from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass




@dataclass
class       __lowerCAmelCase     :



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		TreeNode | None 			=       None
 _lowerCamelCase  :		TreeNode | None 			=       None







def SCREAMING_SNAKE_CASE (   ):
       return TreeNode(3						,  TreeNode(9 )						,  TreeNode(20						,  TreeNode(15 )						,  TreeNode(7 ) ) )


def SCREAMING_SNAKE_CASE (   lowercase_:		TreeNode ):

       def depth_first_search(
           lowercase_:		TreeNode | None						,  lowercase_:		int						,  lowercase_:		list[int] ) -> None:
              if not root:
                     return

              if depth == len(lowercase_ ):
                     right_view.append(root.val )

              depth_first_search(root.right						,  depth + 1						,  lowercase_ )
              depth_first_search(root.left						,  depth + 1						,  lowercase_ )

       _UpperCamelCase   :				list       =     []
       if not root:
              return right_view

       depth_first_search(lowercase_						,  0						,  lowercase_ )
       return right_view


def SCREAMING_SNAKE_CASE (   lowercase_:		TreeNode ):

       def depth_first_search(
           lowercase_:		TreeNode | None						,  lowercase_:		int						,  lowercase_:		list[int] ) -> None:
              if not root:
                     return

              if depth == len(lowercase_ ):
                     left_view.append(root.val )

              depth_first_search(root.left						,  depth + 1						,  lowercase_ )
              depth_first_search(root.right						,  depth + 1						,  lowercase_ )

       _UpperCamelCase   :				list       =     []
       if not root:
              return left_view

       depth_first_search(lowercase_						,  0						,  lowercase_ )
       return left_view


def SCREAMING_SNAKE_CASE (   lowercase_:		TreeNode ):

       def breadth_first_search(lowercase_:		TreeNode						,  lowercase_:		list[int] ) -> None:
              _UpperCamelCase   :				Optional[int]       =     [(root, 0)]
              _UpperCamelCase   :				str       =     defaultdict(lowercase_ )

              while queue:
                     _UpperCamelCase   :				Optional[Any]       =     queue.pop(0 )
                     _UpperCamelCase  ,					_UpperCamelCase   :				List[Any]       =     first

                     lookup[hd].append(node.val )

                     if node.left:
                            queue.append((node.left, hd - 1) )
                     if node.right:
                            queue.append((node.right, hd + 1) )

              for pair in sorted(lookup.items()						,  key=lambda lowercase_ : each[0] ):
                     top_view.append(pair[1][0] )

       _UpperCamelCase   :				list       =     []
       if not root:
              return top_view

       breadth_first_search(lowercase_						,  lowercase_ )
       return top_view


def SCREAMING_SNAKE_CASE (   lowercase_:		TreeNode ):
       from collections import defaultdict

       def breadth_first_search(lowercase_:		TreeNode						,  lowercase_:		list[int] ) -> None:
              _UpperCamelCase   :				List[str]       =     [(root, 0)]
              _UpperCamelCase   :				Tuple       =     defaultdict(lowercase_ )

              while queue:
                     _UpperCamelCase   :				Union[str, Any]       =     queue.pop(0 )
                     _UpperCamelCase  ,					_UpperCamelCase   :				Optional[int]       =     first
                     lookup[hd].append(node.val )

                     if node.left:
                            queue.append((node.left, hd - 1) )
                     if node.right:
                            queue.append((node.right, hd + 1) )

              for pair in sorted(lookup.items()						,  key=lambda lowercase_ : each[0] ):
                     bottom_view.append(pair[1][-1] )

       _UpperCamelCase   :				list       =     []
       if not root:
              return bottom_view

       breadth_first_search(lowercase_						,  lowercase_ )
       return bottom_view


if __name__ == "__main__":
 import doctest

 doctest.testmod()




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 SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     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}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     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)



