






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 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Optional[Any]:

						"""simple docstring"""



						if hor == 128:
												lowerCAmelCase__        =  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
												lowerCAmelCase__        =  (32, 128, 256)
												lowerCAmelCase__        =  ('UpResnetBlock1D', 'UpResnetBlock1D')

						elif hor == 32:
												lowerCAmelCase__        =  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
												lowerCAmelCase__        =  (32, 64, 128, 256)
												lowerCAmelCase__        =  ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
						lowerCAmelCase__        =  torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"						)
						lowerCAmelCase__        =  model.state_dict()
						lowerCAmelCase__        =  {
						    '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': 14,
						    'out_channels': 14,
						    'extra_in_channels': 0,
						    'time_embedding_type': 'positional',
						    'flip_sin_to_cos': False,
						    'freq_shift': 1,
						    'sample_size': 6_5536,
						    'mid_block_type': 'MidResTemporalBlock1D',
						    'act_fn': 'mish',
						}
						lowerCAmelCase__        =  UNetaDModel(**UpperCamelCase_						)
						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(zip(model.state_dict().keys()      ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCAmelCase__        =  state_dict.pop(UpperCamelCase_						)
						hf_value_function.load_state_dict(UpperCamelCase_						)

						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(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      )					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  {
						    'in_channels': 14,
						    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
						    'up_block_types': (),
						    'out_block_type': 'ValueFunction',
						    'mid_block_type': 'ValueFunctionMidBlock1D',
						    'block_out_channels': (32, 64, 128, 256),
						    'layers_per_block': 1,
						    'downsample_each_block': True,
						    'sample_size': 6_5536,
						    'out_channels': 14,
						    '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__        =  torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'						)
						lowerCAmelCase__        =  model
						lowerCAmelCase__        =  UNetaDModel(**UpperCamelCase_						)
						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(zip(state_dict.keys()      ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCAmelCase__        =  state_dict.pop(UpperCamelCase_						)

						hf_value_function.load_state_dict(UpperCamelCase_						)

						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(UpperCamelCase_      ,    UpperCamelCase_						)


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





