





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  (		)  ->						Dict:


			'''simple docstring'''







			__A				 =       Dataset.from_dict({'''i''': range(1_7     )}     )
			__A				 =       len(_SCREAMING_SNAKE_CASE     )
			__A				 =       3
			__A				 =       [
			    split_dataset_by_node(_SCREAMING_SNAKE_CASE    ,	rank=_SCREAMING_SNAKE_CASE    ,	world_size=_SCREAMING_SNAKE_CASE     ) for rank in range(_SCREAMING_SNAKE_CASE     )
			]
			assert sum(len(_SCREAMING_SNAKE_CASE     ) 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  (		)  ->						Optional[Any]:


			'''simple docstring'''







			def gen():
						return ({"i": i} for i in range(1_7     ))

			__A				 =       3
			__A				 =       IterableDataset.from_generator(_SCREAMING_SNAKE_CASE     )
			__A				 =       len(list(_SCREAMING_SNAKE_CASE     )     )
			__A				 =       [
			    split_dataset_by_node(_SCREAMING_SNAKE_CASE    ,	rank=_SCREAMING_SNAKE_CASE    ,	world_size=_SCREAMING_SNAKE_CASE     ) for rank in range(_SCREAMING_SNAKE_CASE     )
			]
			assert sum(len(list(_SCREAMING_SNAKE_CASE     )     ) 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  (		snake_case     )  ->						Optional[Any]:


			'''simple docstring'''







			def gen(snake_case     ):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(1_7     ))

			__A				 =       3
			__A				 =       shards_per_node * world_size
			__A				 =       {"shards": [F"shard_{shard_idx}.txt" for shard_idx in range(_SCREAMING_SNAKE_CASE     )]}
			__A				 =       IterableDataset.from_generator(_SCREAMING_SNAKE_CASE    ,	gen_kwargs=_SCREAMING_SNAKE_CASE     )
			__A				 =       len(list(_SCREAMING_SNAKE_CASE     )     )
			assert full_ds.n_shards == world_size * shards_per_node
			__A				 =       [
			    split_dataset_by_node(_SCREAMING_SNAKE_CASE    ,	rank=_SCREAMING_SNAKE_CASE    ,	world_size=_SCREAMING_SNAKE_CASE     ) for rank in range(_SCREAMING_SNAKE_CASE     )
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(_SCREAMING_SNAKE_CASE     )     ) 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  (		)  ->						List[str]:


			'''simple docstring'''







			def gen():
						return ({"i": i} for i in range(1_7     ))

			__A				 =       2
			__A				 =       IterableDataset.from_generator(_SCREAMING_SNAKE_CASE     )
			__A				 =       len(list(_SCREAMING_SNAKE_CASE     )     )

			__A				 =       split_dataset_by_node(_SCREAMING_SNAKE_CASE    ,	rank=0    ,	world_size=_SCREAMING_SNAKE_CASE     ).shuffle(seed=4_2     )
			assert len(list(_SCREAMING_SNAKE_CASE     )     ) == 1 + full_size // world_size
			with pytest.raises(_SCREAMING_SNAKE_CASE     ):
						split_dataset_by_node(_SCREAMING_SNAKE_CASE    ,	rank=0    ,	world_size=_SCREAMING_SNAKE_CASE     ).shuffle()

			__A				 =       split_dataset_by_node(full_ds.shuffle(seed=4_2     )    ,	rank=0    ,	world_size=_SCREAMING_SNAKE_CASE     )
			assert len(list(_SCREAMING_SNAKE_CASE     )     ) == 1 + full_size // world_size
			with pytest.raises(_SCREAMING_SNAKE_CASE     ):
						split_dataset_by_node(full_ds.shuffle()    ,	rank=0    ,	world_size=_SCREAMING_SNAKE_CASE     )
@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  (		snake_case     )  ->						Union[str, Any]:


			'''simple docstring'''







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


			'''simple docstring'''







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








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

   for k in range(t - 1     ):
      # select a column of modified matrix A':
      __A				 =       r[k:, [k]]
      # construct first basis vector
      __A				 =       np.zeros_like(snake_case     )
      __A				 =       1.0
      # determine scaling factor
      __A				 =       np.linalg.norm(snake_case     )
      # construct vector v for Householder reflection
      __A				 =       x + np.sign(x[0]     ) * alpha * ea
      v /= np.linalg.norm(snake_case     )

      # construct the Householder matrix
      __A				 =       np.eye(m - k     ) - 2.0 * v @ v.T
      # pad with ones and zeros as necessary
      __A				 =       np.block([[np.eye(snake_case     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

