


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	(  SCREAMING_SNAKE_CASE,			SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      IFImgaImgPipeline
       _UpperCamelCase    		=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
       _UpperCamelCase    		=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       _UpperCamelCase    		=      PipelineTesterMixin.required_optional_params - {"""latents"""}

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self._get_dummy_components()

       def 				UpperCAmelCase				(			self	,						a__	,						a__=0					):






        '''simple docstring'''






        if str(a__					).startswith('''mps'''					):
         snake_case   : List[str]	  =      torch.manual_seed(a__					)
        else:
         snake_case   : List[str]	  =      torch.Generator(device=a__					).manual_seed(a__					)

        snake_case   : Optional[int]	  =      floats_tensor((1, 3, 32, 32)	,						rng=random.Random(a__					)					).to(a__					)

        snake_case   : int	  =      {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }

        return inputs

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)

       @unittest.skipIf(torch_device != '''cuda'''	,						reason='''float16 requires CUDA'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        super().test_save_load_floataa(expected_max_diff=1e-1					)

       @unittest.skipIf(torch_device != '''cuda'''	,						reason='''float16 requires CUDA'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        super().test_floataa_inference(expected_max_diff=1e-1					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        self._test_save_load_local()



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2	,						)










from typing import Callable






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''

 def wrapper(__lowerCamelCase       ):
  snake_case   : str	  =      function_with_docstring.__doc__
  return target_function

 return wrapper






