






"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase_						:					List[Any]      				=			TypeVar("""T""")


class   _lowercase			(  Generic[T]    ):


      def __init__(     self					:						List[Any]				, a					:						T    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =data  # Assign data
            __snake_case   :     Node[T] | None	      =None  # Initialize next as null
            __snake_case   :     Node[T] | None	      =None  # Initialize prev as null



class   _lowercase			(  Generic[T]    ):


      def __init__(     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     Node[T] | None	      =None


      def _UpperCamelCase    (     self					:						Dict				, a					:						T    ):





            """simple docstring"""







            if self.head is None:
                  __snake_case   :     Tuple	      =Node(a    )
            else:
                  __snake_case   :     str	      =Node(a    )
                  __snake_case   :     List[str]	      =new_node
                  __snake_case   :     List[str]	      =self.head
                  __snake_case   :     Any	      =None
                  __snake_case   :     str	      =new_node


      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            if self.head is None:
                  return None
            else:
                  assert self.head is not None
                  __snake_case   :     Optional[int]	      =self.head.data
                  __snake_case   :     Any	      =self.head.next
                  if self.head is not None:
                        __snake_case   :     Any	      =None
                  return temp


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            return self.head.data if self.head is not None else None


      def __len__(     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =self.head
            __snake_case   :     Optional[int]	      =0
            while temp is not None:
                  count += 1
                  __snake_case   :     Union[str, Any]	      =temp.next
            return count


      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            return self.head is None







      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







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


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    UpperCamelCase_						:					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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

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


