





'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase__    :      int						=    {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    ['''ViTFeatureExtractor''']
  lowercase__    :      Tuple						=    ['''ViTImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      List[Any]						=    [
      '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTForImageClassification''',
      '''ViTForMaskedImageModeling''',
      '''ViTModel''',
      '''ViTPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Union[str, Any]						=    [
      '''TFViTForImageClassification''',
      '''TFViTModel''',
      '''TFViTPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Tuple						=    [
      '''FlaxViTForImageClassification''',
      '''FlaxViTModel''',
      '''FlaxViTPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

  lowercase__    :      str						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])