



"""simple docstring"""







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 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """config.json"""



			@register_to_config
			def __init__( self       :							Optional[int]      ,  _lowercase       :							Optional[Any]=2      ,  _lowercase       :							Dict=5      ,  _lowercase       :							str=(2, 5)      ,  _lowercase       :							Optional[int]="for diffusion"      ,  _lowercase       :							Optional[int]=[1, 3]      ,  )       ->				Tuple:
									pass



class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """config.json"""



			@register_to_config
			def __init__( self       :							int      ,  _lowercase       :							List[Any]=2      ,  _lowercase       :							Tuple=5      ,  _lowercase       :							Dict=(2, 5)      ,  _lowercase       :							Any="for diffusion"      ,  _lowercase       :							Tuple=[1, 3]      ,  )       ->				Dict:
									pass



class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """config.json"""



			@register_to_config
			def __init__( self       :							Dict      ,  _lowercase       :							str=2      ,  _lowercase       :							List[str]=5      ,  _lowercase       :							Tuple=(2, 5)      ,  _lowercase       :							Tuple="for diffusion"      ,  _lowercase       :							str=[1, 3]      ,  _lowercase       :							str=[1, 3]      ,  )       ->				Optional[Any]:
									pass



class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """config.json"""



			@register_to_config
			def __init__( self       :							Optional[int]      ,  _lowercase       :							Any=2      ,  _lowercase       :							Dict=5      ,  _lowercase       :							Dict=(2, 5)      ,  _lowercase       :							List[str]="for diffusion"      ,  _lowercase       :							Tuple=[1, 5]      ,  _lowercase       :							Tuple=[5, 4]      ,  )       ->				Dict:
									pass



class 			_a     (   unittest.TestCase):



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Dict:
									with self.assertRaises(_lowercase		):
															ConfigMixin.load_config("dummy_path"		)



			def 							__lowercase				( self       :							int		)       ->				List[str]:
									snake_case      : Union[str, Any]							=   SampleObject()
									snake_case      : Union[str, 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
									snake_case      : List[Any]							=   SampleObject(_name_or_path="lalala"		)
									snake_case      : List[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]

									# can override default
									snake_case      : Any							=   SampleObject(c=6		)
									snake_case      : Optional[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.
									snake_case      : Union[str, Any]							=   SampleObject(1      ,  c=6		)
									snake_case      : Dict							=   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 							__lowercase				( self       :							Optional[int]		)       ->				Union[str, Any]:
									snake_case      : Any							=   SampleObject()
									snake_case      : 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]

									with tempfile.TemporaryDirectory() as tmpdirname:
															obj.save_config(_lowercase		)
															snake_case      : int							=   SampleObject.from_config(SampleObject.load_config(_lowercase		)		)
															snake_case      : Any							=   new_obj.config

									# unfreeze configs
									snake_case      : Any							=   dict(_lowercase		)
									snake_case      : Union[str, Any]							=   dict(_lowercase		)

									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 							__lowercase				( self       :							List[str]		)       ->				Optional[int]:
									snake_case      : List[str]							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : Optional[Any]							=   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 							__lowercase				( self       :							List[str]		)       ->				Tuple:
									snake_case      : List[Any]							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : Optional[Any]							=   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 							__lowercase				( self       :							Optional[int]		)       ->				List[str]:
									snake_case      : Optional[Any]							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : 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 							__lowercase				( self       :							Tuple		)       ->				Union[str, Any]:
									snake_case      : Union[str, Any]							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : List[str]							=   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 							__lowercase				( self       :							Any		)       ->				str:
									snake_case      : Union[str, Any]							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : Tuple							=   DDPMScheduler.from_pretrained(
															    "hf-internal-testing/tiny-stable-diffusion-torch"      ,  subfolder="scheduler"      ,  prediction_type="sample"      ,  beta_end=8      ,  )

									with CaptureLogger(_lowercase		) as cap_logger_a:
															snake_case      : str							=   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 							__lowercase				( self       :							List[Any]		)       ->				int:
									snake_case      : Dict							=   logging.get_logger("diffusers.configuration_utils"		)
									# 30 for warning
									logger.setLevel(30		)

									with CaptureLogger(_lowercase		) as cap_logger:
															snake_case      : 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 							__lowercase				( self       :							Any		)       ->				List[str]:
									# 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],

									snake_case      : str							=   SampleObject()

									snake_case      : List[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(_lowercase		)

															# now loading it with SampleObject2 should put f into `_use_default_values`
															snake_case      : List[str]							=   SampleObjecta.from_config(_lowercase		)

															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`
									snake_case      : List[str]							=   SampleObjecta.from_config(config.config		)
									assert new_config.f == [5, 4]

									config.config._use_default_values.pop()
									snake_case      : str							=   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]








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A    =	{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTJForCausalLM',
			    'GPTJForQuestionAnswering',
			    'GPTJForSequenceClassification',
			    'GPTJModel',
			    'GPTJPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TFGPTJForCausalLM',
			    'TFGPTJForQuestionAnswering',
			    'TFGPTJForSequenceClassification',
			    'TFGPTJModel',
			    'TFGPTJPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'FlaxGPTJForCausalLM',
			    'FlaxGPTJModel',
			    'FlaxGPTJPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



