





from __future__ import annotations

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

from matplotlib import pyplot as plt

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				_snake_case :			Tuple  						=   (low + high) // 2
				_snake_case						,			_snake_case						,			_snake_case :			Tuple  						=   max_subarray(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case						,			_snake_case						,			_snake_case :			Union[str, Any]  						=   max_subarray(__lowerCAmelCase	,			mid + 1	,			__lowerCAmelCase )
				_snake_case						,			_snake_case						,			_snake_case :			List[Any]  						=   max_cross_sum(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )
				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__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case						,			_snake_case :			Dict  						=   float('-inf' ), -1
				_snake_case						,			_snake_case :			Optional[Any]  						=   float('-inf' ), -1

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

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

				return max_left, max_right, (left_sum + right_sum)

def       A__(		__lowerCAmelCase ):
				_snake_case :			Tuple  						=   [randint(1	,			__lowerCAmelCase ) for _ in range(__lowerCAmelCase )]
				_snake_case :			str  						=   time.time()
				max_subarray(__lowerCAmelCase	,			0	,			input_size - 1 )
				_snake_case :			Union[str, Any]  						=   time.time()
				return end - start

def       A__(		):
				_snake_case :			Dict  						=   [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]
				_snake_case :			Tuple  						=   [time_max_subarray(__lowerCAmelCase ) for input_size in input_sizes]
				print('No of Inputs\t\tTime Taken' )
				for input_size, runtime in zip(__lowerCAmelCase	,			__lowerCAmelCase ):
								print(__lowerCAmelCase	,			'\t\t'	,			__lowerCAmelCase )
				plt.plot(__lowerCAmelCase	,			__lowerCAmelCase )
				plt.xlabel('Number of Inputs' )
				plt.ylabel('Time taken in seconds' )
				plt.show()


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

		testmod()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






