

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			(		__a):
			__a :       List[Any]									=							"""config.json"""
			@register_to_config
			def __init__(		self      ,		_A=2      ,		_A=5      ,		_A=(2, 5)      ,		_A="for diffusion"      ,		_A=[1, 3]      ,		)    ->    List[Any]:






				'''simple docstring'''







				pass






class     _UpperCAmelCase			(		__a):
			__a :       Union[str, Any]									=							"""config.json"""
			@register_to_config
			def __init__(		self      ,		_A=2      ,		_A=5      ,		_A=(2, 5)      ,		_A="for diffusion"      ,		_A=[1, 3]      ,		)    ->    List[Any]:






				'''simple docstring'''







				pass






class     _UpperCAmelCase			(		__a):
			__a :       List[str]									=							"""config.json"""
			@register_to_config
			def __init__(		self      ,		_A=2      ,		_A=5      ,		_A=(2, 5)      ,		_A="for diffusion"      ,		_A=[1, 3]      ,		_A=[1, 3]      ,		)    ->    str:






				'''simple docstring'''







				pass






class     _UpperCAmelCase			(		__a):
			__a :       Union[str, Any]									=							"""config.json"""
			@register_to_config
			def __init__(		self      ,		_A=2      ,		_A=5      ,		_A=(2, 5)      ,		_A="for diffusion"      ,		_A=[1, 5]      ,		_A=[5, 4]      ,		)    ->    List[str]:






				'''simple docstring'''







				pass






class     _UpperCAmelCase			(		unittest.TestCase):
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				with self.assertRaises(_A     ):
					ConfigMixin.load_config("""dummy_path"""     )
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					SampleObject()
				_UpperCAmelCase    :							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
				_UpperCAmelCase    :							List[str]									=					SampleObject(_name_or_path="""lalala"""     )
				_UpperCAmelCase    :							Tuple									=					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    :							Union[str, Any]									=					SampleObject(c=6     )
				_UpperCAmelCase    :							Union[str, 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.
				_UpperCAmelCase    :							List[str]									=					SampleObject(1      ,		c=6     )
				_UpperCAmelCase    :							Tuple									=					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 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					SampleObject()
				_UpperCAmelCase    :							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     )
					_UpperCAmelCase    :							Dict									=					SampleObject.from_config(SampleObject.load_config(_A     )     )
					_UpperCAmelCase    :							List[Any]									=					new_obj.config

				# unfreeze configs
				_UpperCAmelCase    :							Dict									=					dict(_A     )
				_UpperCAmelCase    :							Optional[int]									=					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 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					logging.get_logger("""diffusers.configuration_utils"""     )
				# 30 for warning
				logger.setLevel(30     )

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							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 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					logging.get_logger("""diffusers.configuration_utils"""     )
				# 30 for warning
				logger.setLevel(30     )

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							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 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







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

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							str									=					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 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							Any									=					logging.get_logger("""diffusers.configuration_utils"""     )
				# 30 for warning
				logger.setLevel(30     )

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							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 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







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

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							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:
					_UpperCAmelCase    :							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 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					logging.get_logger("""diffusers.configuration_utils"""     )
				# 30 for warning
				logger.setLevel(30     )

				with CaptureLogger(_A     ) as cap_logger:
					_UpperCAmelCase    :							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 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					SampleObject()

				_UpperCAmelCase    :							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(_A     )

					# now loading it with SampleObject2 should put f into `_use_default_values`
					_UpperCAmelCase    :							List[str]									=					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`
				_UpperCAmelCase    :							int									=					SampleObjecta.from_config(config.config     )
				assert new_config.f == [5, 4]

				config.config._use_default_values.pop()
				_UpperCAmelCase    :							Tuple									=					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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Optional[int]													=  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  ['''T5Tokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[str]													=  ['''T5TokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Optional[Any]													=  [
						    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''T5EncoderModel''',
						    '''T5ForConditionalGeneration''',
						    '''T5Model''',
						    '''T5PreTrainedModel''',
						    '''load_tf_weights_in_t5''',
						    '''T5ForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  int													=  [
						    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFT5EncoderModel''',
						    '''TFT5ForConditionalGeneration''',
						    '''TFT5Model''',
						    '''TFT5PreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''FlaxT5EncoderModel''',
						    '''FlaxT5ForConditionalGeneration''',
						    '''FlaxT5Model''',
						    '''FlaxT5PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__				:  Dict													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





