




'''simple docstring'''




from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
UpperCAmelCase_       				=      TypeVar('T', bound='Enum')

@unique
class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[Any]				     =						80
  lowerCAmelCase_				:					Tuple				     =						70
  lowerCAmelCase_				:					List[str]				     =						60
  lowerCAmelCase_				:					Union[str, Any]				     =						50
  lowerCAmelCase_				:					Any				     =						40
  lowerCAmelCase_				:					Optional[int]				     =						30
  lowerCAmelCase_				:					Optional[int]				     =						20
  lowerCAmelCase_				:					Tuple				     =						10

@unique
class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					int				     =						24
  lowerCAmelCase_				:					Dict				     =						21
  lowerCAmelCase_				:					Any				     =						18
  lowerCAmelCase_				:					int				     =						15
  lowerCAmelCase_				:					Any				     =						12
  lowerCAmelCase_				:					Optional[Any]				     =						9
  lowerCAmelCase_				:					Optional[Any]				     =						6
  lowerCAmelCase_				:					List[Any]				     =						3
  lowerCAmelCase_				:					Dict				     =						2
  lowerCAmelCase_				:					Optional[Any]				     =						1
  lowerCAmelCase_				:					str				     =						-1
  lowerCAmelCase_				:					Union[str, Any]				     =						-2
  lowerCAmelCase_				:					Optional[Any]				     =						-3
  lowerCAmelCase_				:					List[str]				     =						-6
  lowerCAmelCase_				:					Optional[int]				     =						-9
  lowerCAmelCase_				:					int				     =						-12
  lowerCAmelCase_				:					List[Any]				     =						-15
  lowerCAmelCase_				:					Tuple				     =						-18
  lowerCAmelCase_				:					Tuple				     =						-21
  lowerCAmelCase_				:					Union[str, Any]				     =						-24


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	type[T] ):






   """simple docstring"""






   return {unit.name: unit.value for unit in cls if unit.value > 0}



  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	type[T] ):






   """simple docstring"""






   return {unit.name: unit.value for unit in cls if unit.value < 0}

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ):
 '''simple docstring'''





 UpperCAmelCase__										=    SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
 for name_prefix, value_prefix in prefixes.items():
  UpperCAmelCase__										=    value / (10**value_prefix)
  if numerical_part > 1:
   return F'''{numerical_part!s} {name_prefix}'''
 return str(SCREAMING_SNAKE_CASE__       )

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ):
 '''simple docstring'''





 for prefix in BinaryUnit:
  UpperCAmelCase__										=    value / (2**prefix.value)
  if numerical_part > 1:
   return F'''{numerical_part!s} {prefix.name}'''
 return str(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''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 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   if str(_UpperCAmelCase ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )

   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

   UpperCAmelCase__										=    {
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }

   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






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


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






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