





def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Union[str, Any]    ,    SCREAMING_SNAKE_CASE		:Tuple    ,    SCREAMING_SNAKE_CASE		:Dict    ,    SCREAMING_SNAKE_CASE		:Tuple    )			->    int:
 if start == end:
  return start if lst[start] > item else start + 1
 if start > end:
  return start

 __lowerCAmelCase     :       List[Any]						=   (start + end) // 2
 if lst[mid] < item:
  return binary_search(lowercase_    ,    lowercase_    ,    mid + 1    ,    lowercase_    )
 elif lst[mid] > item:
  return binary_search(lowercase_    ,    lowercase_    ,    lowercase_    ,    mid - 1    )
 else:
  return mid






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Union[str, Any]    )			->    Any:
 __lowerCAmelCase     :       str						=   len(lowercase_    )

 for index in range(1    ,    lowercase_    ):
  __lowerCAmelCase     :       Optional[int]						=   lst[index]
  __lowerCAmelCase     :       Optional[int]						=   binary_search(lowercase_    ,    lowercase_    ,    0    ,    index - 1    )
  __lowerCAmelCase     :       Any						=   lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

 return lst






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:List[Any]    ,    SCREAMING_SNAKE_CASE		:Tuple    )			->    str:
 if not left:
  return right

 if not right:
  return left

 if left[0] < right[0]:
  return [left[0], *merge(left[1:]    ,    lowercase_    )]

 return [right[0], *merge(lowercase_    ,    right[1:]    )]






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Tuple    )			->    Optional[Any]:
 __lowerCAmelCase     :       List[Any]						=   len(lowercase_    )
 __lowerCAmelCase     :       Tuple						=   [], []
 __lowerCAmelCase     :       Dict						=   [lst[0]]
 __lowerCAmelCase     :       List[str]						=   []
 __lowerCAmelCase     :       Tuple						=   1
 while i < length:
  if lst[i] < lst[i - 1]:
   runs.append(lowercase_    )
   __lowerCAmelCase     :       Union[str, Any]						=   [lst[i]]
  else:
   new_run.append(lst[i]    )
  i += 1
 runs.append(lowercase_    )

 for run in runs:
  sorted_runs.append(insertion_sort(lowercase_    )    )
 for run in sorted_runs:
  __lowerCAmelCase     :       Union[str, Any]						=   merge(lowercase_    ,    lowercase_    )

 return sorted_array






def        _SCREAMING_SNAKE_CASE		(			)			->    Union[str, Any]:
 __lowerCAmelCase     :       List[str]						=   [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
 __lowerCAmelCase     :       Dict						=   tim_sort(lowercase_    )
 print(lowercase_    )


if __name__ == "__main__":
      main()
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)



