from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase				 =   TypeVar("""T""")






class 				_SCREAMING_SNAKE_CASE( Generic[T]						):
   def __init__(     self				,  lowerCAmelCase_     )						->Optional[Any]:
        a	:     List[Any]						      =    data  # Assign data
        a	:     Node[T] | None						      =    None  # Initialize next as null
        a	:     Node[T] | None						      =    None  # Initialize prev as null






class 				_SCREAMING_SNAKE_CASE( Generic[T]						):
   def __init__(     self     )						->None:
        a	:     Node[T] | None						      =    None
   def _lowercase(     self				,  lowerCAmelCase_     )						->None:
        if self.head is None:
             a	:     int						      =    Node(lowerCAmelCase_     )
        else:
             a	:     str						      =    Node(lowerCAmelCase_     )
             a	:     Dict						      =    new_node
             a	:     Dict						      =    self.head
             a	:     str						      =    None
             a	:     Tuple						      =    new_node
   def _lowercase(     self     )						->T | None:
        if self.head is None:
             return None
        else:
             assert self.head is not None
             a	:     Dict						      =    self.head.data
             a	:     Optional[Any]						      =    self.head.next
             if self.head is not None:
                  a	:     str						      =    None
             return temp
   def _lowercase(     self     )						->T | None:
        return self.head.data if self.head is not None else None
   def __len__(     self     )						->int:
        a	:     Optional[int]						      =    self.head
        a	:     List[Any]						      =    0
        while temp is not None:
             count += 1
             a	:     List[Any]						      =    temp.next
        return count
   def _lowercase(     self     )						->bool:
        return self.head is None


   def _lowercase(     self     )						->None:
        print('''stack elements are:'''     )
        a	:     Any						      =    self.head
        while temp is not None:
             print(temp.data				,  end='''->'''     )
             a	:     Any						      =    temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  UpperCAmelCase				 =   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())
from typing import TYPE_CHECKING

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


UpperCAmelCase				 =   {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  UpperCAmelCase				 =   [
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)