from typing import List

import numpy as np


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {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."
                )				)
      SCREAMING_SNAKE_CASE_          =  max(lists_lengths.values()     ,      default=0				)
      return max(1     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      for group_idx in range(__UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            SCREAMING_SNAKE_CASE_          =  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            SCREAMING_SNAKE_CASE_          =  range(__UpperCamelCase     ,      start + num_shards_to_add				)
            shards_indices_per_group.append(__UpperCamelCase				)
      return shards_indices_per_group


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  _number_of_shards_in_gen_kwargs(__UpperCamelCase				)
      if num_shards == 1:
            return [dict(__UpperCamelCase				)]
      else:
            SCREAMING_SNAKE_CASE_          =  _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      a__ (		__UpperCamelCase				):
      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      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {len(__UpperCamelCase				) for value in gen_kwargs.values() if isinstance(__UpperCamelCase     ,      __UpperCamelCase				)}
      SCREAMING_SNAKE_CASE_          =  {}
      for size in list_sizes:
            SCREAMING_SNAKE_CASE_          =  list(range(__UpperCamelCase				)				)
            rng.shuffle(indices_per_size[size]				)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      SCREAMING_SNAKE_CASE_          =  dict(__UpperCamelCase				)
      for key, value in shuffled_kwargs.items():
            if isinstance(__UpperCamelCase     ,      __UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  [value[i] for i in indices_per_size[len(__UpperCamelCase				)]]
      return shuffled_kwargs



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


