





'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					=    {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''ViTFeatureExtractor''']
	_SCREAMING_SNAKE_CASE					=    ['''ViTImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ViTForImageClassification''',
	    '''ViTForMaskedImageModeling''',
	    '''ViTModel''',
	    '''ViTPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TFViTForImageClassification''',
	    '''TFViTModel''',
	    '''TFViTPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxViTForImageClassification''',
	    '''FlaxViTModel''',
	    '''FlaxViTPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_vit import ViTFeatureExtractor
		from .image_processing_vit import ViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_vit import (
		    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ViTForImageClassification,
		    ViTForMaskedImageModeling,
		    ViTModel,
		    ViTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''



from __future__ import annotations

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

from matplotlib import pyplot as plt




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

			__lowercase										=							(low + high) // 2
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       mid + 1		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							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    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1

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

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

			return max_left, max_right, (left_sum + right_sum)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							[randint(1		,       lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
			__lowercase										=							time.time()
			max_subarray(lowerCamelCase_		,       0		,       input_size - 1				)
			__lowercase										=							time.time()
			return end - start




def    _lowerCAmelCase						(				):
			__lowercase										=							[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]
			__lowercase										=							[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()





