




"""simple docstring"""


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


A_						=				logging.get_logger(__name__)

A_						=				{
    '''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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"speech_to_text_2"
 lowercase__ 					=					["past_key_values"]
 lowercase__ 					=					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}


 def __init__(				self:		List[str],      a_:		str=10_000,      a_:		str=6,      a_:		List[Any]=2_048,      a_:		Optional[int]=4,      a_:		List[str]=0.0,      a_:		Optional[int]=True,      a_:		List[str]="relu",      a_:		Optional[Any]=256,      a_:		Any=0.1,      a_:		List[str]=0.0,      a_:		Optional[int]=0.0,      a_:		Optional[Any]=0.02,      a_:		Union[str, Any]=2,      a_:		List[str]=True,      a_:		Optional[Any]=1,      a_:		Dict=0,      a_:		Dict=2,      a_:		Optional[int]=1_024,      **a_:		Any,      ):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  vocab_size
   _snake_case							:		str								=  d_model
   _snake_case							:		Tuple								=  decoder_ffn_dim
   _snake_case							:		Optional[int]								=  decoder_layers
   _snake_case							:		Optional[Any]								=  decoder_attention_heads
   _snake_case							:		Union[str, Any]								=  dropout
   _snake_case							:		List[str]								=  attention_dropout
   _snake_case							:		str								=  activation_dropout
   _snake_case							:		Union[str, Any]								=  activation_function
   _snake_case							:		List[str]								=  init_std
   _snake_case							:		int								=  decoder_layerdrop
   _snake_case							:		int								=  use_cache
   _snake_case							:		str								=  decoder_layers
   _snake_case							:		Tuple								=  scale_embedding  # scale factor will be sqrt(d_model) if True
   _snake_case							:		int								=  max_target_positions

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








"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


