





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


SCREAMING_SNAKE_CASE_      :			List[str]		  =      get_logger(__name__)



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Dict:
		if text.startswith(snake_case       ):
				return text[len(snake_case       ) :]
		return text  # or whatever



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Any:
		return {remove_prefix(snake_case	,      'model.'       ): v for k, v in sd.items()}



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Optional[int]:
		__lowercase					=    {}
		for k in state_dicts[0].keys():
				__lowercase					=    [sd[k] for sd in state_dicts]
				__lowercase					=    sum(snake_case       ) / len(snake_case       )
				assert isinstance(snake_case	,      torch.Tensor       )
				__lowercase					=    new_t
		return new_sd



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						None:
		__lowercase					=    AutoModelForSeqaSeqLM.from_pretrained(snake_case       )
		if os.path.isfile(snake_case       ):
				__lowercase					=    [pl_ckpt_path]
		else:
				assert os.path.isdir(snake_case       )
				__lowercase					=    list(Path(snake_case       ).glob('*.ckpt'       )       )
				assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

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

		__lowercase					=    [sanitize(torch.load(snake_case	,      map_location='cpu'       )['state_dict']       ) for x in ckpt_files]
		__lowercase					=    average_state_dicts(snake_case       )

		__lowercase	,			__lowercase					=    hf_model.load_state_dict(snake_case	,      strict=snake_case       )
		assert not missing, F"missing keys: {missing}"
		hf_model.save_pretrained(snake_case       )
		try:
				__lowercase					=    AutoTokenizer.from_pretrained(snake_case       )
				tok.save_pretrained(snake_case       )
		except Exception:
				pass
				# dont copy tokenizer if cant


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











def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    str(snake_case       ).strip()
		if not bin_string:
				raise ValueError('Empty string was passed to the function'       )
		__lowercase					=    bin_string[0] == '-'
		if is_negative:
				__lowercase					=    bin_string[1:]
		if not all(char in '01' for char in bin_string       ):
				raise ValueError('Non-binary value was passed to the function'       )
		__lowercase					=    0
		for char in bin_string:
				__lowercase					=    2 * decimal_number + int(snake_case       )
		return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()




