SCREAMING_SNAKE_CASE :	Dict							     =		"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich



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


