
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 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'config.json'



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=(2, 5)  ,						SCREAMING_SNAKE_CASE_="for diffusion"  ,						SCREAMING_SNAKE_CASE_=[1, 3]  ,						)-> str:





													'''simple docstring'''






													pass






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'config.json'



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=(2, 5)  ,						SCREAMING_SNAKE_CASE_="for diffusion"  ,						SCREAMING_SNAKE_CASE_=[1, 3]  ,						)-> Optional[int]:





													'''simple docstring'''






													pass






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'config.json'



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=(2, 5)  ,						SCREAMING_SNAKE_CASE_="for diffusion"  ,						SCREAMING_SNAKE_CASE_=[1, 3]  ,						SCREAMING_SNAKE_CASE_=[1, 3]  ,						)-> str:





													'''simple docstring'''






													pass






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'config.json'



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=(2, 5)  ,						SCREAMING_SNAKE_CASE_="for diffusion"  ,						SCREAMING_SNAKE_CASE_=[1, 5]  ,						SCREAMING_SNAKE_CASE_=[5, 4]  ,						)-> str:





													'''simple docstring'''






													pass






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      SampleObject()
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      SampleObject(_name_or_path='''lalala'''      )
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      SampleObject(c=6      )
													__UpperCamelCase  	=      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.
													__UpperCamelCase  	=      SampleObject(1  ,						c=6      )
													__UpperCamelCase  	=      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      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      SampleObject()
													__UpperCamelCase  	=      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(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE_      )      )
																			__UpperCamelCase  	=      new_obj.config

													# unfreeze configs
													__UpperCamelCase  	=      dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      dict(SCREAMING_SNAKE_CASE_      )

													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      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      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      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      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      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      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      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      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      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      DDPMScheduler.from_pretrained(
																			    '''hf-internal-testing/tiny-stable-diffusion-torch'''  ,						subfolder='''scheduler'''  ,						prediction_type='''sample'''  ,						beta_end=8  ,						)

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger_a:
																			__UpperCamelCase  	=      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      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''diffusers.configuration_utils'''      )
													# 30 for warning
													logger.setLevel(30      )

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cap_logger:
																			__UpperCamelCase  	=      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      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      SampleObject()

													__UpperCamelCase  	=      {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(SCREAMING_SNAKE_CASE_      )

																			# now loading it with SampleObject2 should put f into `_use_default_values`
																			__UpperCamelCase  	=      SampleObjecta.from_config(SCREAMING_SNAKE_CASE_      )

																			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`
													__UpperCamelCase  	=      SampleObjecta.from_config(config.config      )
													assert new_config.f == [5, 4]

													config.config._use_default_values.pop()
													__UpperCamelCase  	=      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_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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