





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Any      	=			{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				__a            ="xlm-roberta-xl"




				def __init__(  self     :					Any					,				__a     :					Optional[Any]=25_08_80					,				__a     :					Optional[Any]=25_60					,				__a     :					Any=36					,				__a     :					List[Any]=32					,				__a     :					str=1_02_40					,				__a     :					int="gelu"					,				__a     :					List[str]=0.1					,				__a     :					Dict=0.1					,				__a     :					Optional[int]=5_14					,				__a     :					Dict=1					,				__a     :					Dict=0.02					,				__a     :					int=1e-0_5					,				__a     :					Any=1					,				__a     :					Optional[Any]=0					,				__a     :					int=2					,				__a     :					int="absolute"					,				__a     :					Tuple=True					,				__a     :					Tuple=None					,				**__a     :					str					,				):
								super().__init__(pad_token_id=_UpperCAmelCase					,				bos_token_id=_UpperCAmelCase					,				eos_token_id=_UpperCAmelCase					,				**_UpperCAmelCase					)
								_a								= vocab_size
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= hidden_act
								_a								= intermediate_size
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= max_position_embeddings
								_a								= type_vocab_size
								_a								= initializer_range
								_a								= layer_norm_eps
								_a								= position_embedding_type
								_a								= use_cache
								_a								= classifier_dropout


class 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								if self.task == "multiple-choice":
												_a								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
												_a								= {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]					)

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

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