'''simple docstring'''
from __future__ import annotations

from typing import Generic, TypeVar

_A		:      Tuple						   =TypeVar('''T''')




class _lowerCAmelCase			(	Generic[T]					):







      def __init__(  self  ,    lowercase     ):
        lowercase				:							Optional[Any]									=   data  # Assign data
        lowercase				:							Node[T] | None									=   None  # Initialize next as null
        lowercase				:							Node[T] | None									=   None  # Initialize prev as null





class _lowerCAmelCase			(	Generic[T]					):







      def __init__(  self     ):
        lowercase				:							Node[T] | None									=   None







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        if self.head is None:
          lowercase				:							str									=   Node(lowercase     )
        else:
          lowercase				:							Optional[Any]									=   Node(lowercase     )
          lowercase				:							List[Any]									=   new_node
          lowercase				:							Dict									=   self.head
          lowercase				:							Any									=   None
          lowercase				:							Optional[int]									=   new_node







      def      lowerCamelCase__      (  self     ):
        if self.head is None:
          return None
        else:
          assert self.head is not None
          lowercase				:							Any									=   self.head.data
          lowercase				:							Optional[int]									=   self.head.next
          if self.head is not None:
            lowercase				:							int									=   None
          return temp







      def      lowerCamelCase__      (  self     ):
        return self.head.data if self.head is not None else None







      def __len__(  self     ):
        lowercase				:							List[Any]									=   self.head
        lowercase				:							Dict									=   0
        while temp is not None:
          count += 1
          lowercase				:							str									=   temp.next
        return count







      def      lowerCamelCase__      (  self     ):
        return self.head is None







      def      lowerCamelCase__      (  self     ):
        print('''stack elements are:'''     )
        lowercase				:							Optional[Any]									=   self.head
        while temp is not None:
          print(temp.data  ,    end='''->'''     )
          lowercase				:							Any									=   temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 _A		:      Stack[int]						   =Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())
'''simple docstring'''
from typing import TYPE_CHECKING

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


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)