




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   lowerCAmelCase						(						):




	"""simple docstring"""







	UpperCAmelCase__								=  Dataset.from_dict({"i": range(17		)}		)
	UpperCAmelCase__								=  len(__UpperCamelCase		)
	UpperCAmelCase__								=  3
	UpperCAmelCase__								=  [
	    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   lowerCAmelCase						(						):




	"""simple docstring"""







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

	UpperCAmelCase__								=  3
	UpperCAmelCase__								=  IterableDataset.from_generator(__UpperCamelCase		)
	UpperCAmelCase__								=  len(list(__UpperCamelCase		)		)
	UpperCAmelCase__								=  [
	    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   lowerCAmelCase						(						_lowerCAmelCase							:				Any		):




	"""simple docstring"""







	def gen(_lowerCAmelCase							:				List[str]		):
		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(__UpperCamelCase		)]}
	UpperCAmelCase__								=  IterableDataset.from_generator(__UpperCamelCase  ,							gen_kwargs=__UpperCamelCase		)
	UpperCAmelCase__								=  len(list(__UpperCamelCase		)		)
	assert full_ds.n_shards == world_size * shards_per_node
	UpperCAmelCase__								=  [
	    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   lowerCAmelCase						(						):




	"""simple docstring"""







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

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

	UpperCAmelCase__								=  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()

	UpperCAmelCase__								=  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   lowerCAmelCase						(						_lowerCAmelCase							:				str		):




	"""simple docstring"""







	UpperCAmelCase__								=  2
	UpperCAmelCase__								=  get_torch_dist_unique_port()
	UpperCAmelCase__								=  Path(__UpperCamelCase		).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
	UpperCAmelCase__								=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	UpperCAmelCase__								=  F'''
        --streaming={streaming}
    '''.split()
	UpperCAmelCase__								=  [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   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str		):




	"""simple docstring"""







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











def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 if a < b:
  return gcd(_lowerCAmelCase  ,							_lowerCAmelCase		)
 if a % b == 0:
  return b
 return gcd(_lowerCAmelCase  ,							a % b		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 if y == 0:
  return 1
 UpperCAmelCase__								=  power(_lowerCAmelCase  ,							y // 2  ,							_lowerCAmelCase		) % mod
 UpperCAmelCase__								=  (temp * temp) % mod
 if y % 2 == 1:
  UpperCAmelCase__								=  (temp * x) % mod
 return temp



def   lowerCAmelCase						(						_lowerCAmelCase							:				int		):




 """simple docstring"""







 UpperCAmelCase__								=  2
 while b < n:
  if gcd(_lowerCAmelCase  ,							_lowerCAmelCase		) == 1 and power(_lowerCAmelCase  ,							n - 1  ,							_lowerCAmelCase		) != 1:
   return False
  b += 1
 return True


if __name__ == "__main__":
  _lowerCAmelCase  :				Dict						=				int(input("Enter number: ").strip())
  if is_carmichael_number(number):
    print(F'''{number} is a Carmichael Number.''')
  else:
    print(F'''{number} is not a Carmichael Number.''')





