


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))



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)