


"""simple docstring"""






import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def    A_ ( ):






     """simple docstring"""

     _a     						=					Dataset.from_dict({'''i''': range(17							)}							)
     _a     						=					len(_lowerCAmelCase							)
     _a     						=					3
     _a     						=					[
         split_dataset_by_node(_lowerCAmelCase,  rank=_lowerCAmelCase,  world_size=_lowerCAmelCase							) for rank in range(_lowerCAmelCase							)
     ]
     assert sum(len(_lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
     assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size
def    A_ ( ):






     """simple docstring"""

     def gen():
          return ({"i": i} for i in range(17							))

     _a     						=					3
     _a     						=					IterableDataset.from_generator(_lowerCAmelCase							)
     _a     						=					len(list(_lowerCAmelCase							)							)
     _a     						=					[
         split_dataset_by_node(_lowerCAmelCase,  rank=_lowerCAmelCase,  world_size=_lowerCAmelCase							) for rank in range(_lowerCAmelCase							)
     ]
     assert sum(len(list(_lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
     assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size
@pytest.mark.parametrize('''shards_per_node''',  [1, 2, 3]							)
def    A_ ( _lowerCAmelCase  :       Any							):






     """simple docstring"""

     def gen(_lowerCAmelCase  :       Any							):
          for shard in shards:
               yield from ({"i": i, "shard": shard} for i in range(17							))

     _a     						=					3
     _a     						=					shards_per_node * world_size
     _a     						=					{'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowerCAmelCase							)]}
     _a     						=					IterableDataset.from_generator(_lowerCAmelCase,  gen_kwargs=_lowerCAmelCase							)
     _a     						=					len(list(_lowerCAmelCase							)							)
     assert full_ds.n_shards == world_size * shards_per_node
     _a     						=					[
         split_dataset_by_node(_lowerCAmelCase,  rank=_lowerCAmelCase,  world_size=_lowerCAmelCase							) for rank in range(_lowerCAmelCase							)
     ]
     assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
     assert sum(len(list(_lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
     assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size
def    A_ ( ):






     """simple docstring"""

     def gen():
          return ({"i": i} for i in range(17							))

     _a     						=					2
     _a     						=					IterableDataset.from_generator(_lowerCAmelCase							)
     _a     						=					len(list(_lowerCAmelCase							)							)

     _a     						=					split_dataset_by_node(_lowerCAmelCase,  rank=0,  world_size=_lowerCAmelCase							).shuffle(seed=42							)
     assert len(list(_lowerCAmelCase							)							) == 1 + full_size // world_size
     with pytest.raises(_lowerCAmelCase							):
          split_dataset_by_node(_lowerCAmelCase,  rank=0,  world_size=_lowerCAmelCase							).shuffle()

     _a     						=					split_dataset_by_node(full_ds.shuffle(seed=42							),  rank=0,  world_size=_lowerCAmelCase							)
     assert len(list(_lowerCAmelCase							)							) == 1 + full_size // world_size
     with pytest.raises(_lowerCAmelCase							):
          split_dataset_by_node(full_ds.shuffle(),  rank=0,  world_size=_lowerCAmelCase							)
@pytest.mark.parametrize('''streaming''',  [False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt''',  reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     _a     						=					2
     _a     						=					get_torch_dist_unique_port()
     _a     						=					Path(_lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     _a     						=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
     _a     						=					f'\n        --streaming={streaming}\n    '.split()
     _a     						=					[sys.executable] + distributed_args + args
     execute_subprocess_async(_lowerCAmelCase,  env=os.environ.copy()							)

@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''',  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ],  )
@require_torch
@pytest.mark.skipif(os.name == '''nt''',  reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def    A_ ( _lowerCAmelCase  :       int,  _lowerCAmelCase  :       List[Any]							):






     """simple docstring"""

     _a     						=					True
     _a     						=					get_torch_dist_unique_port()
     _a     						=					Path(_lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     _a     						=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
     _a     						=					f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
     _a     						=					[sys.executable] + distributed_args + args
     execute_subprocess_async(_lowerCAmelCase,  env=os.environ.copy()							)



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')