'''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  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     if hor == 128:
          UpperCAmelCase_							:       Dict       =				('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
          UpperCAmelCase_							:       Optional[int]       =				(32, 128, 256)
          UpperCAmelCase_							:       Dict       =				('UpResnetBlock1D', 'UpResnetBlock1D')

     elif hor == 32:
          UpperCAmelCase_							:       Optional[Any]       =				('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
          UpperCAmelCase_							:       Optional[int]       =				(32, 64, 128, 256)
          UpperCAmelCase_							:       List[Any]       =				('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
     UpperCAmelCase_							:       List[Any]       =				torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
     UpperCAmelCase_							:       Dict       =				model.state_dict()
     UpperCAmelCase_							:       Tuple       =				{
         '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_							:       Any       =				UNetaDModel(**lowerCamelCase_       )
     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_							:       int       =				dict(zip(model.state_dict().keys()     ,					hf_value_function.state_dict().keys()       )       )
     for k, v in mapping.items():
          UpperCAmelCase_							:       Tuple       =				state_dict.pop(lowerCamelCase_       )
     hf_value_function.load_state_dict(lowerCamelCase_       )

     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(lowerCamelCase_     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       str       =				{
         '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_							:       List[str]       =				torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
     UpperCAmelCase_							:       List[Any]       =				model
     UpperCAmelCase_							:       int       =				UNetaDModel(**lowerCamelCase_       )
     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       =				dict(zip(state_dict.keys()     ,					hf_value_function.state_dict().keys()       )       )
     for k, v in mapping.items():
          UpperCAmelCase_							:       List[str]       =				state_dict.pop(lowerCamelCase_       )

     hf_value_function.load_state_dict(lowerCamelCase_       )

     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(lowerCamelCase_     ,					lowerCamelCase_       )


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







'''simple docstring'''


snake_case__					:      Optional[Any]				      =  tuple[float, float, float]
snake_case__					:      Tuple				      =  tuple[float, float, float]


def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Any       =				end_pointa[0] - end_pointa[0]
     UpperCAmelCase_							:       Optional[Any]       =				end_pointa[1] - end_pointa[1]
     UpperCAmelCase_							:       Any       =				end_pointa[2] - end_pointa[2]
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							Vectorad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				ab[1] * ac[2] - ab[2] * ac[1]  # *i
     UpperCAmelCase_							:       Optional[Any]       =				(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     UpperCAmelCase_							:       Dict       =				ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return tuple(round(lowerCamelCase_     ,					lowerCamelCase_       ) for x in vector       ) == (0, 0, 0)





def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							int = 10       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     return is_zero_vector(get_ad_vectors_cross(lowerCamelCase_     ,					lowerCamelCase_       )     ,					lowerCamelCase_       )






