
"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       (DDIMScheduler,)
							SCREAMING_SNAKE_CASE_     				=       (("""eta""", 0.0), ("""num_inference_steps""", 50))





							def 				UpperCAmelCase__							(  self  :List[Any]					,							**lowerCamelCase_  :int	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		={
										    'num_train_timesteps': 1_000,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**lowerCamelCase_	)
										return config





							def 				UpperCAmelCase__							(  self  :Dict					,							**lowerCamelCase_  :List[str]	):





										"""simple docstring"""

										lowerCamelCase__     :					str		=self.scheduler_classes[0]
										lowerCamelCase__     :					Any		=self.get_scheduler_config(**lowerCamelCase_	)
										lowerCamelCase__     :					Optional[int]		=scheduler_class(**lowerCamelCase_	)

										lowerCamelCase__     :					Optional[int]		=10, 0.0

										lowerCamelCase__     :					List[str]		=self.dummy_model()
										lowerCamelCase__     :					str		=self.dummy_sample_deter

										scheduler.set_timesteps(lowerCamelCase_	)

										for t in scheduler.timesteps:
													lowerCamelCase__     :					Union[str, Any]		=model(lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					Tuple		=scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	).prev_sample

										return sample





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										for timesteps in [100, 500, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase_	)

										lowerCamelCase__     :					Dict		=self.scheduler_classes[0]
										lowerCamelCase__     :					List[str]		=self.get_scheduler_config(steps_offset=1	)
										lowerCamelCase__     :					Dict		=scheduler_class(**lowerCamelCase_	)
										scheduler.set_timesteps(5	)
										assert torch.equal(scheduler.timesteps					,							torch.LongTensor([801, 601, 401, 201, 1]	)	)





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]					,							[0.0_02, 0.02, 0.2, 2]	):
													self.check_over_configs(beta_start=lowerCamelCase_					,							beta_end=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :int	):





										"""simple docstring"""

										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										self.check_over_configs(thresholding=lowerCamelCase_	)
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_					,							prediction_type=lowerCamelCase_					,							sample_max_value=lowerCamelCase_					,							)





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										for t in [1, 10, 49]:
													self.check_over_forward(time_step=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										for t, num_inference_steps in zip([1, 10, 50]					,							[10, 50, 500]	):
													self.check_over_forward(time_step=lowerCamelCase_					,							num_inference_steps=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										for t, eta in zip([1, 10, 49]					,							[0.0, 0.5, 1.0]	):
													self.check_over_forward(time_step=lowerCamelCase_					,							eta=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=self.scheduler_classes[0]
										lowerCamelCase__     :					str		=self.get_scheduler_config()
										lowerCamelCase__     :					Any		=scheduler_class(**lowerCamelCase_	)

										assert torch.sum(torch.abs(scheduler._get_variance(0					,							0	) - 0.0	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(420					,							400	) - 0.1_47_71	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(980					,							960	) - 0.3_24_60	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(0					,							0	) - 0.0	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487					,							486	) - 0.0_09_79	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999					,							998	) - 0.02	)	) < 1e-5





							def 				UpperCAmelCase__							(  self  :Union[str, Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Dict		=self.full_loop()

										lowerCamelCase__     :					int		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Dict		=torch.mean(torch.abs(lowerCamelCase_	)	)

										assert abs(result_sum.item() - 172.0_067	) < 1e-2
										assert abs(result_mean.item() - 0.22_39_67	) < 1e-3





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=self.full_loop(prediction_type='v_prediction'	)

										lowerCamelCase__     :					Tuple		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Optional[int]		=torch.mean(torch.abs(lowerCamelCase_	)	)

										assert abs(result_sum.item() - 52.53_02	) < 1e-2
										assert abs(result_mean.item() - 0.06_84	) < 1e-3





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=self.full_loop(set_alpha_to_one=lowerCamelCase_					,							beta_start=0.01	)
										lowerCamelCase__     :					Optional[int]		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Union[str, Any]		=torch.mean(torch.abs(lowerCamelCase_	)	)

										assert abs(result_sum.item() - 149.8_295	) < 1e-2
										assert abs(result_mean.item() - 0.19_51	) < 1e-3





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					str		=self.full_loop(set_alpha_to_one=lowerCamelCase_					,							beta_start=0.01	)
										lowerCamelCase__     :					Any		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Union[str, Any]		=torch.mean(torch.abs(lowerCamelCase_	)	)

										assert abs(result_sum.item() - 149.0_784	) < 1e-2
										assert abs(result_mean.item() - 0.19_41	) < 1e-3

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)