"""simple docstring"""
import copy

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


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""fsmt_decoder"""



      def __init__(		self       ,     lowercase=0       ,     lowercase=0)      ->     Optional[int]:





        '''simple docstring'''





        super().__init__()
        a__:	Dict     				=      vocab_size
        a__:	str     				=      bos_token_id







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""fsmt"""
      a__								=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(		self       ,     lowercase=["en", "de"]       ,     lowercase=4_20_24       ,     lowercase=4_20_24       ,     lowercase="relu"       ,     lowercase=10_24       ,     lowercase=2_00       ,     lowercase=10_24       ,     lowercase=40_96       ,     lowercase=12       ,     lowercase=16       ,     lowercase=0.0       ,     lowercase=40_96       ,     lowercase=12       ,     lowercase=16       ,     lowercase=0.0       ,     lowercase=0.0       ,     lowercase=0.1       ,     lowercase=0.0       ,     lowercase=0.02       ,     lowercase=2       ,     lowercase=True       ,     lowercase=True       ,     lowercase=False       ,     lowercase=5       ,     lowercase=1.0       ,     lowercase=False       ,     lowercase=True       ,     lowercase=1       ,     lowercase=0       ,     lowercase=2       ,     lowercase=2       ,     **lowercase       ,     )      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      langs
        a__:	Union[str, Any]     				=      src_vocab_size
        a__:	Tuple     				=      tgt_vocab_size
        a__:	Optional[Any]     				=      d_model  # encoder_embed_dim and decoder_embed_dim

        a__:	str     				=      encoder_ffn_dim
        a__:	Optional[int]     				=      encoder_layers
        a__:	Dict     				=      encoder_attention_heads
        a__:	Optional[int]     				=      encoder_layerdrop
        a__:	Dict     				=      decoder_layerdrop
        a__:	Union[str, Any]     				=      decoder_ffn_dim
        a__:	List[Any]     				=      decoder_layers
        a__:	Dict     				=      decoder_attention_heads
        a__:	List[str]     				=      max_position_embeddings
        a__:	Optional[Any]     				=      init_std  # Normal(0, this parameter)
        a__:	Optional[Any]     				=      activation_function

        a__:	List[Any]     				=      DecoderConfig(vocab_size=lowercase       ,     bos_token_id=lowercase)
        if "decoder" in common_kwargs:
          del common_kwargs["decoder"]

        a__:	List[str]     				=      scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        a__:	Dict     				=      attention_dropout
        a__:	Union[str, Any]     				=      activation_dropout
        a__:	List[Any]     				=      dropout

        a__:	Optional[int]     				=      use_cache
        super().__init__(
            pad_token_id=lowercase       ,     bos_token_id=lowercase       ,     eos_token_id=lowercase       ,     decoder_start_token_id=lowercase       ,     is_encoder_decoder=lowercase       ,     tie_word_embeddings=lowercase       ,     forced_eos_token_id=lowercase       ,     max_length=lowercase       ,     num_beams=lowercase       ,     length_penalty=lowercase       ,     early_stopping=lowercase       ,     **lowercase       ,     )



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      copy.deepcopy(self.__dict__)
        a__:	str     				=      self.decoder.to_dict()
        a__:	int     				=      self.__class__.model_type
        return output



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


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    '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   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""splinter"""



      def __init__(		self       ,     lowercase=3_05_22       ,     lowercase=7_68       ,     lowercase=12       ,     lowercase=12       ,     lowercase=30_72       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=True       ,     lowercase=0       ,     lowercase=1_04       ,     **lowercase       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase       ,     **lowercase)

        a__:	Dict     				=      vocab_size
        a__:	Dict     				=      max_position_embeddings
        a__:	Dict     				=      hidden_size
        a__:	int     				=      num_hidden_layers
        a__:	str     				=      num_attention_heads
        a__:	Dict     				=      intermediate_size
        a__:	Tuple     				=      hidden_act
        a__:	Optional[Any]     				=      hidden_dropout_prob
        a__:	List[Any]     				=      attention_probs_dropout_prob
        a__:	Tuple     				=      initializer_range
        a__:	str     				=      type_vocab_size
        a__:	Optional[Any]     				=      layer_norm_eps
        a__:	Any     				=      use_cache
        a__:	List[Any]     				=      question_token_id


