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      lowerCAmelCase_    ( snake_case_	:			Dict      )   ->				Any:


     '''simple docstring'''




     if hor == 1_28:
          UpperCAmelCase_	   =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
          UpperCAmelCase_	   =       (32, 1_28, 2_56)
          UpperCAmelCase_	   =       ('UpResnetBlock1D', 'UpResnetBlock1D')

     elif hor == 32:
          UpperCAmelCase_	   =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
          UpperCAmelCase_	   =       (32, 64, 1_28, 2_56)
          UpperCAmelCase_	   =       ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
     UpperCAmelCase_	   =       torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""      )
     UpperCAmelCase_	   =       model.state_dict()
     UpperCAmelCase_	   =       {
         '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_55_36,
         'mid_block_type': 'MidResTemporalBlock1D',
         'act_fn': 'mish',
     }
     UpperCAmelCase_	   =       UNetaDModel(**_A      )
     print(f"""length of state dict: {len(state_dict.keys()      )}"""      )
     print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()      )}"""      )
     UpperCAmelCase_	   =       dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
     for k, v in mapping.items():
          UpperCAmelCase_	   =       state_dict.pop(_A      )
     hf_value_function.load_state_dict(_A      )

     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(_A ,   _A      )





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         '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, 1_28, 2_56),
         'layers_per_block': 1,
         'downsample_each_block': True,
         'sample_size': 6_55_36,
         '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',
     }

     UpperCAmelCase_	   =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"      )
     UpperCAmelCase_	   =       model
     UpperCAmelCase_	   =       UNetaDModel(**_A      )
     print(f"""length of state dict: {len(state_dict.keys()      )}"""      )
     print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()      )}"""      )

     UpperCAmelCase_	   =       dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
     for k, v in mapping.items():
          UpperCAmelCase_	   =       state_dict.pop(_A      )

     hf_value_function.load_state_dict(_A      )

     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(_A ,   _A      )


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





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




