



'''simple docstring'''


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


_lowerCamelCase	:       Tuple      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Union[str, Any]      	=	{
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """mra"""






    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]=5_0_2_6_5  ,						UpperCamelCase__      :					Optional[Any]=7_6_8  ,						UpperCamelCase__      :					Dict=1_2  ,						UpperCamelCase__      :					Optional[int]=1_2  ,						UpperCamelCase__      :					int=3_0_7_2  ,						UpperCamelCase__      :					Optional[int]="gelu"  ,						UpperCamelCase__      :					str=0.1  ,						UpperCamelCase__      :					Optional[int]=0.1  ,						UpperCamelCase__      :					str=5_1_2  ,						UpperCamelCase__      :					Optional[int]=1  ,						UpperCamelCase__      :					Optional[Any]=0.0_2  ,						UpperCamelCase__      :					int=1E-5  ,						UpperCamelCase__      :					str="absolute"  ,						UpperCamelCase__      :					Any=4  ,						UpperCamelCase__      :					Optional[Any]="full"  ,						UpperCamelCase__      :					Optional[Any]=0  ,						UpperCamelCase__      :					Union[str, Any]=0  ,						UpperCamelCase__      :					List[str]=1  ,						UpperCamelCase__      :					List[Any]=0  ,						UpperCamelCase__      :					Dict=2  ,						**UpperCamelCase__      :					Any  ,						):


       """simple docstring"""







       super().__init__(pad_token_id=UpperCamelCase__  ,						bos_token_id=UpperCamelCase__  ,						eos_token_id=UpperCamelCase__  ,						**UpperCamelCase__ )

       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  hidden_dropout_prob
       UpperCamelCase					=  attention_probs_dropout_prob
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  type_vocab_size
       UpperCamelCase					=  layer_norm_eps
       UpperCamelCase					=  position_embedding_type
       UpperCamelCase					=  block_per_row
       UpperCamelCase					=  approx_mode
       UpperCamelCase					=  initial_prior_first_n_blocks
       UpperCamelCase					=  initial_prior_diagonal_n_blocks









'''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


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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

