code: 
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Tuple:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			len(UpperCAmelCase_			)
         # Create a stack and push index of fist element to it
         _lowercase					:				int           =			[]
         st.append(0			)
  
         # Span value of first element is always 1
         _lowercase					:				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
                _lowercase					:				Optional[int]           =			i + 1 if len(UpperCAmelCase_			) <= 0 else (i - st[0])
  
                # Push this element to stack
                st.append(UpperCAmelCase_			)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         for i in range(0    ,     UpperCAmelCase_			):
                print(arr[i]    ,     end=''' '''			)
  
  
  # Driver program to test above function
  UpperCamelCase__				       =			[10, 4, 5, 90, 120, 80]
  UpperCamelCase__				       =			[0 for i in range(len(price) + 1)]
  
  # Fill the span values in array S[]
  calculation_span(price, S)
  
  
  
  
  
  
  
  print_array(S, len(price))
style_context: 
  
  
  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   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if text.startswith(UpperCAmelCase_			):
                return text[len(UpperCAmelCase_			) :]
         return text  # or whatever
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			{}
         for k in state_dicts[0].keys():
                _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
                _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
                assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
                _lowercase					:				Union[str, Any]           =			new_t
         return new_sd
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
         else:
                assert os.path.isdir(UpperCAmelCase_			)
                _lowercase					:				List[str]           =			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}'			)
  
         _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
         _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
         assert not missing, F'missing keys: {missing}'
         hf_model.save_pretrained(UpperCAmelCase_			)
         try:
                _lowercase					:				Union[str, 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)