


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_	(    )    ->							int:
       lowercase__:  Optional[Any]         =  Dataset.from_dict({'i': range(17	)}	)
       lowercase__:  int         =  len(UpperCAmelCase__	)
       lowercase__:  Optional[int]         =  3
       lowercase__:  Any         =  [
           split_dataset_by_node(UpperCAmelCase__     ,				rank=UpperCAmelCase__     ,				world_size=UpperCAmelCase__	) for rank in range(UpperCAmelCase__	)
       ]
       assert sum(len(UpperCAmelCase__	) 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_	(    )    ->							List[str]:

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

       lowercase__:  str         =  3
       lowercase__:  Tuple         =  IterableDataset.from_generator(UpperCAmelCase__	)
       lowercase__:  Dict         =  len(list(UpperCAmelCase__	)	)
       lowercase__:  Union[str, Any]         =  [
           split_dataset_by_node(UpperCAmelCase__     ,				rank=UpperCAmelCase__     ,				world_size=UpperCAmelCase__	) for rank in range(UpperCAmelCase__	)
       ]
       assert sum(len(list(UpperCAmelCase__	)	) 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_	(    snake_case	)    ->							Dict:

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

       lowercase__:  Optional[int]         =  3
       lowercase__:  Dict         =  shards_per_node * world_size
       lowercase__:  List[str]         =  {'shards': [f'shard_{shard_idx}.txt' for shard_idx in range(UpperCAmelCase__	)]}
       lowercase__:  Optional[Any]         =  IterableDataset.from_generator(UpperCAmelCase__     ,				gen_kwargs=UpperCAmelCase__	)
       lowercase__:  Dict         =  len(list(UpperCAmelCase__	)	)
       assert full_ds.n_shards == world_size * shards_per_node
       lowercase__:  Optional[int]         =  [
           split_dataset_by_node(UpperCAmelCase__     ,				rank=UpperCAmelCase__     ,				world_size=UpperCAmelCase__	) for rank in range(UpperCAmelCase__	)
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(UpperCAmelCase__	)	) 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_	(    )    ->							List[str]:

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

       lowercase__:  Any         =  2
       lowercase__:  List[str]         =  IterableDataset.from_generator(UpperCAmelCase__	)
       lowercase__:  List[str]         =  len(list(UpperCAmelCase__	)	)

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

       lowercase__:  Any         =  split_dataset_by_node(full_ds.shuffle(seed=42	)     ,				rank=0     ,				world_size=UpperCAmelCase__	)
       assert len(list(UpperCAmelCase__	)	) == 1 + full_size // world_size
       with pytest.raises(UpperCAmelCase__	):
              split_dataset_by_node(full_ds.shuffle()     ,				rank=0     ,				world_size=UpperCAmelCase__	)




@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_	(    snake_case	)    ->							List[str]:
       lowercase__:  Optional[int]         =  2
       lowercase__:  Dict         =  get_torch_dist_unique_port()
       lowercase__:  str         =  Path(UpperCAmelCase__	).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
       lowercase__:  Tuple         =  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
       lowercase__:  str         =  f'\n        --streaming={streaming}\n    '.split()
       lowercase__:  List[Any]         =  [sys.executable] + distributed_args + args
       execute_subprocess_async(UpperCAmelCase__     ,				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_	(    snake_case     ,				snake_case	)    ->							Any:
       lowercase__:  Optional[int]         =  True
       lowercase__:  List[Any]         =  get_torch_dist_unique_port()
       lowercase__:  List[str]         =  Path(UpperCAmelCase__	).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
       lowercase__:  Tuple         =  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
       lowercase__:  Tuple         =  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
       lowercase__:  Optional[int]         =  [sys.executable] + distributed_args + args
       execute_subprocess_async(UpperCAmelCase__     ,				env=os.environ.copy()	)







from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

 if not all((nth_term, start_term_a, common_ratio_r)       ):
  return []
 a							=		[]
 a							=		1
 a							=		common_ratio_r
 for _ in range(int(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[int]										=  float(
         input('''Enter the common ratio between two terms (r) of the Geometric Series''')
     )
     print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))

