



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


snake_case     :    Optional[Any]						=    get_logger(__name__)
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

						if text.startswith(__lowercase							):
												return text[len(__lowercase							) :]
						return text  # or whatever
def 						snake_case__							(       __lowercase							)		->		Dict:







						"""simple docstring"""

						return {remove_prefix(__lowercase				,  "model."							): v for k, v in sd.items()}
def 						snake_case__							(       __lowercase							)		->		Optional[int]:







						"""simple docstring"""

						A__						:				Dict         =			{}
						for k in state_dicts[0].keys():
												A__						:				int         =			[sd[k] for sd in state_dicts]
												A__						:				List[str]         =			sum(__lowercase							) / len(__lowercase							)
												assert isinstance(__lowercase				,  torch.Tensor							)
												A__						:				int         =			new_t
						return new_sd
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		None:







						"""simple docstring"""

						A__						:				str         =			AutoModelForSeqaSeqLM.from_pretrained(__lowercase							)
						if os.path.isfile(__lowercase							):
												A__						:				Any         =			[pl_ckpt_path]
						else:
												assert os.path.isdir(__lowercase							)
												A__						:				Optional[int]         =			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}'							)

						A__						:				Union[str, Any]         =			[sanitize(torch.load(__lowercase				,  map_location="cpu"							)["state_dict"]							) for x in ckpt_files]
						A__						:				Union[str, Any]         =			average_state_dicts(__lowercase							)

						A__     ,							A__						:				List[str]         =			hf_model.load_state_dict(__lowercase				,  strict=__lowercase							)
						assert not missing, F'missing keys: {missing}'
						hf_model.save_pretrained(__lowercase							)
						try:
												A__						:				Tuple         =			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)


'''simple docstring'''

from __future__ import annotations







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					value
					snake_case							=					None
					snake_case							=					None







class lowerCamelCase   :
				snake_case_							     = 42

				def __init__(       self )    ->							None:
					snake_case							=					0

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							None:
					if node is None:
						return

					if path_sum == self.target:
						self.paths += 1

					if node.left:
						self.depth_first_search(node.left,						path_sum + node.left.value )
					if node.right:
						self.depth_first_search(node.right,						path_sum + node.right.value )






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							int:
					if node is None:
						return 0
					if target is not None:
						snake_case							=					target

					self.depth_first_search(lowercase_,						node.value )
					self.path_sum(node.left )
					self.path_sum(node.right )

					return self.paths


if __name__ == "__main__":
						import doctest

						doctest.testmod()





