






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_					= logging.get_logger(__name__)

# TODO: upload to AWS
a_					= {
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
	snake_case_  							=							"""retribert"""




	def __init__(      self	:							Any					,      __lowercase	:							Dict=3_05_22					,      __lowercase	:							Tuple=7_68					,      __lowercase	:							Any=8					,      __lowercase	:							Dict=12					,      __lowercase	:							Any=30_72					,      __lowercase	:							Optional[int]="gelu"					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Tuple=0.1					,      __lowercase	:							Optional[Any]=5_12					,      __lowercase	:							List[Any]=2					,      __lowercase	:							Any=0.02					,      __lowercase	:							str=1e-12					,      __lowercase	:							List[str]=True					,      __lowercase	:							Optional[Any]=1_28					,      __lowercase	:							Optional[int]=0					,      **__lowercase	:							Tuple					,      )	->						Optional[Any]:
							super().__init__(pad_token_id=__lowercase					,      **__lowercase      )

							SCREAMING_SNAKE_CASE__     :				str									=vocab_size
							SCREAMING_SNAKE_CASE__     :				Dict									=hidden_size
							SCREAMING_SNAKE_CASE__     :				int									=num_hidden_layers
							SCREAMING_SNAKE_CASE__     :				List[Any]									=num_attention_heads
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=hidden_act
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
							SCREAMING_SNAKE_CASE__     :				List[str]									=hidden_dropout_prob
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__     :				List[str]									=max_position_embeddings
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=type_vocab_size
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=initializer_range
							SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
							SCREAMING_SNAKE_CASE__     :				int									=share_encoders
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=projection_dim







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)