



"""simple docstring"""







from __future__ import annotations

from typing import Any





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):  # noqa: A002
			_lowercase				: Optional[Any]     =   item
			_lowercase				: List[str]     =   next







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self   ):
			_lowercase				: Node | None     =   None
			_lowercase				: Union[str, Any]     =   0






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: int     =   Node(UpperCAmelCase_				,self.head   )
			self.size += 1






	def    lowerCamelCase__					(     self   ):
			# Switched 'self.is_empty()' to 'self.head is None'
			# because mypy was considering the possibility that 'self.head'
			# can be None in below else part and giving error
			if self.head is None:
					return None
			else:
					_lowercase				: Any     =   self.head.item
					_lowercase				: Tuple     =   self.head.next
					self.size -= 1
					return item






	def    lowerCamelCase__					(     self   ):
			return self.head is None






	def __str__(     self   ):
			if self.is_empty():
					return ""
			else:
					_lowercase				: Tuple     =   self.head
					_lowercase				: Union[str, Any]     =   """"""
					_lowercase				: list[str]     =   []
					while iterate:
							item_list.append(str(iterate.item   )   )
							_lowercase				: str     =   iterate.next

					_lowercase				: Tuple     =   """ --> """.join(UpperCAmelCase_   )

					return item_str






	def __len__(     self   ):
			return self.size




"""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 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             '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	):




         '''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 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



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



