'''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 snake_case_      (					__snake_case  :							Any)			->					Dict:


   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''': 65536,
       '''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 snake_case_      (					)			->					List[str]:
   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''': 65536,
       '''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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     try:
          A_          =       parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_          =       '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_          =       ''' '''.join(str(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).split(''' '''				)[-1]				)
          A_          =       []
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]				)
               else:
                    wrong_args.append(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



