


from typing import List

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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}





class   __a			(    lowerCAmelCase_      ):
 __lowercase :							List[str]        = 'efficientformer'
 def __init__(   self				, lowerCAmelCase__ = [3, 2, 6, 4]				, lowerCAmelCase__ = [48, 96, 224, 448]				, lowerCAmelCase__ = [True, True, True, True]				, lowerCAmelCase__ = 448				, lowerCAmelCase__ = 32				, lowerCAmelCase__ = 4				, lowerCAmelCase__ = 7				, lowerCAmelCase__ = 5				, lowerCAmelCase__ = 8				, lowerCAmelCase__ = 4				, lowerCAmelCase__ = 0.0				, lowerCAmelCase__ = 16				, lowerCAmelCase__ = 3				, lowerCAmelCase__ = 3				, lowerCAmelCase__ = 3				, lowerCAmelCase__ = 2				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 0.0				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = True				, lowerCAmelCase__ = True				, lowerCAmelCase__ = 1E-5				, lowerCAmelCase__ = "gelu"				, lowerCAmelCase__ = 0.0_2				, lowerCAmelCase__ = 1E-12				, lowerCAmelCase__ = 224				, lowerCAmelCase__ = 1E-05				, **lowerCAmelCase__				, )					->				None:




        '''simple docstring'''
        super().__init__(**__snake_case )

        lowercase__:   int  			=   hidden_act
        lowercase__:   int  			=   hidden_dropout_prob
        lowercase__:   Optional[Any]  			=   hidden_sizes
        lowercase__:   Optional[Any]  			=   num_hidden_layers
        lowercase__:   Any  			=   num_attention_heads
        lowercase__:   Union[str, Any]  			=   initializer_range
        lowercase__:   Optional[int]  			=   layer_norm_eps
        lowercase__:   str  			=   patch_size
        lowercase__:   Optional[Any]  			=   num_channels
        lowercase__:   List[Any]  			=   depths
        lowercase__:   List[Any]  			=   mlp_expansion_ratio
        lowercase__:   Optional[Any]  			=   downsamples
        lowercase__:   Any  			=   dim
        lowercase__:   Dict  			=   key_dim
        lowercase__:   str  			=   attention_ratio
        lowercase__:   Union[str, Any]  			=   resolution
        lowercase__:   str  			=   pool_size
        lowercase__:   int  			=   downsample_patch_size
        lowercase__:   int  			=   downsample_stride
        lowercase__:   Tuple  			=   downsample_pad
        lowercase__:   Tuple  			=   drop_path_rate
        lowercase__:   int  			=   num_metaad_blocks
        lowercase__:   int  			=   distillation
        lowercase__:   List[str]  			=   use_layer_scale
        lowercase__:   List[str]  			=   layer_scale_init_value
        lowercase__:   str  			=   image_size
        lowercase__:   Optional[int]  			=   batch_norm_eps





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[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

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

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

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

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






