




import numpy as np



def   lowerCAmelCase						(						_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				Tuple		) ->    int:




 """simple docstring"""







 UpperCAmelCase__								=  n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCAmelCase__								=  np.floor(SCREAMING_SNAKE_CASE_		)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCAmelCase__								=  int(n_draws - floored.sum()		)
 if need_to_add > 0:
  UpperCAmelCase__								=  continuous - floored
  UpperCAmelCase__								=  np.sort(np.unique(SCREAMING_SNAKE_CASE_		)		)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   (UpperCAmelCase__       )								=  np.where(remainder == value		)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCAmelCase__								=  min(len(SCREAMING_SNAKE_CASE_		)  ,							SCREAMING_SNAKE_CASE_		)
   UpperCAmelCase__								=  rng.choice(SCREAMING_SNAKE_CASE_  ,							size=SCREAMING_SNAKE_CASE_  ,							replace=SCREAMING_SNAKE_CASE_		)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				Dict=10		) ->    List[str]:




 """simple docstring"""







 UpperCAmelCase__								=  np.unique(SCREAMING_SNAKE_CASE_  ,							return_inverse=SCREAMING_SNAKE_CASE_		)
 UpperCAmelCase__								=  classes.shape[0]
 UpperCAmelCase__								=  np.bincount(SCREAMING_SNAKE_CASE_		)
 if np.min(SCREAMING_SNAKE_CASE_		) < 2:
  raise ValueError("Minimum class count error"		)
 if n_train < n_classes:
  raise ValueError(
      "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)		)
 if n_test < n_classes:
  raise ValueError(
      "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)		)
 UpperCAmelCase__								=  np.split(np.argsort(SCREAMING_SNAKE_CASE_  ,							kind="mergesort"		)  ,							np.cumsum(SCREAMING_SNAKE_CASE_		)[:-1]		)
 for _ in range(SCREAMING_SNAKE_CASE_		):
  UpperCAmelCase__								=  approximate_mode(SCREAMING_SNAKE_CASE_  ,							SCREAMING_SNAKE_CASE_  ,							SCREAMING_SNAKE_CASE_		)
  UpperCAmelCase__								=  class_counts - n_i
  UpperCAmelCase__								=  approximate_mode(SCREAMING_SNAKE_CASE_  ,							SCREAMING_SNAKE_CASE_  ,							SCREAMING_SNAKE_CASE_		)

  UpperCAmelCase__								=  []
  UpperCAmelCase__								=  []

  for i in range(SCREAMING_SNAKE_CASE_		):
   UpperCAmelCase__								=  rng.permutation(class_counts[i]		)
   UpperCAmelCase__								=  class_indices[i].take(SCREAMING_SNAKE_CASE_  ,							mode="clip"		)
   train.extend(perm_indices_class_i[: n_i[i]]		)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]		)
  UpperCAmelCase__								=  rng.permutation(SCREAMING_SNAKE_CASE_		)
  UpperCAmelCase__								=  rng.permutation(SCREAMING_SNAKE_CASE_		)

  yield train, test











_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				float		):




 """simple docstring"""







 if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
  UpperCAmelCase__								=  (
      F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
  )
  raise ValueError(_lowerCAmelCase		)
 return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





