




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      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       =Dataset.from_dict({"""i""": range(17     )}     )
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =3
    SCREAMING_SNAKE_CASE__       =[
        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      UpperCAmelCase_   (     ):
    def gen():
        return ({"i": i} for i in range(17     ))

    SCREAMING_SNAKE_CASE__       =3
    SCREAMING_SNAKE_CASE__       =IterableDataset.from_generator(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =len(list(__UpperCamelCase     )     )
    SCREAMING_SNAKE_CASE__       =[
        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      UpperCAmelCase_   (     __UpperCamelCase     ):
    def gen(__UpperCamelCase     ):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17     ))

    SCREAMING_SNAKE_CASE__       =3
    SCREAMING_SNAKE_CASE__       =shards_per_node * world_size
    SCREAMING_SNAKE_CASE__       ={"""shards""": [f"""shard_{shard_idx}.txt""" for shard_idx in range(__UpperCamelCase     )]}
    SCREAMING_SNAKE_CASE__       =IterableDataset.from_generator(__UpperCamelCase,    gen_kwargs=__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =len(list(__UpperCamelCase     )     )
    assert full_ds.n_shards == world_size * shards_per_node
    SCREAMING_SNAKE_CASE__       =[
        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      UpperCAmelCase_   (     ):
    def gen():
        return ({"i": i} for i in range(17     ))

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

    SCREAMING_SNAKE_CASE__       =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()

    SCREAMING_SNAKE_CASE__       =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      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =2
    SCREAMING_SNAKE_CASE__       =get_torch_dist_unique_port()
    SCREAMING_SNAKE_CASE__       =Path(__UpperCamelCase     ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
    SCREAMING_SNAKE_CASE__       =f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    SCREAMING_SNAKE_CASE__       =f"""
        --streaming={streaming}
    """.split()
    SCREAMING_SNAKE_CASE__       =[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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =True
    SCREAMING_SNAKE_CASE__       =get_torch_dist_unique_port()
    SCREAMING_SNAKE_CASE__       =Path(__UpperCamelCase     ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
    SCREAMING_SNAKE_CASE__       =f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    SCREAMING_SNAKE_CASE__       =f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
    SCREAMING_SNAKE_CASE__       =[sys.executable] + distributed_args + args
    execute_subprocess_async(__UpperCamelCase,    env=os.environ.copy()     )












def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    # Create a stack and push index of fist element to it
    SCREAMING_SNAKE_CASE__       =[]
    st.append(0     )

    # Span value of first element is always 1
    SCREAMING_SNAKE_CASE__       =1

    # Calculate span values for rest of the elements
    for i in range(1,    __UpperCamelCase     ):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(__UpperCamelCase     ) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        SCREAMING_SNAKE_CASE__       =i + 1 if len(__UpperCamelCase     ) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    for i in range(0,    __UpperCamelCase     ):
        print(arr[i],    end=""" """     )


# Driver program to test above function
lowerCamelCase_  						=       [10, 4, 5, 90, 120, 80]
lowerCamelCase_  						=       [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)




print_array(S, len(price))






