
'''simple docstring'''





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


a__		:   Any            =       logging.get_logger(__name__)

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


class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE):
 UpperCAmelCase__    :    Optional[Any]										=					'tvlt'






 def __init__(			self  :Optional[int]	,				_A  :str=224	,				_A  :str=2_048	,				_A  :List[str]=128	,				_A  :List[Any]=[16, 16]	,				_A  :str=[16, 16]	,				_A  :Tuple=3	,				_A  :Optional[int]=1	,				_A  :Any=8	,				_A  :Union[str, Any]=768	,				_A  :Optional[Any]=12	,				_A  :str=12	,				_A  :Dict=3_072	,				_A  :Union[str, Any]="gelu"	,				_A  :Tuple=0.0	,				_A  :Any=0.0	,				_A  :int=0.02	,				_A  :Tuple=1E-6	,				_A  :int=True	,				_A  :Union[str, Any]=False	,				_A  :Optional[Any]=16	,				_A  :Dict=512	,				_A  :List[str]=8	,				_A  :str=2_048	,				_A  :int=0.75	,				_A  :Dict=0.15	,				_A  :int="frame-level"	,				_A  :Any=True	,				_A  :Optional[Any]=True	,				_A  :Optional[Any]="classification"	,				**_A  :int	,				)				->			Union[str, Any]:







       '''simple docstring'''



       super().__init__(**_A			)

       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       	=			image_size
       __A       	=			spectrogram_length
       __A       	=			frequency_length
       __A       	=			image_patch_size
       __A       	=			audio_patch_size
       __A       	=			num_image_channels
       __A       	=			num_audio_channels
       __A       	=			num_frames

       __A       	=			hidden_size
       __A       	=			num_hidden_layers
       __A       	=			num_attention_heads
       __A       	=			intermediate_size
       __A       	=			hidden_act
       __A       	=			hidden_dropout_prob
       __A       	=			attention_probs_dropout_prob
       __A       	=			initializer_range
       __A       	=			layer_norm_eps
       __A       	=			qkv_bias
       __A       	=			use_mean_pooling

       __A       	=			decoder_num_attention_heads
       __A       	=			decoder_hidden_size
       __A       	=			decoder_num_hidden_layers
       __A       	=			decoder_intermediate_size
       __A       	=			pixel_mask_ratio
       __A       	=			audio_mask_ratio
       __A       	=			audio_mask_type

       __A       	=			task_matching
       __A       	=			task_mae
       __A       	=			loss_type








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__		:   Optional[Any]            =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MobileViTV2ForImageClassification",
     "MobileViTV2ForSemanticSegmentation",
     "MobileViTV2Model",
     "MobileViTV2PreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a__		:   List[str]            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






