
from typing import TYPE_CHECKING

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


lowerCamelCase_  :	str 				=					{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_  :	int 				=					["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_  :	Union[str, Any] 				=					["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_  :	List[Any] 				=					[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_  :	Tuple 				=					[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_  :	List[str] 				=					[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

					lowerCamelCase_  :	str 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








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


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

lowerCamelCase_  :	Dict 				=					{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	"""luke"""

			def __init__(    self	:    Tuple  ,		snake_case_	:    List[Any]=5_0267  ,		snake_case_	:    Any=50_0000  ,		snake_case_	:    str=768  ,		snake_case_	:    int=256  ,		snake_case_	:    str=12  ,		snake_case_	:    int=12  ,		snake_case_	:    Dict=3072  ,		snake_case_	:    Optional[Any]="gelu"  ,		snake_case_	:    Dict=0.1  ,		snake_case_	:    List[str]=0.1  ,		snake_case_	:    int=512  ,		snake_case_	:    Dict=2  ,		snake_case_	:    List[Any]=0.02  ,		snake_case_	:    int=1e-12  ,		snake_case_	:    Union[str, Any]=True  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Dict=1  ,		snake_case_	:    Optional[int]=0  ,		snake_case_	:    List[str]=2  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(pad_token_id=snake_case_  ,		bos_token_id=snake_case_  ,		eos_token_id=snake_case_  ,		**snake_case_ )

							UpperCamelCase_:							Dict		=  vocab_size
							UpperCamelCase_:							Tuple		=  entity_vocab_size
							UpperCamelCase_:							Optional[int]		=  hidden_size
							UpperCamelCase_:							Any		=  entity_emb_size
							UpperCamelCase_:							str		=  num_hidden_layers
							UpperCamelCase_:							Union[str, Any]		=  num_attention_heads
							UpperCamelCase_:							Dict		=  hidden_act
							UpperCamelCase_:							Dict		=  intermediate_size
							UpperCamelCase_:							str		=  hidden_dropout_prob
							UpperCamelCase_:							List[str]		=  attention_probs_dropout_prob
							UpperCamelCase_:							int		=  max_position_embeddings
							UpperCamelCase_:							int		=  type_vocab_size
							UpperCamelCase_:							List[Any]		=  initializer_range
							UpperCamelCase_:							Union[str, Any]		=  layer_norm_eps
							UpperCamelCase_:							Tuple		=  use_entity_aware_attention
							UpperCamelCase_:							int		=  classifier_dropout






