





import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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						( A_			,							A_			,							unittest.TestCase       ):
					__A        = IFSuperResolutionPipeline
					__A        = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
					__A        = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					__A        = PipelineTesterMixin.required_optional_params - {"latents"}




					def 						snake_case_		(    self							)    ->	Optional[int]:
							return self._get_superresolution_dummy_components()




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	Optional[Any]:



							if str(__lowerCamelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCamelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)

							UpperCamelCase__								=						floats_tensor((1, 3, 32, 32)     ,   rng=random.Random(__lowerCamelCase							)							).to(__lowerCamelCase							)

							UpperCamelCase__								=						{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': 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 						snake_case_		(    self							)    ->	str:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							self._test_save_load_optional_components()




					@unittest.skipIf(torch_device != """cuda"""     ,   reason="""float16 requires CUDA"""							)
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							super().test_save_load_floataa(expected_max_diff=1E-1							)




					def 						snake_case_		(    self							)    ->	str:
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)




					def 						snake_case_		(    self							)    ->	Optional[int]:
							self._test_save_load_local()




					def 						snake_case_		(    self							)    ->	List[str]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1E-2     ,   )






"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



