
'''simple docstring'''





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			('_front', '_back', '_len')



  @dataclass
  class 			lowerCAmelCase__ :



    '''simple docstring'''






    _UpperCamelCase							=			None
    _UpperCamelCase							=			None
    _UpperCamelCase							=			None



  class 			lowerCAmelCase__ :



    '''simple docstring'''






    _UpperCamelCase							=			('_cur',)

    def __init__(self	:			Tuple						,			lowerCAmelCase_	:			Deque._Node | None     ) ->							None:
          snake_case_    							=				cur

    def __iter__(self	:			Union[str, Any]     ) ->							Deque._Iterator:
          return self


    def 		_lowerCamelCase						(self	:			Tuple     ) ->							Any:
          if self._cur is None:
                # finished iterating
                raise StopIteration
          snake_case_    							=				self._cur.val
          snake_case_    							=				self._cur.next_node

          return val

  def __init__(self	:			Dict						,			lowerCAmelCase_	:			Iterable[Any] | None = None     ) ->							None:
        snake_case_    							=				None
        snake_case_    							=				None
        snake_case_    							=				0

        if iterable is not None:
              # append every value to the deque
              for val in iterable:
                    self.append(lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Any     ) ->							None:
        snake_case_    							=				self._Node(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )
        if self.is_empty():
              # front = back
              snake_case_    							=				snake_case_    							=				node
              snake_case_    							=				1
        else:
              # connect nodes
              snake_case_    							=				node
              snake_case_    							=				self._back
              snake_case_    							=				node  # assign new back to the new node

              self._len += 1

              # make sure there were no errors
              assert not self.is_empty(), "Error on appending value."

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			Any     ) ->							None:
        snake_case_    							=				self._Node(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )
        if self.is_empty():
              # front = back
              snake_case_    							=				snake_case_    							=				node
              snake_case_    							=				1
        else:
              # connect nodes
              snake_case_    							=				self._front
              snake_case_    							=				node
              snake_case_    							=				node  # assign new front to the new node

              self._len += 1

              # make sure there were no errors
              assert not self.is_empty(), "Error on appending value."

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Iterable[Any]     ) ->							None:
        for val in iterable:
              self.append(lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			Iterable[Any]     ) ->							None:
        for val in iterable:
              self.appendleft(lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							Any:
        assert not self.is_empty(), "Deque is empty."

        snake_case_    							=				self._back
        snake_case_    							=				self._back.prev_node  # set new back
        # drop the last node - python will deallocate memory automatically
        snake_case_    							=				None

        self._len -= 1

        return topop.val

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Any:
        assert not self.is_empty(), "Deque is empty."

        snake_case_    							=				self._front
        snake_case_    							=				self._front.next_node  # set new front and drop the first node
        snake_case_    							=				None

        self._len -= 1

        return topop.val

  def 		_lowerCamelCase						(self	:			str     ) ->							bool:
        return self._front is None

  def __len__(self	:			Optional[int]     ) ->							int:
        return self._len

  def __eq__(self	:			str						,			lowerCAmelCase_	:			object     ) ->							bool:

        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented

        snake_case_    							=				self._front
        snake_case_    							=				other._front

        # if the length of the dequeues are not the same, they are not equal
        if len(self     ) != len(lowerCAmelCase_     ):
              return False

        while me is not None and oth is not None:
              # compare every value
              if me.val != oth.val:
                    return False
              snake_case_    							=				me.next_node
              snake_case_    							=				oth.next_node

        return True

  def __iter__(self	:			Tuple     ) ->							Deque._Iterator:
        return Deque._Iterator(self._front     )


  def __repr__(self	:			Tuple     ) ->							str:
        snake_case_    							=				[]
        snake_case_    							=				self._front
        while aux is not None:
              # append the values in a list to display
              values_list.append(aux.val     )
              snake_case_    							=				aux.next_node

        return f'''[{', '.join(repr(lowerCAmelCase_     ) for val in values_list     )}]'''


if __name__ == "__main__":
     import doctest

     doctest.testmod()



'''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()      )

