





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE      	=		False
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):
						pass
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    str:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'	)
							pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Any											=	pipe(
							    image=_A		,   generator=_A		,   guidance_scale=7.5		,   num_inference_steps=50		,   output_type='numpy'		,   ).images

							snake_case_		:			List[Any]											=	image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							snake_case_		:			Optional[int]											=	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2










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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "lilt"






						def __init__(      self      :      Any		,   _A      :      Dict=30522		,   _A      :      List[Any]=768		,   _A      :      str=12		,   _A      :      int=12		,   _A      :      Optional[int]=3072		,   _A      :      Optional[Any]="gelu"		,   _A      :      Dict=0.1		,   _A      :      Tuple=0.1		,   _A      :      str=512		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[int]=0		,   _A      :      Dict="absolute"		,   _A      :      Any=None		,   _A      :      Optional[Any]=4		,   _A      :      Any=1024		,   **_A      :      Union[str, Any]		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   **_A	)

							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Dict											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Tuple											=	hidden_act
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	max_position_embeddings
							snake_case_		:			Any											=	type_vocab_size
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			List[str]											=	position_embedding_type
							snake_case_		:			Optional[int]											=	classifier_dropout
							snake_case_		:			Dict											=	channel_shrink_ratio
							snake_case_		:			Tuple											=	max_ad_position_embeddings

