


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						:						str		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						int		  =			{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"roberta-prelayernorm"







					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]=50_265					,lowerCamelCase__     :			str=768					,lowerCamelCase__     :			List[Any]=12					,lowerCamelCase__     :			List[str]=12					,lowerCamelCase__     :			Optional[Any]=3_072					,lowerCamelCase__     :			List[str]="gelu"					,lowerCamelCase__     :			str=0.1					,lowerCamelCase__     :			Tuple=0.1					,lowerCamelCase__     :			str=512					,lowerCamelCase__     :			List[str]=2					,lowerCamelCase__     :			Optional[Any]=0.0_2					,lowerCamelCase__     :			Any=1e-12					,lowerCamelCase__     :			str=1					,lowerCamelCase__     :			Any=0					,lowerCamelCase__     :			Union[str, Any]=2					,lowerCamelCase__     :			Dict="absolute"					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			int=None					,**lowerCamelCase__     :			int					,):
									super().__init__(pad_token_id=__lowerCAmelCase					,bos_token_id=__lowerCAmelCase					,eos_token_id=__lowerCAmelCase					,**__lowerCAmelCase    )

									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							hidden_size
									UpperCAmelCase__    =							num_hidden_layers
									UpperCAmelCase__    =							num_attention_heads
									UpperCAmelCase__    =							hidden_act
									UpperCAmelCase__    =							intermediate_size
									UpperCAmelCase__    =							hidden_dropout_prob
									UpperCAmelCase__    =							attention_probs_dropout_prob
									UpperCAmelCase__    =							max_position_embeddings
									UpperCAmelCase__    =							type_vocab_size
									UpperCAmelCase__    =							initializer_range
									UpperCAmelCase__    =							layer_norm_eps
									UpperCAmelCase__    =							position_embedding_type
									UpperCAmelCase__    =							use_cache
									UpperCAmelCase__    =							classifier_dropout






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					@property
					def 	__lowerCAmelCase		(       self     :			Dict    ):







									if self.task == "multiple-choice":
													UpperCAmelCase__    =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
													UpperCAmelCase__    =							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]    )




"""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


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

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

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



