




'''simple docstring'''







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


_lowerCamelCase							:						Union[str, Any]								=  logging.get_logger(__name__)

_lowerCamelCase							:						Union[str, Any]								=  {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = """vilt"""






					def __init__(    self,  __UpperCamelCase=30_522,  __UpperCamelCase=2,  __UpperCamelCase=2,  __UpperCamelCase=40,  __UpperCamelCase=768,  __UpperCamelCase=12,  __UpperCamelCase=12,  __UpperCamelCase=3_072,  __UpperCamelCase="gelu",  __UpperCamelCase=0.0,  __UpperCamelCase=0.0,  __UpperCamelCase=0.02,  __UpperCamelCase=1E-12,  __UpperCamelCase=384,  __UpperCamelCase=32,  __UpperCamelCase=3,  __UpperCamelCase=True,  __UpperCamelCase=-1,  __UpperCamelCase=False,  __UpperCamelCase=-1,  **__UpperCamelCase,  ):
							super().__init__(tie_word_embeddings=__UpperCamelCase,  **__UpperCamelCase )

							_snake_case	       =       vocab_size
							_snake_case	       =       type_vocab_size
							_snake_case	       =       modality_type_vocab_size
							_snake_case	       =       max_position_embeddings

							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       num_attention_heads
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       initializer_range
							_snake_case	       =       layer_norm_eps

							_snake_case	       =       image_size
							_snake_case	       =       patch_size
							_snake_case	       =       num_channels
							_snake_case	       =       qkv_bias
							_snake_case	       =       max_image_length
							_snake_case	       =       num_images













"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

