
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 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :List[str]   					=     'config.json'
	@register_to_config
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any=2	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=5	,							SCREAMING_SNAKE_CASE_  :      str=(2, 5)	,							SCREAMING_SNAKE_CASE_  :      int="for diffusion"	,							SCREAMING_SNAKE_CASE_  :      Dict=[1, 3]	,							)	->					int:
					pass

class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     'config.json'
	@register_to_config
	def __init__(	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      int=2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=(2, 5)	,							SCREAMING_SNAKE_CASE_  :      Dict="for diffusion"	,							SCREAMING_SNAKE_CASE_  :      Tuple=[1, 3]	,							)	->					Optional[Any]:
					pass

class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :int   					=     'config.json'
	@register_to_config
	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Tuple=2	,							SCREAMING_SNAKE_CASE_  :      str=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=(2, 5)	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="for diffusion"	,							SCREAMING_SNAKE_CASE_  :      Any=[1, 3]	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=[1, 3]	,							)	->					Optional[Any]:
					pass

class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :List[str]   					=     'config.json'
	@register_to_config
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=5	,							SCREAMING_SNAKE_CASE_  :      Dict=(2, 5)	,							SCREAMING_SNAKE_CASE_  :      Any="for diffusion"	,							SCREAMING_SNAKE_CASE_  :      Dict=[1, 5]	,							SCREAMING_SNAKE_CASE_  :      int=[5, 4]	,							)	->					Union[str, Any]:
					pass

class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Optional[int]	)	->					Optional[int]:
					with self.assertRaises(SCREAMING_SNAKE_CASE_	):
									ConfigMixin.load_config('''dummy_path'''	)
	def 						_lowercase  (	self  :      List[Any]	)	->					Optional[Any]:
					lowercase_					  = SampleObject()
					lowercase_					  = 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
					lowercase_					  = SampleObject(_name_or_path='''lalala'''	)
					lowercase_					  = 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
					lowercase_					  = SampleObject(c=6	)
					lowercase_					  = 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.
					lowercase_					  = SampleObject(1	,							c=6	)
					lowercase_					  = 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  :      Tuple	)	->					int:
					lowercase_					  = SampleObject()
					lowercase_					  = 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_	)
									lowercase_					  = SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE_	)	)
									lowercase_					  = new_obj.config

					# unfreeze configs
					lowercase_					  = dict(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = 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 						_lowercase  (	self  :      str	)	->					Tuple:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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]	)	->					int:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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  :      List[str]	)	->					Optional[int]:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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  :      List[str]	)	->					Any:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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  :      Optional[Any]	)	->					int:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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:
									lowercase_					  = DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''	,							beta_start=8_8	)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 8_8

					# no warning should be thrown
					assert cap_logger.out == ""
					assert cap_logger_a.out == ""
	def 						_lowercase  (	self  :      Tuple	)	->					int:
					lowercase_					  = logging.get_logger('''diffusers.configuration_utils'''	)
					# 30 for warning
					logger.setLevel(3_0	)

					with CaptureLogger(SCREAMING_SNAKE_CASE_	) as cap_logger:
									lowercase_					  = 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  :      str	)	->					Optional[Any]:
					# 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],

					lowercase_					  = SampleObject()

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

					config.config._use_default_values.pop()
					lowercase_					  = 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_torch_available, is_vision_available


__a        = {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DonutSwinModel',
						    'DonutSwinPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DonutFeatureExtractor']
						__a        = ['DonutImageProcessor']


if TYPE_CHECKING:
						from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
						from .processing_donut import DonutProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_donut_swin import (
												    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DonutSwinModel,
												    DonutSwinPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_donut import DonutFeatureExtractor
												from .image_processing_donut import DonutImageProcessor

else:
						import sys

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




