






from __future__ import annotations

from typing import Generic, TypeVar

A										=      TypeVar('T')


class 				__a				(	Generic[T]    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= data  # Assign data
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= None  # Initialize next as null
						SCREAMING_SNAKE_CASE_ :						Any    				= None  # Initialize prev as null






class 				__a				(	Generic[T]    ):





		'''simple docstring'''



		def __init__(    self							):
						SCREAMING_SNAKE_CASE_ :						str    				= None


		def 	__snake_case      (    self			,     UpperCamelCase__							):



						if self.head is None:
										SCREAMING_SNAKE_CASE_ :						Any    				= Node(a_							)
						else:
										SCREAMING_SNAKE_CASE_ :						Dict    				= Node(a_							)
										SCREAMING_SNAKE_CASE_ :						str    				= new_node
										SCREAMING_SNAKE_CASE_ :						str    				= self.head
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= None
										SCREAMING_SNAKE_CASE_ :						Tuple    				= new_node


		def 	__snake_case      (    self							):



						if self.head is None:
										return None
						else:
										assert self.head is not None
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.head.data
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.head.next
										if self.head is not None:
														SCREAMING_SNAKE_CASE_ :						str    				= None
										return temp


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


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


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






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


# Code execution starts here
if __name__ == "__main__":
				# Start with the empty stack
				A										=      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


__lowercase:		Tuple    						=  {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[str]    						=  ["ImageGPTFeatureExtractor"]
       __lowercase:		Tuple    						=  ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str    						=  [
           "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ImageGPTForCausalImageModeling",
           "ImageGPTForImageClassification",
           "ImageGPTModel",
           "ImageGPTPreTrainedModel",
           "load_tf_weights_in_imagegpt",
       ]


if TYPE_CHECKING:
       from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
              from .image_processing_imagegpt import ImageGPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_imagegpt import (
                  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ImageGPTForCausalImageModeling,
                  ImageGPTForImageClassification,
                  ImageGPTModel,
                  ImageGPTPreTrainedModel,
                  load_tf_weights_in_imagegpt,
              )

else:
       import sys

       __lowercase:		List[str]    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)