from __future__ import annotations

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

from matplotlib import pyplot as plt





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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

							snake_case_          =   (low + high) // 2
							snake_case_    ,   snake_case_    ,   snake_case_          =   max_subarray(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_    ,   snake_case_    ,   snake_case_          =   max_subarray(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)
							snake_case_    ,   snake_case_    ,   snake_case_          =   max_cross_sum(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_    ,   snake_case_          =   float('-inf'				), -1
							snake_case_    ,   snake_case_          =   float('-inf'				), -1

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

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

							return max_left, max_right, (left_sum + right_sum)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
							snake_case_          =   [time_max_subarray(UpperCamelCase__				) for input_size in input_sizes]
							print('No of Inputs\t\tTime Taken'				)
							for input_size, runtime in zip(UpperCamelCase__       ,							UpperCamelCase__				):
														print(UpperCamelCase__       ,							'\t\t'       ,							UpperCamelCase__				)
							plt.plot(UpperCamelCase__       ,							UpperCamelCase__				)
							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 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											snake_case_          =   AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											model.to(snake_case	)

											from datasets import load_dataset

											snake_case_          =   load_dataset('nielsr/rvlcdip-demo'	)

											snake_case_          =   dataset['train'][0]['image'].convert('RGB'	)

											snake_case_          =   image_processor(snake_case				,       return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																		snake_case_          =   model(**snake_case	)
																		snake_case_          =   outputs.logits

											snake_case_          =   torch.Size((1, 16)	)
											self.assertEqual(logits.shape				,       snake_case	)

											snake_case_          =   torch.tensor(
											    [-0.41_58, -0.40_92, -0.43_47]				,       device=snake_case				,       dtype=torch.float				,       )
											self.assertTrue(torch.allclose(logits[0, :3]				,       snake_case				,       atol=1e-4	)	)




