

"""simple docstring"""




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


__UpperCAmelCase			=					logging.get_logger(__name__)

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







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""lxmert"""
  snake_case_												=							{}

  def __init__(	self		:      Optional[Any]       ,A		:      Any=30_522       ,A		:      Dict=768       ,A		:      Optional[Any]=12       ,A		:      Optional[int]=9_500       ,A		:      List[Any]=1_600       ,A		:      int=400       ,A		:      List[Any]=3_072       ,A		:      List[Any]="gelu"       ,A		:      str=0.1       ,A		:      Optional[Any]=0.1       ,A		:      Any=512       ,A		:      Tuple=2       ,A		:      Optional[int]=0.0_2       ,A		:      List[Any]=1e-12       ,A		:      List[Any]=9       ,A		:      Optional[int]=5       ,A		:      List[str]=5       ,A		:      List[Any]=2_048       ,A		:      Union[str, Any]=4       ,A		:      Tuple=6.6_7       ,A		:      Union[str, Any]=True       ,A		:      Optional[Any]=True       ,A		:      str=True       ,A		:      Tuple=True       ,A		:      Union[str, Any]=True       ,A		:      int=True       ,A		:      List[Any]=True       ,**A		:      Tuple       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       vocab_size
   UpperCAmelCase__       :	str        =       hidden_size
   UpperCAmelCase__       :	Optional[int]        =       num_attention_heads
   UpperCAmelCase__       :	List[Any]        =       hidden_act
   UpperCAmelCase__       :	Tuple        =       intermediate_size
   UpperCAmelCase__       :	Optional[Any]        =       hidden_dropout_prob
   UpperCAmelCase__       :	List[Any]        =       attention_probs_dropout_prob
   UpperCAmelCase__       :	Optional[int]        =       max_position_embeddings
   UpperCAmelCase__       :	List[str]        =       type_vocab_size
   UpperCAmelCase__       :	Dict        =       initializer_range
   UpperCAmelCase__       :	List[str]        =       layer_norm_eps
   UpperCAmelCase__       :	Tuple        =       num_qa_labels
   UpperCAmelCase__       :	Optional[Any]        =       num_object_labels
   UpperCAmelCase__       :	Dict        =       num_attr_labels
   UpperCAmelCase__       :	Optional[int]        =       l_layers
   UpperCAmelCase__       :	Dict        =       x_layers
   UpperCAmelCase__       :	int        =       r_layers
   UpperCAmelCase__       :	Optional[int]        =       visual_feat_dim
   UpperCAmelCase__       :	Optional[Any]        =       visual_pos_dim
   UpperCAmelCase__       :	Any        =       visual_loss_normalizer
   UpperCAmelCase__       :	Optional[Any]        =       task_matched
   UpperCAmelCase__       :	str        =       task_mask_lm
   UpperCAmelCase__       :	Union[str, Any]        =       task_obj_predict
   UpperCAmelCase__       :	int        =       task_qa
   UpperCAmelCase__       :	Union[str, Any]        =       visual_obj_loss
   UpperCAmelCase__       :	Tuple        =       visual_attr_loss
   UpperCAmelCase__       :	Tuple        =       visual_feat_loss
   UpperCAmelCase__       :	int        =       {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
   super().__init__(**A     )








'''simple docstring'''



from typing import TYPE_CHECKING

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


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

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





