





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   ( _UpperCAmelCase				)    ->  Any:
				if hor == 128:
								lowerCamelCase__					:   Tuple      				=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
								lowerCamelCase__					:   Optional[Any]      				=	(32, 128, 256)
								lowerCamelCase__					:   Optional[Any]      				=	('UpResnetBlock1D', 'UpResnetBlock1D')

				elif hor == 32:
								lowerCamelCase__					:   Optional[int]      				=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
								lowerCamelCase__					:   Optional[Any]      				=	(32, 64, 128, 256)
								lowerCamelCase__					:   Optional[Any]      				=	('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
				lowerCamelCase__					:   Any      				=	torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
				lowerCamelCase__					:   List[str]      				=	model.state_dict()
				lowerCamelCase__					:   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': 6_5536,
				    'mid_block_type': 'MidResTemporalBlock1D',
				    'act_fn': 'mish',
				}
				lowerCamelCase__					:   Optional[int]      				=	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__					:   Tuple      				=	dict(zip(model.state_dict().keys()		,			hf_value_function.state_dict().keys()				)				)
				for k, v in mapping.items():
								lowerCamelCase__					:   str      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
				lowerCamelCase__					:   Any      				=	{
				    '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': 6_5536,
				    '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__					:   Any      				=	torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'				)
				lowerCamelCase__					:   Any      				=	model
				lowerCamelCase__					:   Union[str, Any]      				=	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__					:   Tuple      				=	dict(zip(state_dict.keys()		,			hf_value_function.state_dict().keys()				)				)
				for k, v in mapping.items():
								lowerCamelCase__					:   Tuple      				=	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()












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    sock.connect((host, port)				)
    sock.send(b'Hello server!'				)

    with open('Received_file'		,			'wb'				) as out_file:
        print('File opened'				)
        print('Receiving data...'				)
        while True:
            lowerCamelCase__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

    print('Successfully received the file'				)
    sock.close()
    print('Connection closed'				)


if __name__ == "__main__":
    main()





