def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	[]
 for data in source_data:
  for i, el in enumerate(_A       ):
   if len(_A       ) < i + 1:
    data_lists.append([]       )
   data_lists[i].append(float(_A       )       )
 return data_lists



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	[]
 for dlist, weight in zip(_A				,					_A       ):
  __a							=	min(_A       )
  __a							=	max(_A       )

  __a							=	[]
  # 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:
   __a							=	f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A       )

  score_lists.append(_A       )

 return score_lists



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	[0 for i in range(len(score_lists[0]       )       )]

 for slist in score_lists:
  for j, ele in enumerate(_A       ):
   __a							=	final_scores[j] + ele

 return final_scores






def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	get_data(_A       )
 __a							=	calculate_each_score(_A				,					_A       )
 __a							=	generate_final_scores(_A       )

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

 return source_data



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


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


