




import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__lowerCAmelCase :Tuple     =    logging.getLogger(__name__)






class        _a(    __A    ):
		lowerCamelCase__     :List[str]   				=		"""summarization"""
		lowerCamelCase__     :str   				=		["""loss"""]
		lowerCamelCase__     :str   				=		ROUGE_KEYS
		lowerCamelCase__     :List[Any]   				=		"""rouge2"""







		def __init__(    self ,						__snake_case ,						**__snake_case				)      ->      Optional[int]:


							'''simple docstring'''

							if hparams.sortish_sampler and hparams.gpus > 1:
												_snake_case       :					Any				       =   False
							elif hparams.max_tokens_per_batch is not None:
												if hparams.gpus > 1:
																	raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"				)
												if hparams.sortish_sampler:
																	raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"				)

							super().__init__(__snake_case ,						num_labels=__snake_case ,						mode=self.mode ,						**__snake_case				)
							use_task_specific_params(self.model ,						"summarization"				)
							save_git_info(self.hparams.output_dir				)
							_snake_case       :					Optional[Any]				       =   Path(self.output_dir				) / "metrics.json"
							_snake_case       :					Tuple				       =   Path(self.output_dir				) / "hparams.pkl"
							pickle_save(self.hparams ,						self.hparams_save_path				)
							_snake_case       :					Union[str, Any]				       =   0
							_snake_case       :					List[str]				       =   defaultdict(__snake_case				)
							_snake_case       :					Tuple				       =   self.config.model_type
							_snake_case       :					Union[str, Any]				       =   self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

							_snake_case       :					Tuple				       =   {
							    "data_dir": self.hparams.data_dir,
							    "max_source_length": self.hparams.max_source_length,
							    "prefix": self.model.config.prefix or "",
							}
							_snake_case       :					str				       =   {
							    "train": self.hparams.n_train,
							    "val": self.hparams.n_val,
							    "test": self.hparams.n_test,
							}
							_snake_case       :					List[Any]				       =   {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

							_snake_case       :					List[Any]				       =   {
							    "train": self.hparams.max_target_length,
							    "val": self.hparams.val_max_target_length,
							    "test": self.hparams.test_max_target_length,
							}
							assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
							assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
							if self.hparams.freeze_embeds:
												freeze_embeds(self.model				)
							if self.hparams.freeze_encoder:
												freeze_params(self.model.get_encoder()				)
												assert_all_frozen(self.model.get_encoder()				)

							_snake_case       :					Optional[Any]				       =   get_git_info()["repo_sha"]
							_snake_case       :					Any				       =   hparams.num_workers
							_snake_case       :					str				       =   None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,						__snake_case				):
												_snake_case       :					Dict				       =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
												_snake_case       :					List[Any]				       =   self.decoder_start_token_id
							_snake_case       :					Optional[Any]				       =   (
							    SeqaSeqDataset if hasattr(self.tokenizer ,						"prepare_seq2seq_batch"				) else LegacySeqaSeqDataset
							)
							_snake_case       :					str				       =   False
							_snake_case       :					Any				       =   self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
							if self.hparams.eval_max_gen_length is not None:
												_snake_case       :					Union[str, Any]				       =   self.hparams.eval_max_gen_length
							else:
												_snake_case       :					Any				       =   self.model.config.max_length
							_snake_case       :					Any				       =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







		def        lowercase       (    self ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   {
							    k: self.tokenizer.batch_decode(v.tolist()				) if "mask" not in k else v.shape for k, v in batch.items()
							}
							save_json(__snake_case ,						Path(self.output_dir				) / "text_batch.json"				)
							save_json({k: v.tolist() for k, v in batch.items()} ,						Path(self.output_dir				) / "tok_batch.json"				)

							_snake_case       :					Union[str, Any]				       =   True
							return readable_batch







		def        lowercase       (    self ,						__snake_case ,						**__snake_case				)      ->      Dict:


							'''simple docstring'''

							return self.model(__snake_case ,						**__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Any				       =   self.tokenizer.batch_decode(
							    __snake_case ,						skip_special_tokens=__snake_case ,						clean_up_tokenization_spaces=__snake_case				)
							return lmap(str.strip ,						__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					str				       =   self.tokenizer.pad_token_id
							_snake_case       , _snake_case       :					int				       =   batch["input_ids"], batch["attention_mask"]
							_snake_case       :					str				       =   batch["labels"]
							if isinstance(self.model ,						__snake_case				):
												_snake_case       :					str				       =   self.model._shift_right(__snake_case				)
							else:
												_snake_case       :					Optional[Any]				       =   shift_tokens_right(__snake_case ,						__snake_case				)
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
												_snake_case       :					Union[str, Any]				       =   decoder_input_ids
												self.save_readable_batch(__snake_case				)

							_snake_case       :					int				       =   self(__snake_case ,						attention_mask=__snake_case ,						decoder_input_ids=__snake_case ,						use_cache=__snake_case				)
							_snake_case       :					Optional[int]				       =   outputs["logits"]
							if self.hparams.label_smoothing == 0:
												# Same behavior as modeling_bart.py, besides ignoring pad_token_id
												_snake_case       :					Dict				       =   nn.CrossEntropyLoss(ignore_index=__snake_case				)

												assert lm_logits.shape[-1] == self.vocab_size
												_snake_case       :					str				       =   ce_loss_fct(lm_logits.view(-1 ,						lm_logits.shape[-1]				) ,						tgt_ids.view(-1				)				)
							else:
												_snake_case       :					str				       =   nn.functional.log_softmax(__snake_case ,						dim=-1				)
												_snake_case       , _snake_case       :					Union[str, Any]				       =   label_smoothed_nll_loss(
												    __snake_case ,						__snake_case ,						self.hparams.label_smoothing ,						ignore_index=__snake_case				)
							return (loss,)







		@property
		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							return self.tokenizer.pad_token_id







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Any				       =   self._step(__snake_case				)

							_snake_case       :					Any				       =   dict(zip(self.loss_names ,						__snake_case				)				)
							# tokens per batch
							_snake_case       :					Optional[Any]				       =   batch["input_ids"].ne(self.pad				).sum() + batch["labels"].ne(self.pad				).sum()
							_snake_case       :					List[Any]				       =   batch["input_ids"].shape[0]
							_snake_case       :					Tuple				       =   batch["input_ids"].eq(self.pad				).sum()
							_snake_case       :					str				       =   batch["input_ids"].eq(self.pad				).float().mean()
							# TODO(SS): make a wandb summary metric for this
							return {"loss": loss_tensors[0], "log": logs}







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							return self._generative_step(__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case="val"				)      ->      str:


							'''simple docstring'''

							self.step_count += 1
							_snake_case       :					List[str]				       =   {k: torch.stack([x[k] for x in outputs]				).mean() for k in self.loss_names}
							_snake_case       :					List[str]				       =   losses["loss"]
							_snake_case       :					List[str]				       =   {
							    k: np.array([x[k] for x in outputs]				).mean() for k in self.metric_names + ["gen_time", "gen_len"]
							}
							_snake_case       :					Any				       =   (
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							_snake_case       :					Union[str, Any]				       =   torch.tensor(__snake_case				).type_as(__snake_case				)
							generative_metrics.update({k: v.item() for k, v in losses.items()}				)
							losses.update(__snake_case				)
							_snake_case       :					List[str]				       =   {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
							_snake_case       :					Dict				       =   self.step_count
							self.metrics[prefix].append(__snake_case				)  # callback writes this to self.metrics_save_path
							_snake_case       :					Optional[Any]				       =   flatten_list([x["preds"] for x in outputs]				)
							return {
							    "log": all_metrics,
							    "preds": preds,
							    f"""{prefix}_loss""": loss,
							    f"""{prefix}_{self.val_metric}""": metric_tensor,
							}







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							return calculate_rouge(__snake_case ,						__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   time.time()

							# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
							_snake_case       :					Any				       =   self.model.generate(
							    batch["input_ids"] ,						attention_mask=batch["attention_mask"] ,						use_cache=__snake_case ,						decoder_start_token_id=self.decoder_start_token_id ,						num_beams=self.eval_beams ,						max_length=self.eval_max_length ,						)
							_snake_case       :					Union[str, Any]				       =   (time.time() - ta) / batch["input_ids"].shape[0]
							_snake_case       :					Optional[int]				       =   self.ids_to_clean_text(__snake_case				)
							_snake_case       :					Optional[int]				       =   self.ids_to_clean_text(batch["labels"]				)
							_snake_case       :					Dict				       =   self._step(__snake_case				)
							_snake_case       :					Any				       =   dict(zip(self.loss_names ,						__snake_case				)				)
							_snake_case       :					Dict				       =   self.calc_generative_metrics(__snake_case ,						__snake_case				)
							_snake_case       :					Union[str, Any]				       =   np.mean(lmap(__snake_case ,						__snake_case				)				)
							base_metrics.update(gen_time=__snake_case ,						gen_len=__snake_case ,						preds=__snake_case ,						target=__snake_case ,						**__snake_case				)
							return base_metrics







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							return self._generative_step(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							return self.validation_epoch_end(__snake_case ,						prefix="test"				)







		def        lowercase       (    self ,						__snake_case				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					str				       =   self.n_obs[type_path]
							_snake_case       :					Tuple				       =   self.target_lens[type_path]
							_snake_case       :					List[Any]				       =   self.dataset_class(
							    self.tokenizer ,						type_path=__snake_case ,						n_obs=__snake_case ,						max_target_length=__snake_case ,						**self.dataset_kwargs ,						)
							return dataset







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case = False				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					int				       =   self.get_dataset(__snake_case				)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
												_snake_case       :					Optional[Any]				       =   dataset.make_sortish_sampler(__snake_case ,						distributed=self.hparams.gpus > 1				)
												return DataLoader(
												    __snake_case ,						batch_size=__snake_case ,						collate_fn=dataset.collate_fn ,						shuffle=__snake_case ,						num_workers=self.num_workers ,						sampler=__snake_case ,						)

							elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
												_snake_case       :					str				       =   dataset.make_dynamic_sampler(
												    self.hparams.max_tokens_per_batch ,						distributed=self.hparams.gpus > 1				)
												return DataLoader(
												    __snake_case ,						batch_sampler=__snake_case ,						collate_fn=dataset.collate_fn ,						num_workers=self.num_workers ,						)
							else:
												return DataLoader(
												    __snake_case ,						batch_size=__snake_case ,						collate_fn=dataset.collate_fn ,						shuffle=__snake_case ,						num_workers=self.num_workers ,						sampler=__snake_case ,						)







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   self.get_dataloader("train" ,						batch_size=self.hparams.train_batch_size ,						shuffle=__snake_case				)
							return dataloader







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.get_dataloader("val" ,						batch_size=self.hparams.eval_batch_size				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							return self.get_dataloader("test" ,						batch_size=self.hparams.eval_batch_size				)







		@staticmethod
		def        lowercase       (    __snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							BaseTransformer.add_model_specific_args(__snake_case ,						__snake_case				)
							add_generic_args(__snake_case ,						__snake_case				)
							parser.add_argument(
							    "--max_source_length" ,						default=1_0_2_4 ,						type=__snake_case ,						help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    ) ,						)
							parser.add_argument(
							    "--max_target_length" ,						default=5_6 ,						type=__snake_case ,						help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    ) ,						)
							parser.add_argument(
							    "--val_max_target_length" ,						default=1_4_2 ,						type=__snake_case ,						help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    ) ,						)
							parser.add_argument(
							    "--test_max_target_length" ,						default=1_4_2 ,						type=__snake_case ,						help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    ) ,						)
							parser.add_argument("--freeze_encoder" ,						action="store_true"				)
							parser.add_argument("--freeze_embeds" ,						action="store_true"				)
							parser.add_argument("--sortish_sampler" ,						action="store_true" ,						default=__snake_case				)
							parser.add_argument("--overwrite_output_dir" ,						action="store_true" ,						default=__snake_case				)
							parser.add_argument("--max_tokens_per_batch" ,						type=__snake_case ,						default=__snake_case				)
							parser.add_argument("--logger_name" ,						type=__snake_case ,						choices=["default", "wandb", "wandb_shared"] ,						default="default"				)
							parser.add_argument("--n_train" ,						type=__snake_case ,						default=-1 ,						required=__snake_case ,						help="# examples. -1 means use all."				)
							parser.add_argument("--n_val" ,						type=__snake_case ,						default=5_0_0 ,						required=__snake_case ,						help="# examples. -1 means use all."				)
							parser.add_argument("--n_test" ,						type=__snake_case ,						default=-1 ,						required=__snake_case ,						help="# examples. -1 means use all."				)
							parser.add_argument(
							    "--task" ,						type=__snake_case ,						default="summarization" ,						required=__snake_case ,						help="# examples. -1 means use all."				)
							parser.add_argument("--label_smoothing" ,						type=__snake_case ,						default=0.0 ,						required=__snake_case				)
							parser.add_argument("--src_lang" ,						type=__snake_case ,						default="" ,						required=__snake_case				)
							parser.add_argument("--tgt_lang" ,						type=__snake_case ,						default="" ,						required=__snake_case				)
							parser.add_argument("--eval_beams" ,						type=__snake_case ,						default=__snake_case ,						required=__snake_case				)
							parser.add_argument(
							    "--val_metric" ,						type=__snake_case ,						default=__snake_case ,						required=__snake_case ,						choices=["bleu", "rouge2", "loss", None]				)
							parser.add_argument("--eval_max_gen_length" ,						type=__snake_case ,						default=__snake_case ,						help="never generate more than n tokens"				)
							parser.add_argument("--save_top_k" ,						type=__snake_case ,						default=1 ,						required=__snake_case ,						help="How many checkpoints to save"				)
							parser.add_argument(
							    "--early_stopping_patience" ,						type=__snake_case ,						default=-1 ,						required=__snake_case ,						help=(
							        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
							        " val_check_interval will effect it."
							    ) ,						)
							return parser






class        _a(    __A    ):
		lowerCamelCase__     :Tuple   				=		"""translation"""
		lowerCamelCase__     :Tuple   				=		["""loss"""]
		lowerCamelCase__     :Tuple   				=		["""bleu"""]
		lowerCamelCase__     :Optional[int]   				=		"""bleu"""







		def __init__(    self ,						__snake_case ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							super().__init__(__snake_case ,						**__snake_case				)
							_snake_case       :					Optional[int]				       =   hparams.src_lang
							_snake_case       :					Dict				       =   hparams.tgt_lang







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Optional[int]:


							'''simple docstring'''

							return calculate_bleu(__snake_case ,						__snake_case				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase=None				):
					Path(args.output_dir				).mkdir(exist_ok=_UpperCamelCase				)
					check_output_dir(_UpperCamelCase  ,  expected_items=3				)

					if model is None:
										if "summarization" in args.task:
															_snake_case       :					Optional[Any]				       =   SummarizationModule(_UpperCamelCase				)
										else:
															_snake_case       :					str				       =   TranslationModule(_UpperCamelCase				)
					_snake_case       :					str				       =   Path(args.data_dir				).name
					if (
					    args.logger_name == "default"
					    or args.fast_dev_run
					    or str(args.output_dir				).startswith("/tmp"				)
					    or str(args.output_dir				).startswith("/var"				)
					):
										_snake_case       :					Optional[Any]				       =   True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										_snake_case       :					Union[str, Any]				       =   os.environ.get("WANDB_PROJECT"  ,  _UpperCamelCase				)
										_snake_case       :					Union[str, Any]				       =   WandbLogger(name=model.output_dir.name  ,  project=_UpperCamelCase				)

					elif args.logger_name == "wandb_shared":
										from pytorch_lightning.loggers import WandbLogger

										_snake_case       :					int				       =   WandbLogger(name=model.output_dir.name  ,  project=F"""hf_{dataset}"""				)

					if args.early_stopping_patience >= 0:
										_snake_case       :					Optional[Any]				       =   get_early_stopping_callback(model.val_metric  ,  args.early_stopping_patience				)
					else:
										_snake_case       :					int				       =   False

					_snake_case       :					int				       =   args.val_metric == "loss"
					_snake_case       :					List[str]				       =   generic_train(
					    _UpperCamelCase  ,  _UpperCamelCase  ,  logging_callback=SeqaSeqLoggingCallback()  ,  checkpoint_callback=get_checkpoint_callback(
					        args.output_dir  ,  model.val_metric  ,  args.save_top_k  ,  _UpperCamelCase				)  ,  early_stopping_callback=_UpperCamelCase  ,  logger=_UpperCamelCase  ,  )
					pickle_save(model.hparams  ,  model.output_dir / "hparams.pkl"				)
					if not args.do_predict:
										return model

					_snake_case       :					int				       =   ""
					_snake_case       :					Optional[int]				       =   sorted(glob.glob(os.path.join(args.output_dir  ,  "*.ckpt"				)  ,  recursive=_UpperCamelCase				)				)
					if checkpoints:
										_snake_case       :					Tuple				       =   checkpoints[-1]
										_snake_case       :					Optional[Any]				       =   checkpoints[-1]
					trainer.logger.log_hyperparams(model.hparams				)

					# test() without a model tests using the best checkpoint automatically
					trainer.test()
					return model


if __name__ == "__main__":
							__lowerCAmelCase :List[str]     =    argparse.ArgumentParser()
							__lowerCAmelCase :int     =    pl.Trainer.add_argparse_args(parser)
							__lowerCAmelCase :Optional[Any]     =    SummarizationModule.add_model_specific_args(parser, os.getcwd())

							__lowerCAmelCase :List[str]     =    parser.parse_args()

							main(args)






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    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					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": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					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()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   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    A				(							):
					_snake_case       :					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": 65_536,
					    "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       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   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()				)}"""				)

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