



"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase			:			Union[str, Any]										=						logging.get_logger(__name__)

_lowercase			:			int										=						{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 	__SCREAMING_SNAKE_CASE     (				_UpperCAmelCase		,       _UpperCAmelCase       ):


					'''simple docstring'''





					_a             =							'bit'
					_a             =							['preactivation', 'bottleneck']
					_a             =							['SAME', 'VALID']



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Any=3,  lowerCamelCase							:  Union[str, Any]=64,  lowerCamelCase							:  Optional[Any]=[256, 512, 1024, 2048],  lowerCamelCase							:  int=[3, 4, 6, 3],  lowerCamelCase							:  Tuple="preactivation",  lowerCamelCase							:  str="relu",  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Any=32,  lowerCamelCase							:  Tuple=0.0,  lowerCamelCase							:  int=False,  lowerCamelCase							:  Any=32,  lowerCamelCase							:  List[Any]=1,  lowerCamelCase							:  Optional[int]=None,  lowerCamelCase							:  List[Any]=None,  **lowerCamelCase							:  List[str],  )-> Dict:
								super().__init__(**SCREAMING_SNAKE_CASE_					)
								if layer_type not in self.layer_types:
											raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types					)}'''					)
								if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
														lowerCamelCase__		:		str		     =global_padding.upper()
											else:
														raise ValueError(F'''Padding strategy {global_padding} not supported'''					)
								lowerCamelCase__		:		Optional[int]		     =num_channels
								lowerCamelCase__		:		str		     =embedding_size
								lowerCamelCase__		:		List[Any]		     =hidden_sizes
								lowerCamelCase__		:		Any		     =depths
								lowerCamelCase__		:		Optional[int]		     =layer_type
								lowerCamelCase__		:		List[str]		     =hidden_act
								lowerCamelCase__		:		Any		     =global_padding
								lowerCamelCase__		:		Dict		     =num_groups
								lowerCamelCase__		:		Optional[int]		     =drop_path_rate
								lowerCamelCase__		:		Dict		     =embedding_dynamic_padding
								lowerCamelCase__		:		str		     =output_stride
								lowerCamelCase__		:		str		     =width_factor

								lowerCamelCase__		:		int		     =["""stem"""] + [F'''stage{idx}''' for idx in range(1,  len(SCREAMING_SNAKE_CASE_					) + 1					)]
								lowerCamelCase__		:		List[str]		     =get_aligned_output_features_output_indices(
								    out_features=SCREAMING_SNAKE_CASE_,  out_indices=SCREAMING_SNAKE_CASE_,  stage_names=self.stage_names					)









"""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


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





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


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

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

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

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =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




