



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_			=					{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					["""DebertaTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					[
      """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DebertaForMaskedLM""",
      """DebertaForQuestionAnswering""",
      """DebertaForSequenceClassification""",
      """DebertaForTokenClassification""",
      """DebertaModel""",
      """DebertaPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					[
      """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDebertaForMaskedLM""",
      """TFDebertaForQuestionAnswering""",
      """TFDebertaForSequenceClassification""",
      """TFDebertaForTokenClassification""",
      """TFDebertaModel""",
      """TFDebertaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




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


a_			=					logging.get_logger(__name__)

a_			=					{
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """lxmert"""
 lowerCAmelCase__       					=  {}




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=12	,				__UpperCAmelCase=9500	,				__UpperCAmelCase=1600	,				__UpperCAmelCase=400	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=9	,				__UpperCAmelCase=5	,				__UpperCAmelCase=5	,				__UpperCAmelCase=2048	,				__UpperCAmelCase=4	,				__UpperCAmelCase=6.67	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    num_qa_labels
    __lowerCamelCase													=    num_object_labels
    __lowerCamelCase													=    num_attr_labels
    __lowerCamelCase													=    l_layers
    __lowerCamelCase													=    x_layers
    __lowerCamelCase													=    r_layers
    __lowerCamelCase													=    visual_feat_dim
    __lowerCamelCase													=    visual_pos_dim
    __lowerCamelCase													=    visual_loss_normalizer
    __lowerCamelCase													=    task_matched
    __lowerCamelCase													=    task_mask_lm
    __lowerCamelCase													=    task_obj_predict
    __lowerCamelCase													=    task_qa
    __lowerCamelCase													=    visual_obj_loss
    __lowerCamelCase													=    visual_attr_loss
    __lowerCamelCase													=    visual_feat_loss
    __lowerCamelCase													=    {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
    super().__init__(**__UpperCAmelCase				)






