

from __future__ import annotations

from typing import Generic, TypeVar

__snake_case							:     int 						=	TypeVar("""T""")



class    A__(Generic[T]    ):
						"""simple docstring"""






						def __init__(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		Union[str, Any]				    =				data  # Assign data
									a_		:		Node[T] | None				    =				None  # Initialize next as null
									a_		:		Node[T] | None				    =				None  # Initialize prev as null







class    A__(Generic[T]    ):
						"""simple docstring"""






						def __init__(  self  )      ->				None:
									a_		:		Node[T] | None				    =				None





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				None:
									if self.head is None:
												a_		:		List[str]				    =				Node(_lowercase  )
									else:
												a_		:		List[str]				    =				Node(_lowercase  )
												a_		:		List[Any]				    =				new_node
												a_		:		Dict				    =				self.head
												a_		:		Optional[int]				    =				None
												a_		:		List[Any]				    =				new_node





						def 	UpperCamelCase__			(  self  )      ->				T | None:
									if self.head is None:
												return None
									else:
												assert self.head is not None
												a_		:		Tuple				    =				self.head.data
												a_		:		Any				    =				self.head.next
												if self.head is not None:
															a_		:		List[str]				    =				None
												return temp





						def 	UpperCamelCase__			(  self  )      ->				T | None:
									return self.head.data if self.head is not None else None





						def __len__(  self  )      ->				int:
									a_		:		Optional[Any]				    =				self.head
									a_		:		str				    =				0
									while temp is not None:
												count += 1
												a_		:		Dict				    =				temp.next
									return count





						def 	UpperCamelCase__			(  self  )      ->				bool:
									return self.head is None





						def 	UpperCamelCase__			(  self  )      ->				None:
									print("""stack elements are:"""  )
									a_		:		str				    =				self.head
									while temp is not None:
												print(temp.data       ,     end="""->"""  )
												a_		:		str				    =				temp.next


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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


