





"""simple docstring"""



def 		__lowercase					(       snake_case_  :  list[list[float]]						)       ->Tuple:



				'''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  list[list[float]]    ,snake_case_  :  list[int]						)       ->int:



				'''simple docstring'''







				__A   :						Optional[int]									=					[]
				for dlist, weight in zip(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_						):
								__A   :						str									=					min(SCREAMING_SNAKE_CASE_						)
								__A   :						str									=					max(SCREAMING_SNAKE_CASE_						)

								__A   :						List[str]									=					[]
								# 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   :						List[Any]									=					F"""Invalid weight of {weight:f} provided"""
												raise ValueError(SCREAMING_SNAKE_CASE_						)

								score_lists.append(SCREAMING_SNAKE_CASE_						)

				return score_lists







def 		__lowercase					(       snake_case_  :  list[list[float]]						)       ->Dict:



				'''simple docstring'''







				__A   :						Optional[int]									=					[0 for i in range(len(score_lists[0]						)						)]

				for slist in score_lists:
								for j, ele in enumerate(SCREAMING_SNAKE_CASE_						):
												__A   :						Union[str, Any]									=					final_scores[j] + ele

				return final_scores







def 		__lowercase					(       snake_case_  :  list[list[float]]    ,snake_case_  :  list[int]						)       ->Optional[int]:



				'''simple docstring'''







				__A   :						List[str]									=					get_data(SCREAMING_SNAKE_CASE_						)
				__A   :						Tuple									=					calculate_each_score(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_						)
				__A   :						int									=					generate_final_scores(SCREAMING_SNAKE_CASE_						)

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

				return source_data











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BloomForCausalLM""",
           """BloomModel""",
           """BloomPreTrainedModel""",
           """BloomForSequenceClassification""",
           """BloomForTokenClassification""",
           """BloomForQuestionAnswering""",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




