'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_A		:			Optional[Any]	      ={
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}




class _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	str     =					"vilt"







      def __init__(     self     ,						lowercase=3_0522     ,						lowercase=2     ,						lowercase=2     ,						lowercase=40     ,						lowercase=768     ,						lowercase=12     ,						lowercase=12     ,						lowercase=3072     ,						lowercase="gelu"     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=0.02     ,						lowercase=1e-12     ,						lowercase=384     ,						lowercase=32     ,						lowercase=3     ,						lowercase=True     ,						lowercase=-1     ,						lowercase=False     ,						lowercase=-1     ,						**lowercase     ,						):
        super().__init__(tie_word_embeddings=lowercase     ,						**lowercase    )

        lowercase							:       List[str]											=      vocab_size
        lowercase							:       str											=      type_vocab_size
        lowercase							:       str											=      modality_type_vocab_size
        lowercase							:       Dict											=      max_position_embeddings

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

        lowercase							:       List[Any]											=      image_size
        lowercase							:       Union[str, Any]											=      patch_size
        lowercase							:       int											=      num_channels
        lowercase							:       str											=      qkv_bias
        lowercase							:       List[Any]											=      max_image_length
        lowercase							:       Union[str, Any]											=      num_images
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A		:			int	      ={'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			str	      =[
     '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CvtForImageClassification''',
     '''CvtModel''',
     '''CvtPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			Optional[Any]	      =[
     '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCvtForImageClassification''',
     '''TFCvtModel''',
     '''TFCvtPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_cvt import (
      CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CvtForImageClassification,
      CvtModel,
      CvtPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_cvt import (
      TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCvtForImageClassification,
      TFCvtModel,
      TFCvtPreTrainedModel,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)