sitting on a park bench'

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

			__lowerCAmelCase    :		Any		    =   output.images[0]

			assert image.shape == (5_12, 5_12, 3)
			assert np.abs(expected_image - image				).max() < 1E-2

"""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__           =	{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'beit'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=81_92 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=2_24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[3, 5, 7, 11] ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 6] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		Dict		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
			__lowerCAmelCase    :		List[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   initializer_range
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm_eps

			__lowerCAmelCase    :		Any		    =   image_size
			__lowerCAmelCase    :		Union[str, Any]		    =   patch_size
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   use_mask_token
			__lowerCAmelCase    :		Any		    =   use_absolute_position_embeddings
			__lowerCAmelCase    :		int		    =   use_relative_position_bias
			__lowerCAmelCase    :		List[str]		    =   use_shared_relative_position_bias
			__lowerCAmelCase    :		Tuple		    =   layer_scale_init_value
			__lowerCAmelCase    :		int		    =   drop_path_rate
			__lowerCAmelCase    :		Dict		    =   use_mean_pooling
			# decode head attributes (semantic segmentation)
			__lowerCAmelCase    :		Optional[Any]		    =   out_indices
			__lowerCAmelCase    :		Optional[Any]		    =   pool_scales
			# auxiliary head attributes (semantic segmentation)
			__lowerCAmelCase    :		Union[str, Any]		    =   use_auxiliary_head
			__lowerCAmelCase    :		Optional[int]		    =   auxiliary_loss_weight
			__lowerCAmelCase    :		Optional[Any]		    =   auxiliary_channels
			__lowerCAmelCase    :		List[str]		    =   auxiliary_num_convs
			__lowerCAmelCase    :		List[str]		    =   auxiliary_concat_input
			__lowerCAmelCase    :		Optional[int]		    =   semantic_loss_ignore_index




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]				)







		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4