


"""simple docstring"""
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class    __A   (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					int				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						inspect.getfile(accelerate.test_utils				)
														snake_case_							=						os.path.sep.join(
														    mod_file.split(os.path.sep				)[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""]				)

														from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

														snake_case_							=						test_metrics



							@require_cpu
							def 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->List[str]:



														"""simple docstring"""



														debug_launcher(self.test_metrics.main						,				num_processes=1				)



							@require_cpu
							def 						lowerCAmelCase				( self							:					Any				)    ->Optional[Any]:



														"""simple docstring"""



														debug_launcher(self.test_metrics.main				)



							@require_single_gpu
							def 						lowerCAmelCase				( self							:					Optional[Any]				)    ->int:



														"""simple docstring"""



														self.test_metrics.main()



							@require_multi_gpu
							def 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->int:



														"""simple docstring"""



														print(F"""Found {torch.cuda.device_count()} devices."""				)
														snake_case_							=						["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
														with patch_environment(omp_num_threads=1				):
																					execute_subprocess_async(UpperCAmelCase_						,				env=os.environ.copy()				)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  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 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      list[float]			=   []
    # 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_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[float]			=   [0 for i in range(len(score_lists[0]					)					)]

  for slist in score_lists:
    for j, ele in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

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

  return source_data



