

from __future__ import annotations


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		List[Any]				    =				[None] * (len(a__) + len(a__))

			a_		:		str				    =				0  # pointer to current index for left Half
			a_		:		Optional[int]				    =				0  # pointer to current index for the right Half
			a_		:		Any				    =				0  # pointer to current index for the sorted array Half

			while pointera < len(a__) and pointera < len(a__):
						if left_half[pointera] < right_half[pointera]:
									a_		:		Union[str, Any]				    =				left_half[pointera]
									pointera += 1
									index += 1
						else:
									a_		:		Any				    =				right_half[pointera]
									pointera += 1
									index += 1
			while pointera < len(a__):
						a_		:		Any				    =				left_half[pointera]
						pointera += 1
						index += 1

			while pointera < len(a__):
						a_		:		List[Any]				    =				right_half[pointera]
						pointera += 1
						index += 1

			return sorted_array


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if len(a__) <= 1:
						return array
			# the actual formula to calculate the middle element = left + (right - left) // 2
			# this avoids integer overflow in case of large N
			a_		:		Tuple				    =				0 + (len(a__) - 0) // 2

			# Split the array into halves till the array length becomes equal to One
			# merge the arrays of single length returned by mergeSort function and
			# pass them into the merge arrays function which merges the array
			a_		:		List[str]				    =				array[:middle]
			a_		:		str				    =				array[middle:]

			return merge(merge_sort(a__)       ,					merge_sort(a__))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





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


