from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Sequence[float]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> List[str]:







  """simple docstring"""





  if not arr:
    return None, None, 0
  if low == high:
    return low, high, arr[low]

  SCREAMING_SNAKE_CASE__      							=				(low + high) // 2
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				max_subarray(__a			,					__a			,					__a     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				max_subarray(__a			,					mid + 1			,					__a     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				max_cross_sum(__a			,					__a			,					__a			,					__a     )
  if left_sum >= right_sum and left_sum >= cross_sum:
    return left_low, left_high, left_sum
  elif right_sum >= left_sum and right_sum >= cross_sum:
    return right_low, right_high, right_sum
  return cross_left, cross_right, cross_sum



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Sequence[float]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				float("""-inf"""     ), -1
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				float("""-inf"""     ), -1

  SCREAMING_SNAKE_CASE__      							=				0
  for i in range(__a			,					low - 1			,					-1     ):
    summ += arr[i]
    if summ > left_sum:
      SCREAMING_SNAKE_CASE__      							=				summ
      SCREAMING_SNAKE_CASE__      							=				i

  SCREAMING_SNAKE_CASE__      							=				0
  for i in range(mid + 1			,					high + 1     ):
    summ += arr[i]
    if summ > right_sum:
      SCREAMING_SNAKE_CASE__      							=				summ
      SCREAMING_SNAKE_CASE__      							=				i

  return max_left, max_right, (left_sum + right_sum)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[randint(1			,					__a     ) for _ in range(__a     )]
  SCREAMING_SNAKE_CASE__      							=				time.time()
  max_subarray(__a			,					0			,					input_size - 1     )
  SCREAMING_SNAKE_CASE__      							=				time.time()
  return end - start



def      __SCREAMING_SNAKE_CASE  ( )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
  SCREAMING_SNAKE_CASE__      							=				[time_max_subarray(__a     ) for input_size in input_sizes]
  print("""No of Inputs\t\tTime Taken"""     )
  for input_size, runtime in zip(__a			,					__a     ):
    print(__a			,					"""\t\t"""			,					__a     )
  plt.plot(__a			,					__a     )
  plt.xlabel("""Number of Inputs"""     )
  plt.ylabel("""Time taken in seconds"""     )
  plt.show()


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

   testmod()




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
