
'''simple docstring'''





import os
import sys
import time

from . import transposition_cipher as trans_cipher
def 	snake_case_							(				):
      snake_case_    							=				"Prehistoric Men.txt"
      snake_case_    							=				"Output.txt"
      snake_case_    							=				int(input("Enter key: "      )      )
      snake_case_    							=				input("Encrypt/Decrypt [e/d]: "      )

      if not os.path.exists(_a      ):
            print(F'''File {input_file} does not exist. Quitting...'''      )
            sys.exit()
      if os.path.exists(_a      ):
            print(F'''Overwrite {output_file}? [y/n]'''      )
            snake_case_    							=				input("> "      )
            if not response.lower().startswith("y"      ):
                  sys.exit()

      snake_case_    							=				time.time()
      if mode.lower().startswith("e"      ):
            with open(_a      ) as f:
                  snake_case_    							=				f.read()
            snake_case_    							=				trans_cipher.encrypt_message(_a			,       _a      )
      elif mode.lower().startswith("d"      ):
            with open(_a      ) as f:
                  snake_case_    							=				f.read()
            snake_case_    							=				trans_cipher.decrypt_message(_a			,       _a      )

      with open(_a			,       "w"      ) as output_obj:
            output_obj.write(_a      )

      snake_case_    							=				round(time.time() - start_time			,       2      )
      print(("Done (", total_time, "seconds )")      )


if __name__ == "__main__":
     main()



'''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 	snake_case_							(				):
      snake_case_    							=				Dataset.from_dict({"i": range(17      )}      )
      snake_case_    							=				len(_a      )
      snake_case_    							=				3
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(_a      ) 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 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				IterableDataset.from_generator(_a      )
      snake_case_    							=				len(list(_a      )      )
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(list(_a      )      ) 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 	snake_case_							(				_a  :				List[Any]      ):
      def gen(_a  :				Dict      ):
            for shard in shards:
                  yield from ({"i": i, "shard": shard} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				shards_per_node * world_size
      snake_case_    							=				{"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(_a      )]}
      snake_case_    							=				IterableDataset.from_generator(_a			,       gen_kwargs=_a      )
      snake_case_    							=				len(list(_a      )      )
      assert full_ds.n_shards == world_size * shards_per_node
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
      assert sum(len(list(_a      )      ) 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 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

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

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

      snake_case_    							=				split_dataset_by_node(full_ds.shuffle(seed=42      )			,       rank=0			,       world_size=_a      )
      assert len(list(_a      )      ) == 1 + full_size // world_size
      with pytest.raises(_a      ):
            split_dataset_by_node(full_ds.shuffle()			,       rank=0			,       world_size=_a      )
@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 	snake_case_							(				_a  :				Any      ):
      snake_case_    							=				2
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       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 	snake_case_							(				_a  :				int			,       _a  :				List[Any]      ):
      snake_case_    							=				True
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       env=os.environ.copy()      )

