


'''simple docstring'''



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


A__		:					int												=  logging.get_logger(__name__)

A__		:					str												=  {
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Any							     =			'glpn'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]			,     SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]			,     SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]			,     SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]			,     SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]			,     SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]			,     SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]			,     SCREAMING_SNAKE_CASE_="gelu"			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=1E-6			,     SCREAMING_SNAKE_CASE_=64			,     SCREAMING_SNAKE_CASE_=10			,     SCREAMING_SNAKE_CASE_=-1			,     **SCREAMING_SNAKE_CASE_			,     )				->     List[Any]:
         super().__init__(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		num_channels
         __lowerCamelCase   :	Any								=		num_encoder_blocks
         __lowerCamelCase   :	Tuple								=		depths
         __lowerCamelCase   :	str								=		sr_ratios
         __lowerCamelCase   :	Any								=		hidden_sizes
         __lowerCamelCase   :	Union[str, Any]								=		patch_sizes
         __lowerCamelCase   :	Dict								=		strides
         __lowerCamelCase   :	Optional[Any]								=		mlp_ratios
         __lowerCamelCase   :	Optional[int]								=		num_attention_heads
         __lowerCamelCase   :	str								=		hidden_act
         __lowerCamelCase   :	Optional[Any]								=		hidden_dropout_prob
         __lowerCamelCase   :	str								=		attention_probs_dropout_prob
         __lowerCamelCase   :	Dict								=		initializer_range
         __lowerCamelCase   :	List[Any]								=		drop_path_rate
         __lowerCamelCase   :	Union[str, Any]								=		layer_norm_eps
         __lowerCamelCase   :	int								=		decoder_hidden_size
         __lowerCamelCase   :	Union[str, Any]								=		max_depth
         __lowerCamelCase   :	Optional[Any]								=		head_in_index










'''simple docstring'''



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_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






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







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







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






