





"""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_					:						List[str]      = get_logger(__name__)





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if text.startswith(_UpperCAmelCase ):
    return text[len(_UpperCAmelCase ) :]
  return text  # or whatever





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  return {remove_prefix(_UpperCAmelCase			, 'model.' ): v for k, v in sd.items()}





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  {}
  for k in state_dicts[0].keys():
    A_  :	Optional[int]    						=  [sd[k] for sd in state_dicts]
    A_  :	int    						=  sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
    assert isinstance(_UpperCAmelCase			, torch.Tensor )
    A_  :	Optional[Any]    						=  new_t
  return new_sd





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase )
  if os.path.isfile(_UpperCAmelCase ):
    A_  :	Union[str, Any]    						=  [pl_ckpt_path]
  else:
    assert os.path.isdir(_UpperCAmelCase )
    A_  :	Optional[Any]    						=  list(Path(_UpperCAmelCase ).glob('*.ckpt' ) )
    assert ckpt_files, f"""could not find any ckpt files inside the {pl_ckpt_path} directory"""

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

  A_  :	Dict    						=  [sanitize(torch.load(_UpperCAmelCase			, map_location='cpu' )['state_dict'] ) for x in ckpt_files]
  A_  :	Any    						=  average_state_dicts(_UpperCAmelCase )

  A_						,	A_  :	List[str]    						=  hf_model.load_state_dict(_UpperCAmelCase			, strict=_UpperCAmelCase )
  assert not missing, f"""missing keys: {missing}"""
  hf_model.save_pretrained(_UpperCAmelCase )
  try:
    A_  :	Optional[Any]    						=  AutoTokenizer.from_pretrained(_UpperCAmelCase )
    tok.save_pretrained(_UpperCAmelCase )
  except Exception:
    pass
    # dont copy tokenizer if cant


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






"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  len(_UpperCAmelCase )
  # Create a stack and push index of fist element to it
  A_  :	List[Any]    						=  []
  st.append(0 )

  # Span value of first element is always 1
  A_  :	List[str]    						=  1

  # Calculate span values for rest of the elements
  for i in range(1			, _UpperCAmelCase ):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_UpperCAmelCase ) > 0 and price[st[0]] <= price[i]:
      st.pop()

    # If stack becomes empty, then price[i] is greater
    # than all elements on left of it, i.e. price[0],
    # price[1], ..price[i-1]. Else the price[i]  is
    # greater than elements after top of stack
    A_  :	Union[str, Any]    						=  i + 1 if len(_UpperCAmelCase ) <= 0 else (i - st[0])

    # Push this element to stack
    st.append(_UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for i in range(0			, _UpperCAmelCase ):
    print(arr[i]			, end=' ' )


# Driver program to test above function
lowerCamelCase_					:						List[Any]      = [10, 4, 5, 90, 1_20, 80]
lowerCamelCase_					:						Optional[Any]      = [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))