



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			(EulerAncestralDiscreteScheduler,)
						_lowerCAmelCase             =			10


						def 	_lowerCamelCase      (			self    :	List[str]   ,							**_a    :	int      ):
													a__:							List[Any]									={
													    'num_train_timesteps': 1_1_0_0,
													    'beta_start': 0.0_0_0_1,
													    'beta_end': 0.0_2,
													    'beta_schedule': 'linear',
													}

													config.update(**_A      )
													return config


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																				self.check_over_configs(num_train_timesteps=_A      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																				self.check_over_configs(beta_start=_A   ,							beta_end=_A      )


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=_A      )


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


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Optional[int]									=self.scheduler_classes[0]
													a__:							List[str]									=self.get_scheduler_config()
													a__:							List[Any]									=scheduler_class(**_A      )

													scheduler.set_timesteps(self.num_inference_steps      )

													a__:							List[Any]									=torch.manual_seed(0      )

													a__:							Dict									=self.dummy_model()
													a__:							List[str]									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													a__:							Any									=sample.to(_A      )

													for i, t in enumerate(scheduler.timesteps      ):
																				a__:							Dict									=scheduler.scale_model_input(_A   ,							_A      )

																				a__:							Tuple									=model(_A   ,							_A      )

																				a__:							Dict									=scheduler.step(_A   ,							_A   ,							_A   ,							generator=_A      )
																				a__:							Optional[Any]									=output.prev_sample

													a__:							int									=torch.sum(torch.abs(_A      )      )
													a__:							Tuple									=torch.mean(torch.abs(_A      )      )

													assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
													assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Optional[int]									=self.scheduler_classes[0]
													a__:							Optional[int]									=self.get_scheduler_config(prediction_type="v_prediction"      )
													a__:							List[str]									=scheduler_class(**_A      )

													scheduler.set_timesteps(self.num_inference_steps      )

													a__:							int									=torch.manual_seed(0      )

													a__:							Dict									=self.dummy_model()
													a__:							Optional[Any]									=self.dummy_sample_deter * scheduler.init_noise_sigma
													a__:							int									=sample.to(_A      )

													for i, t in enumerate(scheduler.timesteps      ):
																				a__:							Any									=scheduler.scale_model_input(_A   ,							_A      )

																				a__:							Tuple									=model(_A   ,							_A      )

																				a__:							Any									=scheduler.step(_A   ,							_A   ,							_A   ,							generator=_A      )
																				a__:							Dict									=output.prev_sample

													a__:							List[str]									=torch.sum(torch.abs(_A      )      )
													a__:							Any									=torch.mean(torch.abs(_A      )      )

													assert abs(result_sum.item() - 1_0_8.4_4_3_9      ) < 1e-2
													assert abs(result_mean.item() - 0.1_4_1_2      ) < 1e-3







						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							str									=self.scheduler_classes[0]
													a__:							List[Any]									=self.get_scheduler_config()
													a__:							Union[str, Any]									=scheduler_class(**_A      )

													scheduler.set_timesteps(self.num_inference_steps   ,							device=_A      )
													a__:							Optional[int]									=torch.manual_seed(0      )

													a__:							Any									=self.dummy_model()
													a__:							Union[str, Any]									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													a__:							Optional[Any]									=sample.to(_A      )

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

																				a__:							Dict									=model(_A   ,							_A      )

																				a__:							str									=scheduler.step(_A   ,							_A   ,							_A   ,							generator=_A      )
																				a__:							List[str]									=output.prev_sample

													a__:							Any									=torch.sum(torch.abs(_A      )      )
													a__:							List[Any]									=torch.mean(torch.abs(_A      )      )

													assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
													assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3






"""simple docstring"""







from typing import TYPE_CHECKING

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


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "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

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





