



'''simple docstring'''





from __future__ import annotations

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> list[list[int]]:
  __lowerCamelCase			:			list[list[int]]       			=					[]
  create_all_state(1			,_lowerCAmelCase			,_lowerCAmelCase			,[]			,_lowerCAmelCase			)
  return result

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,) -> None:
  if level == 0:
    total_list.append(current_list[:]			)
    return

  for i in range(_lowerCAmelCase			,total_number - level + 2			):
    current_list.append(_lowerCAmelCase			)
    create_all_state(i + 1			,_lowerCAmelCase			,level - 1			,_lowerCAmelCase			,_lowerCAmelCase			)
    current_list.pop()

def 			a_	(				_lowerCAmelCase			) -> None:
  for i in total_list:
    print(*_lowerCAmelCase			)


if __name__ == "__main__":
   _UpperCamelCase											=		4
   _UpperCamelCase											=		2
   _UpperCamelCase											=		generate_all_combinations(n, k)
   print_all_state(total_list)











'''simple docstring'''





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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    '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    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""pegasus"""
  a_				   =["""past_key_values"""]
  a_				   ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




  def __init__(					self :      Any     ,      _a :      int=5_0265     ,      _a :      Tuple=1024     ,      _a :      Optional[Any]=12     ,      _a :      int=4096     ,      _a :      str=16     ,      _a :      Union[str, Any]=12     ,      _a :      Optional[Any]=4096     ,      _a :      Optional[int]=16     ,      _a :      Union[str, Any]=0.0     ,      _a :      str=0.0     ,      _a :      List[Any]=True     ,      _a :      Optional[int]=True     ,      _a :      Tuple="gelu"     ,      _a :      Dict=1024     ,      _a :      int=0.1     ,      _a :      Any=0.0     ,      _a :      Union[str, Any]=0.0     ,      _a :      Any=0.02     ,      _a :      Optional[Any]=0     ,      _a :      str=False     ,      _a :      Tuple=0     ,      _a :      Optional[int]=1     ,      _a :      Union[str, Any]=1     ,      **_a :      Optional[int]     ,      )   ->   Any:
    __lowerCamelCase			:			Union[str, Any]       			=					vocab_size
    __lowerCamelCase			:			int       			=					max_position_embeddings
    __lowerCamelCase			:			Union[str, Any]       			=					d_model
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_ffn_dim
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_layers
    __lowerCamelCase			:			List[Any]       			=					encoder_attention_heads
    __lowerCamelCase			:			Optional[int]       			=					decoder_ffn_dim
    __lowerCamelCase			:			int       			=					decoder_layers
    __lowerCamelCase			:			Tuple       			=					decoder_attention_heads
    __lowerCamelCase			:			Optional[Any]       			=					dropout
    __lowerCamelCase			:			Tuple       			=					attention_dropout
    __lowerCamelCase			:			int       			=					activation_dropout
    __lowerCamelCase			:			Dict       			=					activation_function
    __lowerCamelCase			:			List[Any]       			=					init_std
    __lowerCamelCase			:			List[str]       			=					encoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					use_cache
    __lowerCamelCase			:			Tuple       			=					encoder_layers
    __lowerCamelCase			:			int       			=					scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=_a     ,      eos_token_id=_a     ,      is_encoder_decoder=_a     ,      decoder_start_token_id=_a     ,      forced_eos_token_id=_a     ,      **_a     ,      )




  @property
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      Any  )   ->   int:
    return self.d_model






