

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						if hor == 128:
												snake_case_   =	('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
												snake_case_   =	(32, 128, 256)
												snake_case_   =	('''UpResnetBlock1D''', '''UpResnetBlock1D''')

						elif hor == 32:
												snake_case_   =	('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
												snake_case_   =	(32, 64, 128, 256)
												snake_case_   =	('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
						snake_case_   =	torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
						snake_case_   =	model.state_dict()
						snake_case_   =	{
						    '''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''',
						}
						snake_case_   =	UNetaDModel(**SCREAMING_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()       )}'''       )
						snake_case_   =	dict(zip(model.state_dict().keys()  ,		hf_value_function.state_dict().keys()       )       )
						for k, v in mapping.items():
												snake_case_   =	state_dict.pop(SCREAMING_SNAKE_CASE__       )
						hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{
						    '''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''',
						}

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

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

						hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


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


from collections import deque

from .hash_table import HashTable





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Tuple							)      ->Tuple:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict							)      ->Tuple:
									snake_case_   =	deque([]							) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(_UpperCamelCase							)
									snake_case_   =	self.values[key]







			def 				snake_case__(     self :				List[Any]							)      ->str:
									return (
									    sum(self.charge_factor - len(_UpperCamelCase							) for slot in self.values							)
									    / self.size_table
									    * self.charge_factor
									)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=None							)      ->str:
									if not (
									    len(self.values[key]							) == self.charge_factor and self.values.count(_UpperCamelCase							) == 0
									):
															return key
									return super()._collision_resolution(_UpperCamelCase							,   _UpperCamelCase							)