


"""simple docstring"""
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__SCREAMING_SNAKE_CASE							:			int						=			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> List[str]:
							return (abs(source - target ) / target) < 0.01







@pytest.mark.integration
def   _a (					_SCREAMING_SNAKE_CASE )			-> List[Any]:
							snake_case_							=						_TestCommandArgs(dataset=A__ ,       all_configs=A__ ,       save_infos=A__ )
							snake_case_							=						TestCommand(*A__ )
							test_command.run()
							snake_case_							=						os.path.join(A__ ,       """README.md""" )
							assert os.path.exists(A__ )
							snake_case_							=						DatasetInfosDict.from_directory(A__ )
							snake_case_							=						DatasetInfosDict(
							    {
							        """default""": DatasetInfo(
							            features=Features(
							                {
							                    """tokens""": Sequence(Value("""string""" ) ),
							                    """ner_tags""": Sequence(
							                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""] ) ),
							                    """langs""": Sequence(Value("""string""" ) ),
							                    """spans""": Sequence(Value("""string""" ) ),
							                } ) ,       splits=[
							                {
							                    """name""": """train""",
							                    """num_bytes""": 2_351_563,
							                    """num_examples""": 10_000,
							                },
							                {
							                    """name""": """validation""",
							                    """num_bytes""": 238_418,
							                    """num_examples""": 1_000,
							                },
							            ] ,       download_size=3_940_680 ,       dataset_size=2_589_981 ,       )
							    } )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														snake_case_							=						getattr(dataset_infos["""default"""] ,       A__ ), getattr(expected_dataset_infos["""default"""] ,       A__ )
														if key == "num_bytes":
																					assert is_apercent_close(A__ ,       A__ )
														elif key == "splits":
																					assert list(A__ ) == list(A__ )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes ,       expected[split].num_bytes )
														else:
																					result == expected







'''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



