'''simple docstring'''

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


_A			:      Tuple					      =				logging.get_logger(__name__)

_A			:      List[Any]					      =				{
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}







class 				_lowercase     (				A__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""mra"""
 def __init__(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Tuple=5_02_65      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=7_68      ,       SCREAMING_SNAKE_CASE__   :	int=12      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12      ,       SCREAMING_SNAKE_CASE__   :	Tuple=30_72      ,       SCREAMING_SNAKE_CASE__   :	str="gelu"      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=5_12      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=1      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	int=1e-5      ,       SCREAMING_SNAKE_CASE__   :	str="absolute"      ,       SCREAMING_SNAKE_CASE__   :	Any=4      ,       SCREAMING_SNAKE_CASE__   :	List[str]="full"      ,       SCREAMING_SNAKE_CASE__   :	Any=0      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=1      ,       SCREAMING_SNAKE_CASE__   :	Any=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2      ,       **SCREAMING_SNAKE_CASE__   :	int      ,       )  ->    str:
  super().__init__(pad_token_id=__snake_case      ,       bos_token_id=__snake_case      ,       eos_token_id=__snake_case      ,       **__snake_case							)

  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						max_position_embeddings
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						initializer_range
  __lowerCAmelCase											=						type_vocab_size
  __lowerCAmelCase											=						layer_norm_eps
  __lowerCAmelCase											=						position_embedding_type
  __lowerCAmelCase											=						block_per_row
  __lowerCAmelCase											=						approx_mode
  __lowerCAmelCase											=						initial_prior_first_n_blocks
  __lowerCAmelCase											=						initial_prior_diagonal_n_blocks











'''simple docstring'''






from typing import TYPE_CHECKING

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


UpperCamelCase__:   str								=			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			["LxmertTokenizerFast"]

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			[
						    "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__:   Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





