
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__(    snake_case_	):

	"""simple docstring"""

	_A  =		"config.json"







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





			pass


class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	_A  =		"config.json"







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





			pass


class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	_A  =		"config.json"







	@register_to_config
	def __init__(      self				:			int							,			snake_case__				:			str=2							,			snake_case__				:			Optional[int]=5							,			snake_case__				:			Optional[int]=(2, 5)							,			snake_case__				:			Optional[int]="for diffusion"							,			snake_case__				:			Optional[int]=[1, 3]							,			snake_case__				:			Optional[int]=[1, 3]							,			):
			"""simple docstring"""





			pass


class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	_A  =		"config.json"







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





			pass


class        UpperCamelCase__(    unittest.TestCase	):

	"""simple docstring"""

	def        _a					(      self				:			Optional[Any] ):
			"""simple docstring"""





			with self.assertRaises(UpperCAmelCase__ ):
					ConfigMixin.load_config("dummy_path" )







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=SampleObject()
			A				=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
			A				=SampleObject(_name_or_path="lalala" )
			A				=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
			A				=SampleObject(c=6 )
			A				=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.
			A				=SampleObject(1							,			c=6 )
			A				=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				:			List[str] ):
			"""simple docstring"""





			A				=SampleObject()
			A				=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(UpperCAmelCase__ )
					A				=SampleObject.from_config(SampleObject.load_config(UpperCAmelCase__ ) )
					A				=new_obj.config

			# unfreeze configs
			A				=dict(UpperCAmelCase__ )
			A				=dict(UpperCAmelCase__ )

			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				:			Union[str, Any] ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger:
					A				=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				:			List[str] ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger:
					A				=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[Any] ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger:
					A				=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				:			Optional[Any] ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger:
					A				=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				:			Union[str, Any] ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

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

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger_a:
					A				=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				:			Tuple ):
			"""simple docstring"""





			A				=logging.get_logger("diffusers.configuration_utils" )
			# 30 for warning
			logger.setLevel(30 )

			with CaptureLogger(UpperCAmelCase__ ) as cap_logger:
					A				=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				:			Union[str, Any] ):
			"""simple docstring"""





			A				=SampleObject()

			A				={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(UpperCAmelCase__ )

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

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

			config.config._use_default_values.pop()
			A				=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,
)


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

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

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

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''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

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
