code: 
  
  
  import random
  import unittest
  
  import torch
  
  from diffusers import IFImgaImgPipeline
  from diffusers.utils import floats_tensor
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import skip_mps, torch_device
  
  from ..pipeline_params import (
      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
  )
  from ..test_pipelines_common import PipelineTesterMixin
  from . import IFPipelineTesterMixin
  @skip_mps
  class _lowerCamelCase (		snake_case__	,  snake_case__	,  unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       IFImgaImgPipeline
  		A_			:    List[str]   					=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  		A_			:    Optional[int]   					=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  		A_			:    Union[str, Any]   					=       PipelineTesterMixin.required_optional_params - {"""latents"""}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[int]:
  			return self._get_dummy_components()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		int						,  _A	:		Tuple=0    )						->				str:
  			if str(__snake_case    ).startswith('mps'    ):
  				__magic_name__ :					List[Any]  			=  torch.manual_seed(__snake_case    )
  			else:
  				__magic_name__ :					Optional[Any]  			=  torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
  
  			__magic_name__ :					Dict  			=  floats_tensor((1, 3, 32, 32)						,  rng=random.Random(__snake_case    )    ).to(__snake_case    )
  
  			__magic_name__ :					Union[str, Any]  			=  {
  			    'prompt': 'A painting of a squirrel eating a burger',
  			    'image': image,
  			    'generator': generator,
  			    'num_inference_steps': 2,
  			    'output_type': 'numpy',
  			}
  
  			return inputs
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Tuple:
  			self._test_save_load_optional_components()
  
  
  
  
  
  
  
  		@unittest.skipIf(
  		    torch_device != 'cuda' or not is_xformers_available()						,  reason='XFormers attention is only available with CUDA and `xformers` installed'						,  )
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )
  
  
  
  
  
  
  
  		@unittest.skipIf(torch_device != 'cuda'						,  reason='float16 requires CUDA'    )
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
  			super().test_save_load_floataa(expected_max_diff=1E-1    )
  
  
  
  
  
  
  
  		@unittest.skipIf(torch_device != 'cuda'						,  reason='float16 requires CUDA'    )
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			super().test_floataa_inference(expected_max_diff=1E-1    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[int]:
  			self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  			self._test_save_load_local()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Union[str, Any]:
  			self._test_inference_batch_single_identical(
  			    expected_max_diff=1E-2						,  )
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)