


'''simple docstring'''



from ...configuration_utils import PretrainedConfig



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       "bert-generation"

     def __init__(    self					:		List[Any]							,			snake_case					:		Optional[int]=5_0358							,			snake_case					:		int=1024							,			snake_case					:		List[Any]=24							,			snake_case					:		Tuple=16							,			snake_case					:		List[Any]=4096							,			snake_case					:		List[Any]="gelu"							,			snake_case					:		Tuple=0.1							,			snake_case					:		List[str]=0.1							,			snake_case					:		str=512							,			snake_case					:		Tuple=0.02							,			snake_case					:		Dict=1E-12							,			snake_case					:		List[str]=0							,			snake_case					:		List[str]=2							,			snake_case					:		List[str]=1							,			snake_case					:		Optional[Any]="absolute"							,			snake_case					:		Tuple=True							,			**snake_case					:		Union[str, Any]							,			)						->							Any:
          super().__init__(pad_token_id=snake_case							,			bos_token_id=snake_case							,			eos_token_id=snake_case							,			**snake_case      )

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



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase      :List[Any]			    =       {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[str]			    =       [
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      __UpperCAmelCase      :int			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)