code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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 						lowercase     (   UpperCAmelCase_				,			UpperCAmelCase_				,			unittest.TestCase):
        __lowercase 		=  IFImgaImgPipeline
        __lowercase 		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
        __lowercase 		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
        __lowercase 		=  PipelineTesterMixin.required_optional_params - {'''latents'''}
  
  
  
  
  
  
        def    A__(self					)       ->   Union[str, Any]:
           return self._get_dummy_components()
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__=0					)       ->   Union[str, Any]:
           if str(a__					).startswith('''mps'''					):
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= torch.manual_seed(a__					)
           else:
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.Generator(device=a__					).manual_seed(a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= floats_tensor((1, 3, 3_2, 3_2) ,  rng=random.Random(a__					)					).to(a__					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''output_type''': '''numpy''',
           }
  
           return inputs
  
  
  
  
  
  
        def    A__(self					)       ->   Union[str, Any]:
           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    A__(self					)       ->   Tuple:
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)
  
  
  
  
  
  
        @unittest.skipIf(torch_device != '''cuda''' ,  reason='''float16 requires CUDA'''					)
        def    A__(self					)       ->   List[str]:
           # 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    A__(self					)       ->   Union[str, Any]:
           super().test_floataa_inference(expected_max_diff=1E-1					)
  
  
  
  
  
  
        def    A__(self					)       ->   Optional[Any]:
           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)
  
  
  
  
  
  
        def    A__(self					)       ->   int:
           self._test_save_load_local()
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           self._test_inference_batch_single_identical(
               expected_max_diff=1E-2 ,  )
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  lowercase__			=	{
      '''+''': lambda x, y: x + y,
      '''-''': lambda x, y: x - y,
      '''*''': lambda x, y: x * y,
      '''/''': lambda x, y: x / y,
  }
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return c.isdigit()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
  
     # iterate over the string in reverse order
     for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(snake_case   ):
           stack.append(int(snake_case   )   )
  
        else:
           # pop values from stack can calculate the result
           # push the result onto the stack again
           SCREAMING_SNAKE_CASE__			:				Dict       						= stack.pop()
           SCREAMING_SNAKE_CASE__			:				Any       						= stack.pop()
           stack.append(calc[c](snake_case	,     snake_case   )   )
  
     return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
         lowercase__			=	'''+ 9 * 2 6'''
         print(evaluate(test_expression))
  
         lowercase__			=	'''/ * 10 2 + 4 1 '''
         print(evaluate(test_expression))
  
  