





'''simple docstring'''


class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase				=      val
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      None





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:

     if self.val:
         if val < self.val:
             if self.left is None:
                 _lowerCAmelCase				=      Node(_lowerCAmelCase		)
             else:
                 self.left.insert(_lowerCAmelCase		)
         elif val > self.val:
             if self.right is None:
                 _lowerCAmelCase				=      Node(_lowerCAmelCase		)
             else:
                 self.right.insert(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      val







def 	__a(SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    if root:
        inorder(root.left		,       __A    )
        res.append(root.val    )
        inorder(root.right		,       __A    )


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    if len(__A    ) == 0:
        return arr
    _lowerCAmelCase				=      Node(arr[0]    )
    for i in range(1		,       len(__A    )    ):
        root.insert(arr[i]    )
    # Traverse BST in order.
    _lowerCAmelCase				=      []
    inorder(__A		,       __A    )
    return res


if __name__ == "__main__":
      print(tree_sort([10, 1, 3, 2, 9, 14, 13]))









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std
     _lowerCAmelCase				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

     super().__init__(
         pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     decoder_start_token_id=_lowerCAmelCase			,     **_lowerCAmelCase			,     )


