
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       len(A_					)
  for i in range(length - 1					):
    __magic_name__				     =       i
    for k in range(i + 1,     A_					):
      if collection[k] < collection[least]:
        __magic_name__				     =       k
    if least != i:
      __magic_name__     ,    __magic_name__				     =       (collection[i], collection[least])
  return collection


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



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


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

