
"""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      :							List[Any]          =					TypeVar("T", bound="Enum")

@unique
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		80
     lowercase		  =		70
     lowercase		  =		60
     lowercase		  =		50
     lowercase		  =		40
     lowercase		  =		30
     lowercase		  =		20
     lowercase		  =		10


@unique
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		24
     lowercase		  =		21
     lowercase		  =		18
     lowercase		  =		15
     lowercase		  =		12
     lowercase		  =		9
     lowercase		  =		6
     lowercase		  =		3
     lowercase		  =		2
     lowercase		  =		1
     lowercase		  =		-1
     lowercase		  =		-2
     lowercase		  =		-3
     lowercase		  =		-6
     lowercase		  =		-9
     lowercase		  =		-12
     lowercase		  =		-15
     lowercase		  =		-18
     lowercase		  =		-21
     lowercase		  =		-24





     @classmethod
     def 		UpperCAmelCase   (      cls ):

      '''simple docstring'''




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







     @classmethod
     def 		UpperCAmelCase   (      cls ):

      '''simple docstring'''




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






def 				A		(							snake_case :float						) ->      str:
 __UpperCamelCase							    =			SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
 for name_prefix, value_prefix in prefixes.items():
  __UpperCamelCase							    =			value / (1_0**value_prefix)
  if numerical_part > 1:
   return f'{numerical_part!s} {name_prefix}'
 return str(snake_case						)






def 				A		(							snake_case :float						) ->      str:
 for prefix in BinaryUnit:
  __UpperCamelCase							    =			value / (2**prefix.value)
  if numerical_part > 1:
   return f'{numerical_part!s} {prefix.name}'
 return str(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





"""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						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=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





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

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





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

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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



