from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE :	Optional[int]							     =		[
     """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTBigCodeForSequenceClassification""",
     """GPTBigCodeForTokenClassification""",
     """GPTBigCodeForCausalLM""",
     """GPTBigCodeModel""",
     """GPTBigCodePreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE :	Dict							     =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



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


