



def 			UpperCamelCase(  __UpperCamelCase							:		list[list[float]] ):
		lowerCAmelCase_			:     list[list[float]]									= []
		for data in source_data:
				for i, el in enumerate(__UpperCamelCase ):
						if len(__UpperCamelCase ) < i + 1:
								data_lists.append([] )
						data_lists[i].append(float(__UpperCamelCase ) )
		return data_lists


def 			UpperCamelCase(  __UpperCamelCase							:		list[list[float]]     ,__UpperCamelCase							:		list[int] ):
		lowerCAmelCase_			:     list[list[float]]									= []
		for dlist, weight in zip(__UpperCamelCase     ,__UpperCamelCase ):
				lowerCAmelCase_			:     int									= min(__UpperCamelCase )
				lowerCAmelCase_			:     Optional[Any]									= max(__UpperCamelCase )

				lowerCAmelCase_			:     list[float]									= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind)) )
								except ZeroDivisionError:
										score.append(1 )

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind) )
								except ZeroDivisionError:
										score.append(0 )

        # weight not 0 or 1
				else:
						lowerCAmelCase_			:     str									= f"""Invalid weight of {weight:f} provided"""
						raise ValueError(__UpperCamelCase )

				score_lists.append(__UpperCamelCase )

		return score_lists


def 			UpperCamelCase(  __UpperCamelCase							:		list[list[float]] ):
		lowerCAmelCase_			:     list[float]									= [0 for i in range(len(score_lists[0] ) )]

		for slist in score_lists:
				for j, ele in enumerate(__UpperCamelCase ):
						lowerCAmelCase_			:     List[str]									= final_scores[j] + ele

		return final_scores





def 			UpperCamelCase(  __UpperCamelCase							:		list[list[float]]     ,__UpperCamelCase							:		list[int] ):
		lowerCAmelCase_			:     Optional[Any]									= get_data(__UpperCamelCase )
		lowerCAmelCase_			:     Tuple									= calculate_each_score(__UpperCamelCase     ,__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= generate_final_scores(__UpperCamelCase )

		# append scores to source data
		for i, ele in enumerate(__UpperCamelCase ):
				source_data[i].append(__UpperCamelCase )

		return source_data







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

