

from __future__ import annotations

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

from matplotlib import pyplot as plt

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



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

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (low + high) // 2
      SCREAMING_SNAKE_CASE_				:  List[str]           =     max_subarray(a					,   a					,   a  )
      SCREAMING_SNAKE_CASE_				:  Any           =     max_subarray(a					,   mid + 1					,   a  )
      SCREAMING_SNAKE_CASE_				:  int           =     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    A_  (							a					,   a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     float('-inf'  ), -1
      SCREAMING_SNAKE_CASE_				:  Any           =     float('-inf'  ), -1

      SCREAMING_SNAKE_CASE_				:  int | float           =     0
      for i in range(a					,   low - 1					,   -1  ):
            summ += arr[i]
            if summ > left_sum:
                  SCREAMING_SNAKE_CASE_				:  int           =     summ
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     i

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

      return max_left, max_right, (left_sum + right_sum)

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     [randint(1					,   a  ) for _ in range(a  )]
      SCREAMING_SNAKE_CASE_				:  Any           =     time.time()
      max_subarray(a					,   0					,   input_size - 1  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     time.time()
      return end - start

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [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, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
    import torch

    from transformers import AutoModelForImageClassification

if is_vision_available():
    from transformers import AutoImageProcessor
@require_torch
@require_vision
class  _A		(  unittest.TestCase):

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        model.to(_SCREAMING_SNAKE_CASE					)

        from datasets import load_dataset

        SCREAMING_SNAKE_CASE_				:  str           =     load_dataset('nielsr/rvlcdip-demo'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dataset['train'][0]['image'].convert('RGB'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image_processor(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_				:  int           =     model(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     outputs.logits

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.Size((1, 16)					)
        self.assertEqual(logits.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor(
            [-0.4158, -0.4092, -0.4347] ,					device=_SCREAMING_SNAKE_CASE ,					dtype=torch.float ,					)
        self.assertTrue(torch.allclose(logits[0, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)






