code: 
  
  
  
  
  
  import tempfile
  import unittest
  
  from diffusers import (
      DDIMScheduler,
      DDPMScheduler,
      DPMSolverMultistepScheduler,
      EulerAncestralDiscreteScheduler,
      EulerDiscreteScheduler,
      PNDMScheduler,
      logging,
  )
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers.utils.testing_utils import CaptureLogger
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
       _a			:	str							=				'config.json'
  
  
  
       @register_to_config
       def __init__(		self    :    Dict					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    Tuple=5					,			lowerCamelCase    :    Union[str, Any]=(2, 5)					,			lowerCamelCase    :    int="for diffusion"					,			lowerCamelCase    :    str=[1, 3]					,			):
         pass
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
       _a			:	Union[str, Any]							=				'config.json'
  
  
  
       @register_to_config
       def __init__(		self    :    Any					,			lowerCamelCase    :    Dict=2					,			lowerCamelCase    :    Optional[int]=5					,			lowerCamelCase    :    Optional[int]=(2, 5)					,			lowerCamelCase    :    Dict="for diffusion"					,			lowerCamelCase    :    str=[1, 3]					,			):
         pass
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
       _a			:	Optional[Any]							=				'config.json'
  
  
  
       @register_to_config
       def __init__(		self    :    int					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    Union[str, Any]=5					,			lowerCamelCase    :    Optional[Any]=(2, 5)					,			lowerCamelCase    :    Union[str, Any]="for diffusion"					,			lowerCamelCase    :    List[Any]=[1, 3]					,			lowerCamelCase    :    List[Any]=[1, 3]					,			):
         pass
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
       _a			:	Tuple							=				'config.json'
  
  
  
       @register_to_config
       def __init__(		self    :    Optional[int]					,			lowerCamelCase    :    Optional[int]=2					,			lowerCamelCase    :    Optional[int]=5					,			lowerCamelCase    :    str=(2, 5)					,			lowerCamelCase    :    str="for diffusion"					,			lowerCamelCase    :    Union[str, Any]=[1, 5]					,			lowerCamelCase    :    Dict=[5, 4]					,			):
         pass
  
  
  
  
  
  
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
       def      __a    (		self    :    Tuple	):
         with self.assertRaises(lowerCamelCase	):
           ConfigMixin.load_config('dummy_path'	)
  
  
  
       def      __a    (		self    :    Any	):
         lowerCamelCase_  :    List[Any]   	=			SampleObject()
         lowerCamelCase_  :    Any   	=			obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]
  
         # init ignore private arguments
         lowerCamelCase_  :    Any   	=			SampleObject(_name_or_path='lalala'	)
         lowerCamelCase_  :    int   	=			obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]
  
         # can override default
         lowerCamelCase_  :    Union[str, Any]   	=			SampleObject(c=6	)
         lowerCamelCase_  :    List[Any]   	=			obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]
  
         # can use positional arguments.
         lowerCamelCase_  :    Tuple   	=			SampleObject(1					,			c=6	)
         lowerCamelCase_  :    Union[str, Any]   	=			obj.config
         assert config["a"] == 1
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]
  
  
  
       def      __a    (		self    :    List[str]	):
         lowerCamelCase_  :    int   	=			SampleObject()
         lowerCamelCase_  :    str   	=			obj.config
  
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]
  
         with tempfile.TemporaryDirectory() as tmpdirname:
           obj.save_config(lowerCamelCase	)
           lowerCamelCase_  :    str   	=			SampleObject.from_config(SampleObject.load_config(lowerCamelCase	)	)
           lowerCamelCase_  :    Optional[Any]   	=			new_obj.config
  
         # unfreeze configs
         lowerCamelCase_  :    int   	=			dict(lowerCamelCase	)
         lowerCamelCase_  :    str   	=			dict(lowerCamelCase	)
  
         assert config.pop('c'	) == (2, 5)  # instantiated as tuple
         assert new_config.pop('c'	) == [2, 5]  # saved & loaded as list because of json
         config.pop('_use_default_values'	)
         assert config == new_config
  
  
  
       def      __a    (		self    :    Optional[int]	):
         lowerCamelCase_  :    List[str]   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    List[str]   	=			DDIMScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'	)
  
         assert ddim.__class__ == DDIMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""
  
  
  
       def      __a    (		self    :    List[str]	):
         lowerCamelCase_  :    Optional[Any]   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    List[str]   	=			EulerDiscreteScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'	)
  
         assert euler.__class__ == EulerDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""
  
  
  
       def      __a    (		self    :    int	):
         lowerCamelCase_  :    str   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    List[Any]   	=			EulerAncestralDiscreteScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'	)
  
         assert euler.__class__ == EulerAncestralDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""
  
  
  
       def      __a    (		self    :    List[str]	):
         lowerCamelCase_  :    Optional[int]   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    List[Any]   	=			PNDMScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'	)
  
         assert pndm.__class__ == PNDMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""
  
  
  
       def      __a    (		self    :    List[str]	):
         lowerCamelCase_  :    Tuple   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    Optional[Any]   	=			DDPMScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'					,			prediction_type='sample'					,			beta_end=8					,			)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger_a:
           lowerCamelCase_  :    Tuple   	=			DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'					,			beta_start=88	)
  
         assert ddpm.__class__ == DDPMScheduler
         assert ddpm.config.prediction_type == "sample"
         assert ddpm.config.beta_end == 8
         assert ddpm_a.config.beta_start == 88
  
         # no warning should be thrown
         assert cap_logger.out == ""
         assert cap_logger_a.out == ""
  
  
  
       def      __a    (		self    :    int	):
         lowerCamelCase_  :    Optional[Any]   	=			logging.get_logger('diffusers.configuration_utils'	)
         # 30 for warning
         logger.setLevel(30	)
  
         with CaptureLogger(lowerCamelCase	) as cap_logger:
           lowerCamelCase_  :    Optional[Any]   	=			DPMSolverMultistepScheduler.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-torch'					,			subfolder='scheduler'	)
  
         assert dpm.__class__ == DPMSolverMultistepScheduler
         # no warning should be thrown
         assert cap_logger.out == ""
  
  
  
  
  
       def      __a    (		self    :    Dict	):
         # let's first save a config that should be in the form
         #    a=2,
         #    b=5,
         #    c=(2, 5),
         #    d="for diffusion",
         #    e=[1, 3],
  
         lowerCamelCase_  :    Optional[Any]   	=			SampleObject()
  
         lowerCamelCase_  :    str   	=			{k: v for k, v in config.config.items() if not k.startswith('_'	)}
  
         # make sure that default config has all keys in `_use_default_values`
         assert set(config_dict.keys()	) == set(config.config._use_default_values	)
  
         with tempfile.TemporaryDirectory() as tmpdirname:
           config.save_config(lowerCamelCase	)
  
           # now loading it with SampleObject2 should put f into `_use_default_values`
           lowerCamelCase_  :    Optional[Any]   	=			SampleObjecta.from_config(lowerCamelCase	)
  
           assert "f" in config._use_default_values
           assert config.f == [1, 3]
  
         # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
         # **BECAUSE** it is part of `config._use_default_values`
         lowerCamelCase_  :    Union[str, Any]   	=			SampleObjecta.from_config(config.config	)
         assert new_config.f == [5, 4]
  
         config.config._use_default_values.pop()
         lowerCamelCase_  :    Optional[Any]   	=			SampleObjecta.from_config(config.config	)
         assert new_config_a.f == [1, 3]
  
         # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
         assert new_config_a.e == [1, 3]
  
style_context: 
  
  
  
  
  
  import os
  import unittest
  
  import pytest
  from parameterized import parameterized
  
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  
  
  # skipping in unittest tests
  
  _lowercase		:     Optional[Any] 					=[(1,)]
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow:
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow and torch_device == "cuda":
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					@slow
  					@require_torch_gpu
  					def      __a    (		self    :    Tuple	):
  							check_slow_torch_cuda()
  
  
  
  					@require_torch_gpu
  					@slow
  					def      __a    (		self    :    Optional[int]	):
  							check_slow_torch_cuda()
  
  
  
  
  
  					@parameterized.expand(lowerCamelCase	)
  					@slow
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
  							check_slow()
  
  
  @slow
  @require_torch_gpu
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @require_torch_gpu
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @slow
  @pytest.mark.parametrize('param'       ,[1]	)
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  
  
  
  
  
  @pytest.mark.parametrize('param'       ,[1]	)
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  