





'''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 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    if hor == 128:
        lowercase         = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowercase         = (32, 128, 256)
        lowercase         = ("UpResnetBlock1D", "UpResnetBlock1D")

    elif hor == 32:
        lowercase         = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowercase         = (32, 64, 128, 256)
        lowercase         = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
    lowercase         = torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'							)
    lowercase         = model.state_dict()
    lowercase         = {
        "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         = 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         = dict(zip(model.state_dict().keys()		,							hf_value_function.state_dict().keys()							)							)
    for k, v in mapping.items():
        lowercase         = 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_    (	):

    """simple docstring"""






    lowercase         = {
        "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         = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
    lowercase         = model
    lowercase         = 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         = dict(zip(state_dict.keys()		,							hf_value_function.state_dict().keys()							)							)
    for k, v in mapping.items():
        lowercase         = 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'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




