





"""simple docstring"""



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


a_			=      logging.get_logger(__name__)

a_			=      {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""ctrl"""
 _lowerCamelCase				=					["""past_key_values"""]
 _lowerCamelCase				=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }

 def __init__(   self ,						__lowerCamelCase=24_6534 ,						__lowerCamelCase=256 ,						__lowerCamelCase=1280 ,						__lowerCamelCase=8192 ,						__lowerCamelCase=48 ,						__lowerCamelCase=16 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=1e-6 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=True ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						Tuple									=					vocab_size
     __A   :						Optional[int]									=					n_positions
     __A   :						List[str]									=					n_embd
     __A   :						List[str]									=					n_layer
     __A   :						Union[str, Any]									=					n_head
     __A   :						List[str]									=					dff
     __A   :						int									=					resid_pdrop
     __A   :						Union[str, Any]									=					embd_pdrop
     __A   :						List[Any]									=					layer_norm_epsilon
     __A   :						Any									=					initializer_range

     __A   :						Optional[int]									=					use_cache

     super().__init__(**__lowerCamelCase  )











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

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




