from typing import TYPE_CHECKING

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


A__						:						str          =      {
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Union[str, Any]          =      ["BartTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Union[str, Any]          =      [
						    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BartForCausalLM",
						    "BartForConditionalGeneration",
						    "BartForQuestionAnswering",
						    "BartForSequenceClassification",
						    "BartModel",
						    "BartPretrainedModel",
						    "PretrainedBartModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[str]          =      [
						    "TFBartForConditionalGeneration",
						    "TFBartForSequenceClassification",
						    "TFBartModel",
						    "TFBartPretrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      [
						    "FlaxBartDecoderPreTrainedModel",
						    "FlaxBartForCausalLM",
						    "FlaxBartForConditionalGeneration",
						    "FlaxBartForQuestionAnswering",
						    "FlaxBartForSequenceClassification",
						    "FlaxBartModel",
						    "FlaxBartPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						A__						:						int          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




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





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




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




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				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())





