

'''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


lowerCAmelCase_     =      get_logger(__name__)

def      __magic_name__							(			A       , A				)      ->		Optional[Any]:
	if text.startswith(A				):
		return text[len(A				) :]
	return text  # or whatever

def      __magic_name__							(			A				)      ->		Dict:
	return {remove_prefix(A       , 'model.'				): v for k, v in sd.items()}

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

def      __magic_name__							(			A       , A       , A				)      ->		None:
	snake_case							=					AutoModelForSeqaSeqLM.from_pretrained(A				)
	if os.path.isfile(A				):
		snake_case							=					[pl_ckpt_path]
	else:
		assert os.path.isdir(A				)
		snake_case							=					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							=					[sanitize(torch.load(A       , map_location='cpu'				)['state_dict']				) for x in ckpt_files]
	snake_case							=					average_state_dicts(A				)

	snake_case			,			snake_case							=					hf_model.load_state_dict(A       , strict=A				)
	assert not missing, F'''missing keys: {missing}'''
	hf_model.save_pretrained(A				)
	try:
		snake_case							=					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







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 0    ):
            __snake_case						       =   value
            __snake_case						       =   None
            __snake_case						       =   None





def        __lowercase(							__snake_case				:		Node | None    ,__snake_case				:		Node | None					)       ->   Node | None:
       if treea is None:
              return treea
       if treea is None:
              return treea

       __snake_case						       =   treea.value + treea.value
       __snake_case						       =   merge_two_binary_trees(treea.left    ,treea.left					)
       __snake_case						       =   merge_two_binary_trees(treea.right    ,treea.right					)
       return treea
def        __lowercase(							__snake_case				:		Node | None					)       ->   None:
       if root:
              print(root.value					)
              print_preorder(root.left					)
              print_preorder(root.right					)


if __name__ == "__main__":
 lowerCamelCase_				:							int       =    Node(1)
 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							Tuple       =    Node(3)
 lowerCamelCase_				:							int       =    Node(4)

 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							List[Any]       =    Node(4)
 lowerCamelCase_				:							List[Any]       =    Node(6)
 lowerCamelCase_				:							Optional[Any]       =    Node(9)
 lowerCamelCase_				:							List[str]       =    Node(5)

 print("Tree1 is: ")
 print_preorder(treea)
 print("Tree2 is: ")
 print_preorder(treea)
 lowerCamelCase_				:							int       =    merge_two_binary_trees(treea, treea)
 print("Merged Tree is: ")
 print_preorder(merged_tree)
