





'''simple docstring'''






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


A_		:  Optional[int]		 =						logging.get_logger(__name__)

A_		:  Dict		 =						{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''lilt'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE=3_0_5_2_2	,		__SCREAMING_SNAKE_CASE=7_6_8	,		__SCREAMING_SNAKE_CASE=1_2	,		__SCREAMING_SNAKE_CASE=1_2	,		__SCREAMING_SNAKE_CASE=3_0_7_2	,		__SCREAMING_SNAKE_CASE="gelu"	,		__SCREAMING_SNAKE_CASE=0.1	,		__SCREAMING_SNAKE_CASE=0.1	,		__SCREAMING_SNAKE_CASE=5_1_2	,		__SCREAMING_SNAKE_CASE=2	,		__SCREAMING_SNAKE_CASE=0.02	,		__SCREAMING_SNAKE_CASE=1e-1_2	,		__SCREAMING_SNAKE_CASE=0	,		__SCREAMING_SNAKE_CASE="absolute"	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=4	,		__SCREAMING_SNAKE_CASE=1_0_2_4	,		**__SCREAMING_SNAKE_CASE	,		):
    super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[Any]    =  vocab_size
    snake_case__     :					List[str]    =  hidden_size
    snake_case__     :					int    =  num_hidden_layers
    snake_case__     :					Dict    =  num_attention_heads
    snake_case__     :					List[str]    =  hidden_act
    snake_case__     :					Union[str, Any]    =  intermediate_size
    snake_case__     :					Tuple    =  hidden_dropout_prob
    snake_case__     :					Optional[Any]    =  attention_probs_dropout_prob
    snake_case__     :					Union[str, Any]    =  max_position_embeddings
    snake_case__     :					List[Any]    =  type_vocab_size
    snake_case__     :					Optional[int]    =  initializer_range
    snake_case__     :					List[Any]    =  layer_norm_eps
    snake_case__     :					Optional[int]    =  position_embedding_type
    snake_case__     :					List[str]    =  classifier_dropout
    snake_case__     :					Tuple    =  channel_shrink_ratio
    snake_case__     :					Dict    =  max_ad_position_embeddings









"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





