
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__				(	__magic_name__      ):
				lowercase            =    'config.json'






				@register_to_config
				def __init__(		self   : Union[str, Any]       ,	a   : Union[str, Any]=2       ,	a   : Dict=5       ,	a   : str=(2, 5)       ,	a   : int="for diffusion"       ,	a   : List[str]=[1, 3]       ,	):






							'''simple docstring'''







							pass







class 	A__				(	__magic_name__      ):
				lowercase            =    'config.json'






				@register_to_config
				def __init__(		self   : Any       ,	a   : List[str]=2       ,	a   : Any=5       ,	a   : Optional[int]=(2, 5)       ,	a   : int="for diffusion"       ,	a   : List[Any]=[1, 3]       ,	):






							'''simple docstring'''







							pass







class 	A__				(	__magic_name__      ):
				lowercase            =    'config.json'






				@register_to_config
				def __init__(		self   : Union[str, Any]       ,	a   : Dict=2       ,	a   : Union[str, Any]=5       ,	a   : str=(2, 5)       ,	a   : Tuple="for diffusion"       ,	a   : Any=[1, 3]       ,	a   : str=[1, 3]       ,	):






							'''simple docstring'''







							pass







class 	A__				(	__magic_name__      ):
				lowercase            =    'config.json'






				@register_to_config
				def __init__(		self   : Optional[int]       ,	a   : Optional[int]=2       ,	a   : Any=5       ,	a   : int=(2, 5)       ,	a   : str="for diffusion"       ,	a   : Dict=[1, 5]       ,	a   : Dict=[5, 4]       ,	):






							'''simple docstring'''







							pass







class 	A__				(	unittest.TestCase      ):






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							with self.assertRaises(a  ):
										ConfigMixin.load_config('dummy_path'  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						SampleObject()
							lowerCAmelCase__ : 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]

							# init ignore private arguments
							lowerCAmelCase__ : Optional[int]   =						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__ : Tuple   =						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__ : Union[str, Any]   =						SampleObject(1       ,	c=6  )
							lowerCAmelCase__ : 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 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						SampleObject()
							lowerCAmelCase__ : Optional[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]

							with tempfile.TemporaryDirectory() as tmpdirname:
										obj.save_config(a  )
										lowerCAmelCase__ : List[str]   =						SampleObject.from_config(SampleObject.load_config(a  )  )
										lowerCAmelCase__ : Tuple   =						new_obj.config

							# unfreeze configs
							lowerCAmelCase__ : int   =						dict(a  )
							lowerCAmelCase__ : Tuple   =						dict(a  )

							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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : 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 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : Optional[int]   =						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 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : Optional[int]   =						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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : Optional[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 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : Union[str, Any]   =						DDPMScheduler.from_pretrained(
										    'hf-internal-testing/tiny-stable-diffusion-torch'       ,	subfolder='scheduler'       ,	prediction_type='sample'       ,	beta_end=8       ,	)

							with CaptureLogger(a  ) as cap_logger_a:
										lowerCAmelCase__ : List[Any]   =						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 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						logging.get_logger('diffusers.configuration_utils'  )
							# 30 for warning
							logger.setLevel(30  )

							with CaptureLogger(a  ) as cap_logger:
										lowerCAmelCase__ : int   =						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 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						SampleObject()

							lowerCAmelCase__ : Union[str, Any]   =						{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(a  )

										# now loading it with SampleObject2 should put f into `_use_default_values`
										lowerCAmelCase__ : Optional[int]   =						SampleObjecta.from_config(a  )

										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[int]   =						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]

from typing import TYPE_CHECKING

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


lowerCamelCase__        =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)