






from __future__ import annotations

UpperCAmelCase__				   =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCAmelCase__				   =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def 						_a    (      a :list[float] )							->			list[float]:
 a		  =      []
 a		  =      len(a )

 for i in range(a ):
  a		  =      -1
  for j in range(i + 1	,							a ):
   if arr[i] < arr[j]:
    a		  =      arr[j]
    break
  result.append(a )
 return result
def 						_a    (      a :list[float] )							->			list[float]:
 a		  =      []
 for i, outer in enumerate(a ):
  a		  =      -1
  for inner in arr[i + 1 :]:
   if outer < inner:
    a		  =      inner
    break
  result.append(a )
 return result
def 						_a    (      a :list[float] )							->			list[float]:
 a		  =      len(a )
 a		  =      []
 a		  =      [-1] * arr_size

 for index in reversed(range(a ) ):
  if stack:
   while stack[-1] <= arr[index]:
    stack.pop()
    if not stack:
     break
  if stack:
   a		  =      stack[-1]
  stack.append(arr[index] )
 return result


if __name__ == "__main__":
     from doctest import testmod
     from timeit import timeit

     testmod()
     print(next_greatest_element_slow(arr))
     print(next_greatest_element_fast(arr))
     print(next_greatest_element(arr))

     UpperCAmelCase__				   =		(
         "from __main__ import arr, next_greatest_element_slow, "
         "next_greatest_element_fast, next_greatest_element"
     )
     print(
         "next_greatest_element_slow():",
         timeit("next_greatest_element_slow(arr)", setup=setup),
     )
     print(
         "next_greatest_element_fast():",
         timeit("next_greatest_element_fast(arr)", setup=setup),
     )
     print(
         "     next_greatest_element():",
         timeit("next_greatest_element(arr)", setup=setup),
     )







"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


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





