



"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest



@require_torchsde
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							(DPMSolverSDEScheduler,)
		_lowerCamelCase      	=							10





		def  UpperCAmelCase__				(       self				,     **_lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      {
								    """num_train_timesteps""": 1_1_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """noise_sampler_seed""": 0,
								}

								config.update(**_lowercase )
								return config





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_lowercase )





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,     [0.0002, 0.002, 0.02] ):
														self.check_over_configs(beta_start=_lowercase				,     beta_end=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[Any]:







								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_lowercase )





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







								'''simple docstring'''

								snake_case_ :			Tuple            =      self.scheduler_classes[0]
								snake_case_ :			Optional[int]            =      self.get_scheduler_config()
								snake_case_ :			Optional[Any]            =      scheduler_class(**_lowercase )

								scheduler.set_timesteps(self.num_inference_steps )

								snake_case_ :			Tuple            =      self.dummy_model()
								snake_case_ :			Dict            =      self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_ :			Union[str, Any]            =      sample.to(_lowercase )

								for i, t in enumerate(scheduler.timesteps ):
														snake_case_ :			List[str]            =      scheduler.scale_model_input(_lowercase				,     _lowercase )

														snake_case_ :			Tuple            =      model(_lowercase				,     _lowercase )

														snake_case_ :			Any            =      scheduler.step(_lowercase				,     _lowercase				,     _lowercase )
														snake_case_ :			Optional[int]            =      output.prev_sample

								snake_case_ :			Optional[int]            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			str            =      torch.mean(torch.abs(_lowercase ) )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2
														assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2
														assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1E-3
								else:
														assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
														assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			str            =      self.scheduler_classes[0]
								snake_case_ :			Optional[Any]            =      self.get_scheduler_config(prediction_type="""v_prediction""" )
								snake_case_ :			Any            =      scheduler_class(**_lowercase )

								scheduler.set_timesteps(self.num_inference_steps )

								snake_case_ :			List[str]            =      self.dummy_model()
								snake_case_ :			List[Any]            =      self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_ :			Any            =      sample.to(_lowercase )

								for i, t in enumerate(scheduler.timesteps ):
														snake_case_ :			List[Any]            =      scheduler.scale_model_input(_lowercase				,     _lowercase )

														snake_case_ :			List[str]            =      model(_lowercase				,     _lowercase )

														snake_case_ :			List[Any]            =      scheduler.step(_lowercase				,     _lowercase				,     _lowercase )
														snake_case_ :			List[str]            =      output.prev_sample

								snake_case_ :			Optional[int]            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			List[str]            =      torch.mean(torch.abs(_lowercase ) )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2
														assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2
														assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1E-3
								else:
														assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2
														assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1E-3





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







								'''simple docstring'''

								snake_case_ :			Dict            =      self.scheduler_classes[0]
								snake_case_ :			Optional[int]            =      self.get_scheduler_config()
								snake_case_ :			Dict            =      scheduler_class(**_lowercase )

								scheduler.set_timesteps(self.num_inference_steps				,     device=_lowercase )

								snake_case_ :			Optional[Any]            =      self.dummy_model()
								snake_case_ :			Union[str, Any]            =      self.dummy_sample_deter.to(_lowercase ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
														snake_case_ :			Dict            =      scheduler.scale_model_input(_lowercase				,     _lowercase )

														snake_case_ :			Optional[int]            =      model(_lowercase				,     _lowercase )

														snake_case_ :			Dict            =      scheduler.step(_lowercase				,     _lowercase				,     _lowercase )
														snake_case_ :			Union[str, Any]            =      output.prev_sample

								snake_case_ :			List[Any]            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			Union[str, Any]            =      torch.mean(torch.abs(_lowercase ) )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2
														assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2
														assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1E-3
								else:
														assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
														assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			str            =      self.scheduler_classes[0]
								snake_case_ :			int            =      self.get_scheduler_config()
								snake_case_ :			List[Any]            =      scheduler_class(**_lowercase				,     use_karras_sigmas=_lowercase )

								scheduler.set_timesteps(self.num_inference_steps				,     device=_lowercase )

								snake_case_ :			int            =      self.dummy_model()
								snake_case_ :			Tuple            =      self.dummy_sample_deter.to(_lowercase ) * scheduler.init_noise_sigma
								snake_case_ :			List[str]            =      sample.to(_lowercase )

								for t in scheduler.timesteps:
														snake_case_ :			int            =      scheduler.scale_model_input(_lowercase				,     _lowercase )

														snake_case_ :			Any            =      model(_lowercase				,     _lowercase )

														snake_case_ :			Union[str, Any]            =      scheduler.step(_lowercase				,     _lowercase				,     _lowercase )
														snake_case_ :			Optional[int]            =      output.prev_sample

								snake_case_ :			Union[str, Any]            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			List[str]            =      torch.mean(torch.abs(_lowercase ) )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
								else:
														assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2









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,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

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



