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     (    A				):







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

     elif hor == 32:
          UpperCamelCase__					    =   ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          UpperCamelCase__					    =   (32, 64, 128, 256)
          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''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     UpperCamelCase__					    =   UNetaDModel(**snake_case_				)
     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(snake_case_				)
     hf_value_function.load_state_dict(snake_case_				)

     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(snake_case_			,				snake_case_				)







def 			__UpperCamelCase     (    ):
     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, 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''',
     }

     UpperCamelCase__					    =   torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''				)
     UpperCamelCase__					    =   model
     UpperCamelCase__					    =   UNetaDModel(**snake_case_				)
     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(snake_case_				)

     hf_value_function.load_state_dict(snake_case_				)

     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(snake_case_			,				snake_case_				)


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










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




