
from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowercase											=    TypeVar("""T""")


class 			lowercase_		(      Generic[T]       ):


       def __init__(    self						,       __A				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=data
              SCREAMING_SNAKE_CASE_							:				str		=None





       def __str__(    self				)						->    str:
              return F'{self.data}'






class 			lowercase_		(      Generic[T]       ):


       def __init__(    self				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Tuple		=None


       def __iter__(    self				)						->    Iterator[T]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.top
              while node:
                     yield node.data
                     SCREAMING_SNAKE_CASE_							:				Tuple		=node.next


       def __str__(    self				)						->    str:
              return "->".join([str(__A				) for item in self]				)


       def __len__(    self				)						->    int:
              return len(tuple(iter(self				)				)				)


       def 	_snake_case				(    self				)						->    bool:
              return self.top is None


       def 	_snake_case				(    self						,       __A				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=Node(__A				)
              if not self.is_empty():
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.top
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=node


       def 	_snake_case				(    self				)						->    T:

              if self.is_empty():
                     raise IndexError('''pop from empty stack'''				)
              assert isinstance(self.top						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.top
              SCREAMING_SNAKE_CASE_							:				Dict		=self.top.next
              return pop_node.data


       def 	_snake_case				(    self				)						->    T:

              if self.is_empty():
                     raise IndexError('''peek from empty stack'''				)

              assert self.top is not None
              return self.top.data





       def 	_snake_case				(    self				)						->    None:
              SCREAMING_SNAKE_CASE_							:				str		=None


if __name__ == "__main__":
 from doctest import testmod

 testmod()





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

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

         return inputs


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'      ,					reason='float16 requires CUDA')
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

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