



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


UpperCamelCase__			=    get_logger(__name__)






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   Union[str, Any]:
						if text.startswith(lowercase__ ):
												return text[len(lowercase__ ) :]
						return text  # or whatever






def 			UpperCAmelCase__     (     lowercase__ )			->   int:
						return {remove_prefix(lowercase__    ,							"""model.""" ): v for k, v in sd.items()}






def 			UpperCAmelCase__     (     lowercase__ )			->   Union[str, Any]:
						__lowercase											=					{}
						for k in state_dicts[0].keys():
												__lowercase											=					[sd[k] for sd in state_dicts]
												__lowercase											=					sum(lowercase__ ) / len(lowercase__ )
												assert isinstance(lowercase__    ,							torch.Tensor )
												__lowercase											=					new_t
						return new_sd






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

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

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

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


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










from .stack import Stack






def 			UpperCAmelCase__     (     lowercase__ )			->   bool:
						__lowercase											=					Stack()
						__lowercase											=					{"""(""": """)""", """[""": """]""", """{""": """}"""}
						for bracket in parentheses:
												if bracket in bracket_pairs:
																		stack.push(lowercase__ )
												elif bracket in (")", "]", "}"):
																		if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																								return False
						return stack.is_empty()


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

			testmod()

			UpperCamelCase__			=    ["((()))", "((())", "(()))"]
			print("Balanced parentheses demonstration:\n")
			for example in examples:
						UpperCamelCase__			=    "" if balanced_parentheses(example) else "not "
						print(F"""{example} is {not_str}balanced""")





