




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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Dataset.from_dict({'''i''': range(17   )}   )
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    3
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(lowercase_   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Tuple:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    shards_per_node * world_size
    __UpperCamelCase			=    {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(lowercase_   )]}
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_       ,  gen_kwargs=lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    assert full_ds.n_shards == world_size * shards_per_node
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


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

    __UpperCamelCase			=    2
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )

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

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




@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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    2
    __UpperCamelCase			=    get_torch_dist_unique_port()
    __UpperCamelCase			=    Path(lowercase_   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    __UpperCamelCase			=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
    __UpperCamelCase			=    F"\n        --streaming={streaming}\n    ".split()
    __UpperCamelCase			=    [sys.executable] + distributed_args + args
    execute_subprocess_async(lowercase_       ,  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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


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








def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





