



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[Any] =       (DDPMParallelScheduler,)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= {
											    "num_train_timesteps": 1000,
											    "beta_start": 0.0_0_0_1,
											    "beta_end": 0.0_2,
											    "beta_schedule": "linear",
											    "variance_type": "fixed_small",
											    "clip_sample": True,
											}

											config.update(**__UpperCAmelCase				)
											return config



							def 		_A	(							self				):







											"""simple docstring"""



											for timesteps in [1, 5, 100, 1000]:
															self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,					[0.0_0_2, 0.0_2, 0.2, 2]				):
															self.check_over_configs(beta_start=__UpperCAmelCase ,					beta_end=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



											for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



											for t in [0, 500, 999]:
															self.check_over_forward(time_step=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.scheduler_classes[0]
											a__	:							Union[str, Any]      						= self.get_scheduler_config()
											a__	:							Union[str, Any]      						= scheduler_class(**__UpperCAmelCase				)

											assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0_9_7_9				)				) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.0_2				)				) < 1E-5



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.scheduler_classes[0]
											a__	:							str      						= self.get_scheduler_config()
											a__	:							Any      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							Any      						= len(__UpperCAmelCase				)

											a__	:							Optional[int]      						= self.dummy_model()
											a__	:							Union[str, Any]      						= self.dummy_sample_deter
											a__	:							Dict      						= self.dummy_sample_deter + 0.1
											a__	:							str      						= self.dummy_sample_deter - 0.1

											a__	:							Tuple      						= samplea.shape[0]
											a__	:							str      						= torch.stack([samplea, samplea, samplea] ,					dim=0				)
											a__	:							str      						= torch.arange(__UpperCAmelCase				)[0:3, None].repeat(1 ,					__UpperCAmelCase				)

											a__	:							Union[str, Any]      						= model(samples.flatten(0 ,					1				) ,					timesteps.flatten(0 ,					1				)				)
											a__	:							str      						= scheduler.batch_step_no_noise(__UpperCAmelCase ,					timesteps.flatten(0 ,					1				) ,					samples.flatten(0 ,					1				)				)

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

											assert abs(result_sum.item() - 1153.1833				) < 1E-2
											assert abs(result_mean.item() - 0.5_0_0_5				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.scheduler_classes[0]
											a__	:							Dict      						= self.get_scheduler_config()
											a__	:							str      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							int      						= len(__UpperCAmelCase				)

											a__	:							int      						= self.dummy_model()
											a__	:							Optional[int]      						= self.dummy_sample_deter
											a__	:							str      						= torch.manual_seed(0				)

											for t in reversed(range(__UpperCAmelCase				)				):
															# 1. predict noise residual
															a__	:							Union[str, Any]      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)

															# 2. predict previous mean of sample x_t-1
															a__	:							Any      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					generator=__UpperCAmelCase				).prev_sample

															a__	:							Optional[Any]      						= pred_prev_sample

											a__	:							Optional[int]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							str      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1E-2
											assert abs(result_mean.item() - 0.3_3_7_2				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.scheduler_classes[0]
											a__	:							Union[str, Any]      						= self.get_scheduler_config(prediction_type="v_prediction"				)
											a__	:							Optional[Any]      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							Union[str, Any]      						= len(__UpperCAmelCase				)

											a__	:							Optional[int]      						= self.dummy_model()
											a__	:							str      						= self.dummy_sample_deter
											a__	:							List[Any]      						= torch.manual_seed(0				)

											for t in reversed(range(__UpperCAmelCase				)				):
															# 1. predict noise residual
															a__	:							List[Any]      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)

															# 2. predict previous mean of sample x_t-1
															a__	:							List[str]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					generator=__UpperCAmelCase				).prev_sample

															a__	:							Dict      						= pred_prev_sample

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

											assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1E-2
											assert abs(result_mean.item() - 0.2_6_3_1				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							Dict      						= [100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__UpperCAmelCase				)

											a__	:							Dict      						= scheduler.timesteps

											for i, timestep in enumerate(__UpperCAmelCase				):
															if i == len(__UpperCAmelCase				) - 1:
																			a__	:							Optional[int]      						= -1
															else:
																			a__	:							str      						= timesteps[i + 1]

															a__	:							List[str]      						= scheduler.previous_timestep(__UpperCAmelCase				)
															a__	:							Any      						= prev_t.item()

															self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.scheduler_classes[0]
											a__	:							Optional[Any]      						= self.get_scheduler_config()
											a__	:							Dict      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							Union[str, Any]      						= [100, 87, 50, 51, 0]

											with self.assertRaises(__UpperCAmelCase ,					msg="`custom_timesteps` must be in descending order."				):
															scheduler.set_timesteps(timesteps=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.scheduler_classes[0]
											a__	:							Dict      						= self.get_scheduler_config()
											a__	:							Optional[int]      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							int      						= [100, 87, 50, 1, 0]
											a__	:							Union[str, Any]      						= len(__UpperCAmelCase				)

											with self.assertRaises(__UpperCAmelCase ,					msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."				):
															scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase ,					timesteps=__UpperCAmelCase				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.scheduler_classes[0]
											a__	:							List[Any]      						= self.get_scheduler_config()
											a__	:							Tuple      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __UpperCAmelCase ,					msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,					):
															scheduler.set_timesteps(timesteps=__UpperCAmelCase				)





"""simple docstring"""

import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 if hor == 1_2_8:
  lowerCAmelCase__   :				str 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				int 			=				(3_2, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D")

 elif hor == 3_2:
  lowerCAmelCase__   :				Any 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				Dict 			=				(3_2, 6_4, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
 lowerCAmelCase__   :				Tuple 			=				torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""   )
 lowerCAmelCase__   :				str 			=				model.state_dict()
 lowerCAmelCase__   :				Union[str, Any] 			=				{
     "down_block_types": down_block_types,
     "block_out_channels": block_out_channels,
     "up_block_types": up_block_types,
     "layers_per_block": 1,
     "use_timestep_embedding": True,
     "out_block_type": "OutConv1DBlock",
     "norm_num_groups": 8,
     "downsample_each_block": False,
     "in_channels": 1_4,
     "out_channels": 1_4,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "sample_size": 6_5_5_3_6,
     "mid_block_type": "MidResTemporalBlock1D",
     "act_fn": "mish",
 }
 lowerCAmelCase__   :				str 			=				UNetaDModel(**lowercase__   )
 print(F"""length of state dict: {len(state_dict.keys()   )}"""   )
 print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()   )}"""   )
 lowerCAmelCase__   :				Dict 			=				dict(zip(model.state_dict().keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				Tuple 			=				state_dict.pop(lowercase__   )
 hf_value_function.load_state_dict(lowercase__   )

 torch.save(hf_value_function.state_dict()					,					F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""   )
 with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""					,					"w"   ) as f:
  json.dump(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Any:
 lowerCAmelCase__   :				Dict 			=				{
     "in_channels": 1_4,
     "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
     "up_block_types": (),
     "out_block_type": "ValueFunction",
     "mid_block_type": "ValueFunctionMidBlock1D",
     "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
     "layers_per_block": 1,
     "downsample_each_block": True,
     "sample_size": 6_5_5_3_6,
     "out_channels": 1_4,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "use_timestep_embedding": True,
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "norm_num_groups": 8,
     "act_fn": "mish",
 }

 lowerCAmelCase__   :				Any 			=				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"   )
 lowerCAmelCase__   :				Optional[int] 			=				model
 lowerCAmelCase__   :				Any 			=				UNetaDModel(**lowercase__   )
 print(F"""length of state dict: {len(state_dict.keys()   )}"""   )
 print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()   )}"""   )

 lowerCAmelCase__   :				str 			=				dict(zip(state_dict.keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				List[Any] 			=				state_dict.pop(lowercase__   )

 hf_value_function.load_state_dict(lowercase__   )

 torch.save(hf_value_function.state_dict()					,					"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"   )
 with open("hub/hopper-medium-v2/value_function/config.json"					,					"w"   ) as f:
  json.dump(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    unet(3_2)
    # unet(128)
    value_function()






