


"""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 			lowerCamelCase__						(   __UpperCAmelCase							,    unittest.TestCase			):
      pass



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







      @property
      def __a						(							self   :    Optional[Any]		):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )







      @property
      def __a						(							self   :    str		):
        A					       = ort.SessionOptions()
        A					       = False
        return options







      def __a						(							self   :    int		):
        A					       = load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo.png'		)
        A					       = load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'		)
        A					       = OnnxStableDiffusionInpaintPipeline.from_pretrained(
            'runwayml/stable-diffusion-inpainting'			,			revision='onnx'			,			safety_checker=_lowerCamelCase			,			feature_extractor=_lowerCamelCase			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
        pipe.set_progress_bar_config(disable=_lowerCamelCase		)

        A					       = 'A red cat sitting on a park bench'

        A					       = np.random.RandomState(0		)
        A					       = pipe(
            prompt=_lowerCamelCase			,			image=_lowerCamelCase			,			mask_image=_lowerCamelCase			,			guidance_scale=7.5			,			num_inference_steps=10			,			generator=_lowerCamelCase			,			output_type='np'			,			)
        A					       = output.images
        A					       = images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        A					       = 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 __a						(							self   :    Optional[Any]		):
        A					       = load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo.png'		)
        A					       = load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'		)
        A					       = LMSDiscreteScheduler.from_pretrained(
            'runwayml/stable-diffusion-inpainting'			,			subfolder='scheduler'			,			revision='onnx'		)
        A					       = OnnxStableDiffusionInpaintPipeline.from_pretrained(
            'runwayml/stable-diffusion-inpainting'			,			revision='onnx'			,			scheduler=_lowerCamelCase			,			safety_checker=_lowerCamelCase			,			feature_extractor=_lowerCamelCase			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
        pipe.set_progress_bar_config(disable=_lowerCamelCase		)

        A					       = 'A red cat sitting on a park bench'

        A					       = np.random.RandomState(0		)
        A					       = pipe(
            prompt=_lowerCamelCase			,			image=_lowerCamelCase			,			mask_image=_lowerCamelCase			,			guidance_scale=7.5			,			num_inference_steps=20			,			generator=_lowerCamelCase			,			output_type='np'			,			)
        A					       = output.images
        A					       = images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        A					       = 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









"""simple docstring"""


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


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





