


'''simple docstring'''




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 			lowerCAmelCase					(					a_   ,					a_   ,					unittest.TestCase				):
	_lowerCamelCase :       Optional[int]         =			IFSuperResolutionPipeline
	_lowerCamelCase :       Optional[int]         =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
	_lowerCamelCase :       str         =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_lowerCamelCase :       List[str]         =			PipelineTesterMixin.required_optional_params - {"latents"}



	def 					lowercase  (       self     ):
		return self._get_superresolution_dummy_components()



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

		lowerCAmelCase       :     Optional[Any]   				=			floats_tensor((1, 3, 32, 32)			,       rng=random.Random(_lowerCamelCase     )     ).to(_lowerCamelCase     )

		lowerCAmelCase       :     int   				=			{
		    '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 					lowercase  (       self     ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )



	def 					lowercase  (       self     ):
		self._test_save_load_optional_components()



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



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



	def 					lowercase  (       self     ):
		self._test_save_load_local()



	def 					lowercase  (       self     ):
		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

_UpperCamelCase						:    Optional[int]		=						TypeVar('T', bound=bool)

class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			value
      lowercase				       =			None
      lowercase				       =			None







    @property
    def      UpperCamelCase_ (     self			):
      return self._value







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						_lowerCamelCase			):
      if not roota:
        return roota

      if not roota:
        return roota

      if roota.value > roota.value:
        lowercase       ,	lowercase				       =			roota, roota

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

      lowercase				       =			RandomizedHeapNode.merge(roota.left				,						_lowerCamelCase			)

      return roota


class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase = ()			):
      lowercase				       =			None

      if data:
        for item in data:
          self.insert(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			RandomizedHeapNode.merge(self._root				,						RandomizedHeapNode(_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.top()

      if self._root is None:
        return None

      lowercase				       =			RandomizedHeapNode.merge(self._root.left				,						self._root.right			)

      return result







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      while self:
        result.append(self.pop()			)

      return result







    def __bool__(     self			):
      return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


