



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			(DDIMScheduler,)
							_a      				=			(('''eta''', 0.0), ('''num_inference_steps''', 50))




							def        UpperCAmelCase__      (	self					:	Optional[int]    ,    **A_					:	int):
									lowerCAmelCase_      :					Optional[Any]     =			{
									    '''num_train_timesteps''': 1_0_0_0,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**A_)
									return config




							def        UpperCAmelCase__      (	self					:	Dict    ,    **A_					:	Optional[int]):
									lowerCAmelCase_      :					List[Any]     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					Optional[int]     =			self.get_scheduler_config(**A_)
									lowerCAmelCase_      :					Optional[int]     =			scheduler_class(**A_)

									lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[int]     =			1_0, 0.0

									lowerCAmelCase_      :					Union[str, Any]     =			self.dummy_model()
									lowerCAmelCase_      :					Optional[int]     =			self.dummy_sample_deter

									scheduler.set_timesteps(A_)

									for t in scheduler.timesteps:
											lowerCAmelCase_      :					List[str]     =			model(A_    ,    A_)
											lowerCAmelCase_      :					List[Any]     =			scheduler.step(A_    ,    A_    ,    A_    ,    A_).prev_sample

									return sample




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




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=A_)

									lowerCAmelCase_      :					Union[str, Any]     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					Tuple     =			self.get_scheduler_config(steps_offset=1)
									lowerCAmelCase_      :					str     =			scheduler_class(**A_)
									scheduler.set_timesteps(5)
									assert torch.equal(scheduler.timesteps    ,    torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]))




							def        UpperCAmelCase__      (	self					:	Optional[int]):
									for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,    [0.002, 0.02, 0.2, 2]):
											self.check_over_configs(beta_start=A_    ,    beta_end=A_)




							def        UpperCAmelCase__      (	self					:	Any):
									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=A_)




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




							def        UpperCAmelCase__      (	self					:	str):
									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=A_)




							def        UpperCAmelCase__      (	self					:	Dict):
									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=A_)




							def        UpperCAmelCase__      (	self					:	Tuple):
									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=A_)




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self.check_over_configs(thresholding=A_)
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=A_    ,    prediction_type=A_    ,    sample_max_value=A_    ,    )




							def        UpperCAmelCase__      (	self					:	Dict):
									for t in [1, 1_0, 4_9]:
											self.check_over_forward(time_step=A_)




							def        UpperCAmelCase__      (	self					:	Any):
									for t, num_inference_steps in zip([1, 1_0, 5_0]    ,    [1_0, 5_0, 5_0_0]):
											self.check_over_forward(time_step=A_    ,    num_inference_steps=A_)




							def        UpperCAmelCase__      (	self					:	Any):
									for t, eta in zip([1, 1_0, 4_9]    ,    [0.0, 0.5, 1.0]):
											self.check_over_forward(time_step=A_    ,    eta=A_)




							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					str     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					int     =			self.get_scheduler_config()
									lowerCAmelCase_      :					Union[str, Any]     =			scheduler_class(**A_)

									assert torch.sum(torch.abs(scheduler._get_variance(0    ,    0) - 0.0)) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_2_0    ,    4_0_0) - 0.1_4771)) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_8_0    ,    9_6_0) - 0.3_2460)) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0    ,    0) - 0.0)) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ,    4_8_6) - 0.0_0979)) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ,    9_9_8) - 0.02)) < 1e-5




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					str     =			self.full_loop()

									lowerCAmelCase_      :					List[Any]     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					Tuple     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 172.0067) < 1e-2
									assert abs(result_mean.item() - 0.22_3967) < 1e-3




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									lowerCAmelCase_      :					Optional[int]     =			self.full_loop(prediction_type='''v_prediction''')

									lowerCAmelCase_      :					int     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					int     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 52.5302) < 1e-2
									assert abs(result_mean.item() - 0.0684) < 1e-3




							def        UpperCAmelCase__      (	self					:	Dict):
									# We specify different beta, so that the first alpha is 0.99
									lowerCAmelCase_      :					Tuple     =			self.full_loop(set_alpha_to_one=A_    ,    beta_start=0.01)
									lowerCAmelCase_      :					str     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					Dict     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 149.8295) < 1e-2
									assert abs(result_mean.item() - 0.1951) < 1e-3







							def        UpperCAmelCase__      (	self					:	int):
									# We specify different beta, so that the first alpha is 0.99
									lowerCAmelCase_      :					int     =			self.full_loop(set_alpha_to_one=A_    ,    beta_start=0.01)
									lowerCAmelCase_      :					List[str]     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					List[str]     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 149.0784) < 1e-2
									assert abs(result_mean.item() - 0.1941) < 1e-3










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			gpt.pos_emb
		trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
		trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
		trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

		for i, block in enumerate(gpt.blocks			):
				trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
				trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
				trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

				trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
				trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
				trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
				trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

		torch.save(trajectory_transformer.state_dict()					,'''pytorch_model.bin'''			)


if __name__ == "__main__":
						A__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





