




"""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    (     snake_case_  ,				snake_case_  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =IFImgaImgPipeline
				_lowerCAmelCase		     =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
				_lowerCAmelCase		     =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowerCAmelCase		     =PipelineTesterMixin.required_optional_params - {"latents"}

				def  _lowerCamelCase   (				self				:       Dict ):
										return self._get_dummy_components()

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       List[Any]=0 ):
										if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
																snake_case__    :   List[str]							     =   torch.manual_seed(SCREAMING_SNAKE_CASE_ )
										else:
																snake_case__    :   Union[str, Any]							     =   torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   List[str]							     =   floats_tensor((1, 3, 3_2, 3_2) ,rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Optional[int]							     =   {
										    '''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				:       str ):
										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				:       Optional[Any] ):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )

				@unittest.skipIf(torch_device != '''cuda''' ,reason='''float16 requires CUDA''' )
				def  _lowerCamelCase   (				self				:       Any ):
										# 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				:       List[Any] ):
										super().test_floataa_inference(expected_max_diff=1E-1 )

				def  _lowerCamelCase   (				self				:       List[str] ):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )

				def  _lowerCamelCase   (				self				:       List[str] ):
										self._test_save_load_local()





				def  _lowerCamelCase   (				self				:       List[Any] ):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2 ,)








"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return int(lowerCamelCase						) if x - int(lowerCamelCase						) <= 0 else int(lowerCamelCase						) + 1


if __name__ == "__main__":
							import doctest

							doctest.testmod()


