






'''simple docstring'''



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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''lxmert'''
      UpperCamelCase_								=			{}






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Dict=3_0522     ,      UpperCAmelCase			:		int=768     ,      UpperCAmelCase			:		Optional[Any]=12     ,      UpperCAmelCase			:		Tuple=9500     ,      UpperCAmelCase			:		List[str]=1600     ,      UpperCAmelCase			:		Union[str, Any]=400     ,      UpperCAmelCase			:		Optional[int]=3072     ,      UpperCAmelCase			:		List[str]="gelu"     ,      UpperCAmelCase			:		Optional[Any]=0.1     ,      UpperCAmelCase			:		List[str]=0.1     ,      UpperCAmelCase			:		Tuple=512     ,      UpperCAmelCase			:		Union[str, Any]=2     ,      UpperCAmelCase			:		Optional[Any]=0.0_2     ,      UpperCAmelCase			:		int=1e-12     ,      UpperCAmelCase			:		int=9     ,      UpperCAmelCase			:		Dict=5     ,      UpperCAmelCase			:		Optional[Any]=5     ,      UpperCAmelCase			:		int=2048     ,      UpperCAmelCase			:		Dict=4     ,      UpperCAmelCase			:		int=6.6_7     ,      UpperCAmelCase			:		List[Any]=True     ,      UpperCAmelCase			:		Dict=True     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		List[str]=True     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		List[Any]=True     ,      UpperCAmelCase			:		Optional[Any]=True     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  List[str]:


       '''simple docstring'''

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











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



