'''simple docstring'''






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


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

_UpperCAmelCase						:    Dict     					=					{
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "splinter"
   def __init__(			self			: List[Any],   UpperCamelCase__			: Optional[Any]=3_05_22,   UpperCamelCase__			: Union[str, Any]=7_68,   UpperCamelCase__			: Tuple=12,   UpperCamelCase__			: Any=12,   UpperCamelCase__			: Optional[int]=30_72,   UpperCamelCase__			: str="gelu",   UpperCamelCase__			: Any=0.1,   UpperCamelCase__			: Any=0.1,   UpperCamelCase__			: List[str]=5_12,   UpperCamelCase__			: Optional[int]=2,   UpperCamelCase__			: Any=0.02,   UpperCamelCase__			: int=1e-12,   UpperCamelCase__			: List[str]=True,   UpperCamelCase__			: Union[str, Any]=0,   UpperCamelCase__			: Tuple=1_04,   **UpperCamelCase__			: List[str],   )     -> Optional[int]:
        super().__init__(pad_token_id=UpperCamelCase__,   **UpperCamelCase__    )

        _A								=     vocab_size
        _A								=     max_position_embeddings
        _A								=     hidden_size
        _A								=     num_hidden_layers
        _A								=     num_attention_heads
        _A								=     intermediate_size
        _A								=     hidden_act
        _A								=     hidden_dropout_prob
        _A								=     attention_probs_dropout_prob
        _A								=     initializer_range
        _A								=     type_vocab_size
        _A								=     layer_norm_eps
        _A								=     use_cache
        _A								=     question_token_id




'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase						:    Optional[Any]     					=					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Dict     					=					[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase						:    int     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



