




'''simple docstring'''





import logging

from transformers import PretrainedConfig


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			logging.getLogger(__name__)


_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Tuple							=   "bertabs"




			def __init__( self    ,							a__=30_522    ,							a__=512    ,							a__=6    ,							a__=512    ,							a__=8    ,							a__=512    ,							a__=0.2    ,							a__=6    ,							a__=768    ,							a__=8    ,							a__=2_048    ,							a__=0.2    ,							**a__    ,							)    ->    List[Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				vocab_size
										snake_case_      =				max_pos

										snake_case_      =				enc_layers
										snake_case_      =				enc_hidden_size
										snake_case_      =				enc_heads
										snake_case_      =				enc_ff_size
										snake_case_      =				enc_dropout

										snake_case_      =				dec_layers
										snake_case_      =				dec_hidden_size
										snake_case_      =				dec_heads
										snake_case_      =				dec_ff_size
										snake_case_      =				dec_dropout










'''simple docstring'''





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							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




@slow
@require_torch_gpu
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

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

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

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				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




