






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     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Union[str, Any]     	=  (DPMSolverSDEScheduler,)
	snake_case__							:     str     	=  1_0





	def 						_A (		self				:					Any ,  **__lowerCamelCase				:					Any	):
		UpperCamelCase     :Any          =	{
		    """num_train_timesteps""": 1_100,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """noise_sampler_seed""": 0,
		}

		config.update(**__lowerCamelCase	)
		return config





	def 						_A (		self				:					str	):
		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=__lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,  [0.0002, 0.002, 0.02]	):
			self.check_over_configs(beta_start=__lowerCamelCase ,  beta_end=__lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=__lowerCamelCase	)





	def 						_A (		self				:					str	):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=__lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :str          =	self.scheduler_classes[0]
		UpperCamelCase     :Optional[Any]          =	self.get_scheduler_config()
		UpperCamelCase     :List[str]          =	scheduler_class(**__lowerCamelCase	)

		scheduler.set_timesteps(self.num_inference_steps	)

		UpperCamelCase     :List[str]          =	self.dummy_model()
		UpperCamelCase     :List[Any]          =	self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCamelCase     :Optional[int]          =	sample.to(__lowerCamelCase	)

		for i, t in enumerate(scheduler.timesteps	):
			UpperCamelCase     :Optional[Any]          =	scheduler.scale_model_input(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :List[str]          =	model(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :str          =	scheduler.step(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :List[str]          =	output.prev_sample

		UpperCamelCase     :Union[str, Any]          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Union[str, Any]          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		if torch_device in ["mps"]:
			assert abs(result_sum.item() - 167.47821044921875	) < 1E-2
			assert abs(result_mean.item() - 0.2178705964565277	) < 1E-3
		elif torch_device in ["cuda"]:
			assert abs(result_sum.item() - 171.59352111816406	) < 1E-2
			assert abs(result_mean.item() - 0.22342906892299652	) < 1E-3
		else:
			assert abs(result_sum.item() - 162.52383422851562	) < 1E-2
			assert abs(result_mean.item() - 0.211619570851326	) < 1E-3





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :List[Any]          =	self.scheduler_classes[0]
		UpperCamelCase     :int          =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
		UpperCamelCase     :Any          =	scheduler_class(**__lowerCamelCase	)

		scheduler.set_timesteps(self.num_inference_steps	)

		UpperCamelCase     :Tuple          =	self.dummy_model()
		UpperCamelCase     :Optional[int]          =	self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCamelCase     :Any          =	sample.to(__lowerCamelCase	)

		for i, t in enumerate(scheduler.timesteps	):
			UpperCamelCase     :Union[str, Any]          =	scheduler.scale_model_input(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :Dict          =	model(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :Optional[int]          =	scheduler.step(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :Dict          =	output.prev_sample

		UpperCamelCase     :List[str]          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Any          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		if torch_device in ["mps"]:
			assert abs(result_sum.item() - 124.77149200439453	) < 1E-2
			assert abs(result_mean.item() - 0.16226289014816284	) < 1E-3
		elif torch_device in ["cuda"]:
			assert abs(result_sum.item() - 128.1663360595703	) < 1E-2
			assert abs(result_mean.item() - 0.16688326001167297	) < 1E-3
		else:
			assert abs(result_sum.item() - 119.8487548828125	) < 1E-2
			assert abs(result_mean.item() - 0.1560530662536621	) < 1E-3





	def 						_A (		self				:					int	):
		UpperCamelCase     :Dict          =	self.scheduler_classes[0]
		UpperCamelCase     :str          =	self.get_scheduler_config()
		UpperCamelCase     :Union[str, Any]          =	scheduler_class(**__lowerCamelCase	)

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

		UpperCamelCase     :Dict          =	self.dummy_model()
		UpperCamelCase     :str          =	self.dummy_sample_deter.to(__lowerCamelCase	) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCamelCase     :Any          =	scheduler.scale_model_input(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :Any          =	model(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :str          =	scheduler.step(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :List[Any]          =	output.prev_sample

		UpperCamelCase     :Tuple          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :List[str]          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		if torch_device in ["mps"]:
			assert abs(result_sum.item() - 167.46957397460938	) < 1E-2
			assert abs(result_mean.item() - 0.21805934607982635	) < 1E-3
		elif torch_device in ["cuda"]:
			assert abs(result_sum.item() - 171.59353637695312	) < 1E-2
			assert abs(result_mean.item() - 0.22342908382415771	) < 1E-3
		else:
			assert abs(result_sum.item() - 162.52383422851562	) < 1E-2
			assert abs(result_mean.item() - 0.211619570851326	) < 1E-3





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :int          =	self.scheduler_classes[0]
		UpperCamelCase     :Dict          =	self.get_scheduler_config()
		UpperCamelCase     :List[str]          =	scheduler_class(**__lowerCamelCase ,  use_karras_sigmas=__lowerCamelCase	)

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

		UpperCamelCase     :int          =	self.dummy_model()
		UpperCamelCase     :Optional[int]          =	self.dummy_sample_deter.to(__lowerCamelCase	) * scheduler.init_noise_sigma
		UpperCamelCase     :List[Any]          =	sample.to(__lowerCamelCase	)

		for t in scheduler.timesteps:
			UpperCamelCase     :List[Any]          =	scheduler.scale_model_input(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :Optional[int]          =	model(__lowerCamelCase ,  __lowerCamelCase	)

			UpperCamelCase     :Tuple          =	scheduler.step(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :Any          =	output.prev_sample

		UpperCamelCase     :Optional[int]          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Any          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		if torch_device in ["mps"]:
			assert abs(result_sum.item() - 176.66974135742188	) < 1E-2
			assert abs(result_mean.item() - 0.23003872730981811	) < 1E-2
		elif torch_device in ["cuda"]:
			assert abs(result_sum.item() - 177.63653564453125	) < 1E-2
			assert abs(result_mean.item() - 0.23003872730981811	) < 1E-2
		else:
			assert abs(result_sum.item() - 170.3135223388672	) < 1E-2
			assert abs(result_mean.item() - 0.23003872730981811	) < 1E-2








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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