
def      UpperCAmelCase__				(							__magic_name__    :  list[list[float]]	):


					'''simple docstring'''

					lowerCAmelCase		:       Optional[Any]							=  []
					for data in source_data:
										for i, el in enumerate(__lowercase	):
															if len(__lowercase	) < i + 1:
																				data_lists.append([]	)
															data_lists[i].append(float(__lowercase	)	)
					return data_lists
def      UpperCAmelCase__				(							__magic_name__    :  list[list[float]]				,	__magic_name__    :  list[int]	):


					'''simple docstring'''

					lowerCAmelCase		:       Any							=  []
					for dlist, weight in zip(__lowercase				,	__lowercase	):
										lowerCAmelCase		:       Optional[int]							=  min(__lowercase	)
										lowerCAmelCase		:       Union[str, Any]							=  max(__lowercase	)

										lowerCAmelCase		:       Optional[Any]							=  []
										# 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		:       Optional[int]							=  f'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowercase	)

										score_lists.append(__lowercase	)

					return score_lists
def      UpperCAmelCase__				(							__magic_name__    :  list[list[float]]	):


					'''simple docstring'''

					lowerCAmelCase		:       List[Any]							=  [0 for i in range(len(score_lists[0]	)	)]

					for slist in score_lists:
										for j, ele in enumerate(__lowercase	):
															lowerCAmelCase		:       str							=  final_scores[j] + ele

					return final_scores
def      UpperCAmelCase__				(							__magic_name__    :  list[list[float]]				,	__magic_name__    :  list[int]	):


					'''simple docstring'''

					lowerCAmelCase		:       Dict							=  get_data(__lowercase	)
					lowerCAmelCase		:       Optional[int]							=  calculate_each_score(__lowercase				,	__lowercase	)
					lowerCAmelCase		:       str							=  generate_final_scores(__lowercase	)

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

					return source_data








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


__a   :							Dict						= logging.get_logger(__name__)

__a   :							Dict						= {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"gpt_bigcode"
     SCREAMING_SNAKE_CASE     =					["past_key_values"]
     SCREAMING_SNAKE_CASE     =					{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Dict=50_257			,						UpperCamelCase_							:      int=1_024			,						UpperCamelCase_							:      List[Any]=768			,						UpperCamelCase_							:      str=12			,						UpperCamelCase_							:      Union[str, Any]=12			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      Tuple="gelu_pytorch_tanh"			,						UpperCamelCase_							:      Optional[Any]=0.1			,						UpperCamelCase_							:      List[str]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      int=1e-5			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Dict=50_256			,						UpperCamelCase_							:      Optional[Any]=50_256			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						vocab_size
         __A       =						n_positions
         __A       =						n_embd
         __A       =						n_layer
         __A       =						n_head
         __A       =						n_inner
         __A       =						activation_function
         __A       =						resid_pdrop
         __A       =						embd_pdrop
         __A       =						attn_pdrop
         __A       =						layer_norm_epsilon
         __A       =						initializer_range
         __A       =						scale_attn_weights
         __A       =						use_cache
         __A       =						attention_softmax_in_fpaa
         __A       =						scale_attention_softmax_in_fpaa
         __A       =						multi_query

         __A       =						bos_token_id
         __A       =						eos_token_id

         super().__init__(bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)




