






from typing import List

import numpy as np

def   snake_case (UpperCAmelCase__  )	->    int:
  UpperCamelCase_: str   				=    {key: len(UpperCAmelCase__  ) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  )}
  if len(set(lists_lengths.values()  )  ) > 1:
    raise RuntimeError(
        (
            'Sharding is ambiguous for this dataset: '
            + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
            + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()  )
            + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
            + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
        )  )
  UpperCamelCase_: Optional[Any]   				=    max(lists_lengths.values()     ,   default=0  )
  return max(1     ,   UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[range]:
  UpperCamelCase_: int   				=    []
  for group_idx in range(UpperCAmelCase__  ):
    UpperCamelCase_: List[str]   				=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
    if num_shards_to_add == 0:
      break
    UpperCamelCase_: Dict   				=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
    UpperCamelCase_: Tuple   				=    range(UpperCAmelCase__     ,   start + num_shards_to_add  )
    shards_indices_per_group.append(UpperCAmelCase__  )
  return shards_indices_per_group

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[dict]:
  UpperCamelCase_: Optional[Any]   				=    _number_of_shards_in_gen_kwargs(UpperCAmelCase__  )
  if num_shards == 1:
    return [dict(UpperCAmelCase__  )]
  else:
    UpperCamelCase_: Optional[Any]   				=    _distribute_shards(num_shards=UpperCAmelCase__     ,   max_num_jobs=UpperCAmelCase__  )
    return [
        {
            key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
            if isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  )
            else value
            for key, value in gen_kwargs.items()
        }
        for group_idx in range(len(UpperCAmelCase__  )  )
    ]

def   snake_case (UpperCAmelCase__  )	->    dict:
  return {
      key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
      if isinstance(gen_kwargs_list[0][key]     ,   UpperCAmelCase__  )
      else gen_kwargs_list[0][key]
      for key in gen_kwargs_list[0]
  }

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    dict:
  UpperCamelCase_: Dict   				=    {len(UpperCAmelCase__  ) for value in gen_kwargs.values() if isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  )}
  UpperCamelCase_: Union[str, Any]   				=    {}
  for size in list_sizes:
    UpperCamelCase_: List[Any]   				=    list(range(UpperCAmelCase__  )  )
    rng.shuffle(indices_per_size[size]  )
  # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
  UpperCamelCase_: List[str]   				=    dict(UpperCAmelCase__  )
  for key, value in shuffled_kwargs.items():
    if isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  ):
      UpperCamelCase_: Dict   				=    [value[i] for i in indices_per_size[len(UpperCAmelCase__  )]]
  return shuffled_kwargs







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()