





from __future__ import annotations

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

from matplotlib import pyplot as plt

def   a_    ( lowerCAmelCase_      :			Sequence[float],	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int	):
   if not arr:
      return None, None, 0
   if low == high:
      return low, high, arr[low]

   __lowerCAmelCase								=	(low + high) // 2
   __lowerCAmelCase								=	max_subarray(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_	)
   __lowerCAmelCase								=	max_subarray(UpperCAmelCase_,	mid + 1,	UpperCAmelCase_	)
   __lowerCAmelCase								=	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   a_    ( lowerCAmelCase_      :			Sequence[float],	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	float('-inf'	), -1
   __lowerCAmelCase								=	float('-inf'	), -1

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

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

   return max_left, max_right, (left_sum + right_sum)

def   a_    ( lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	[randint(1,	UpperCAmelCase_	) for _ in range(UpperCAmelCase_	)]
   __lowerCAmelCase								=	time.time()
   max_subarray(UpperCAmelCase_,	0,	input_size - 1	)
   __lowerCAmelCase								=	time.time()
   return end - start

def   a_    ( ):
   __lowerCAmelCase								=	[10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
   __lowerCAmelCase								=	[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()





'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






