


"""simple docstring"""







import logging
import os

from .state import PartialState




class  UpperCamelCase_	( logging.LoggerAdapter):

	"""simple docstring"""





	@staticmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				Tuple   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				PartialState()
						return not main_process_only or (main_process_only and state.is_main_process)




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				Tuple			,			*UpperCAmelCase__			:				List[Any]			,			**UpperCAmelCase__			:				Dict   )       ->	Tuple:
						if PartialState._shared_state == {}:
											raise RuntimeError(
											    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."   )
						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("main_process_only"			,			UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("in_order"			,			UpperCAmelCase__   )

						if self.isEnabledFor(UpperCAmelCase__   ):
											if self._should_log(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.process(UpperCAmelCase__			,			UpperCAmelCase__   )
																self.logger.log(UpperCAmelCase__			,			UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__   )

											elif in_order:
																__SCREAMING_SNAKE_CASE						   =				PartialState()
																for i in range(state.num_processes   ):
																					if i == state.process_index:
																										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.process(UpperCAmelCase__			,			UpperCAmelCase__   )
																										self.logger.log(UpperCAmelCase__			,			UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__   )
																					state.wait_for_everyone()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = None   ):




					'''simple docstring'''


					if log_level is None:
										__SCREAMING_SNAKE_CASE						   =				os.environ.get("ACCELERATE_LOG_LEVEL"  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				logging.getLogger(lowerCAmelCase_   )
					if log_level is not None:
										logger.setLevel(log_level.upper()   )
										logger.root.setLevel(log_level.upper()   )
					return MultiProcessAdapter(lowerCAmelCase_  ,						{}   )




"""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__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				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
