






import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowercase     :							List[str]	     =			get_logger(__name__)

def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	str			):
		if text.startswith(_a			):
				return text[len(_a			) :]
		return text  # or whatever

def    lowerCAmelCase__		(			_a				:	List[str]			):
		return {remove_prefix(_a     ,					"model."			): v for k, v in sd.items()}

def    lowerCAmelCase__		(			_a				:	List[Dict[str, torch.Tensor]]			):
		snake_case_ :							List[Any]         =				{}
		for k in state_dicts[0].keys():
				snake_case_ :							List[str]         =				[sd[k] for sd in state_dicts]
				snake_case_ :							List[str]         =				sum(_a			) / len(_a			)
				assert isinstance(_a     ,					torch.Tensor			)
				snake_case_ :							Union[str, Any]         =				new_t
		return new_sd

def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	str     ,					_a				:	str			):
		snake_case_ :							Optional[int]         =				AutoModelForSeqaSeqLM.from_pretrained(_a			)
		if os.path.isfile(_a			):
				snake_case_ :							Tuple         =				[pl_ckpt_path]
		else:
				assert os.path.isdir(_a			)
				snake_case_ :							List[Any]         =				list(Path(_a			).glob("*.ckpt"			)			)
				assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

		if len(_a			) > 1:
				logger.info(F'''averaging the weights of {ckpt_files}'''			)

		snake_case_ :							Optional[Any]         =				[sanitize(torch.load(_a     ,					map_location="cpu"			)["state_dict"]			) for x in ckpt_files]
		snake_case_ :							str         =				average_state_dicts(_a			)

		snake_case_       ,				snake_case_ :							Optional[Any]         =				hf_model.load_state_dict(_a     ,					strict=_a			)
		assert not missing, F'''missing keys: {missing}'''
		hf_model.save_pretrained(_a			)
		try:
				snake_case_ :							List[str]         =				AutoTokenizer.from_pretrained(_a			)
				tok.save_pretrained(_a			)
		except Exception:
				pass
				# dont copy tokenizer if cant


if __name__ == "__main__":
							fire.Fire(convert_pl_to_hf)








from __future__ import annotations

from collections.abc import Generator

def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			) or n < 1:
				raise Exception("Sequence only defined for positive integers"			)

		yield n
		while n != 1:
				if n % 2 == 0:
						n //= 2
				else:
						snake_case_ :							List[str]         =				3 * n + 1
				yield n

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				43
		snake_case_ :							Union[str, Any]         =				tuple(collatz_sequence(_a			)			)
		print(_a			)
		print(F'''Collatz sequence from {n} took {len(_a			)} steps.'''			)


if __name__ == "__main__":
							main()
