


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_(   lowerCamelCase_				)					->		Tuple:
    if hor == 128:
        _lowercase :							List[Any]   	=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
        _lowercase :							Optional[Any]   	=	(32, 128, 256)
        _lowercase :							Tuple   	=	('UpResnetBlock1D', 'UpResnetBlock1D')

    elif hor == 32:
        _lowercase :							List[str]   	=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
        _lowercase :							Optional[int]   	=	(32, 64, 128, 256)
        _lowercase :							List[str]   	=	('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
    _lowercase :							Optional[int]   	=	torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''				)
    _lowercase :							Union[str, Any]   	=	model.state_dict()
    _lowercase :							int   	=	{
        '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',
    }
    _lowercase :							Optional[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()				)}'''				)
    _lowercase :							Union[str, Any]   	=	dict(zip(model.state_dict().keys()							,				hf_value_function.state_dict().keys()				)				)
    for k, v in mapping.items():
        _lowercase :							Any   	=	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        UpperCamelCase_(   )					->		Tuple:
    _lowercase :							Union[str, 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',
    }

    _lowercase :							Union[str, Any]   	=	torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'				)
    _lowercase :							Union[str, Any]   	=	model
    _lowercase :							Tuple   	=	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()				)}'''				)

    _lowercase :							List[Any]   	=	dict(zip(state_dict.keys()							,				hf_value_function.state_dict().keys()				)				)
    for k, v in mapping.items():
        _lowercase :							Optional[int]   	=	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()






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


SCREAMING_SNAKE_CASE :   List[Any]							      =				subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE :   Optional[int]							      =				(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)

SCREAMING_SNAKE_CASE :   Any							      =				"|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				re.compile(rF"^({joined_dirs}).*?\.py$")

SCREAMING_SNAKE_CASE :   List[Any]							      =				[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")


