

'''simple docstring'''



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_    =   get_logger(__name__)

def   lowerCamelCase__       (      a__			,      a__)		->    Tuple:



				"""simple docstring"""







				if text.startswith(a__):
								return text[len(a__) :]
				return text  # or whatever

def   lowerCamelCase__       (      a__)		->    Tuple:



				"""simple docstring"""







				return {remove_prefix(a__			,      'model.'): v for k, v in sd.items()}

def   lowerCamelCase__       (      a__)		->    Optional[int]:



				"""simple docstring"""







				_snake_case : Tuple												=		{}
				for k in state_dicts[0].keys():
								_snake_case : Any												=		[sd[k] for sd in state_dicts]
								_snake_case : Union[str, Any]												=		sum(a__) / len(a__)
								assert isinstance(a__			,      torch.Tensor)
								_snake_case : Union[str, Any]												=		new_t
				return new_sd

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    None:



				"""simple docstring"""







				_snake_case : List[str]												=		AutoModelForSeqaSeqLM.from_pretrained(a__)
				if os.path.isfile(a__):
								_snake_case : Any												=		[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 : List[str]												=		[sanitize(torch.load(a__			,      map_location='cpu')['state_dict']) for x in ckpt_files]
				_snake_case : Optional[int]												=		average_state_dicts(a__)

				_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 : Tuple												=		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)






'''simple docstring'''



def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



