






'''simple docstring'''






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	(					)     ->			Dict:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			Dataset.from_dict({'i': range(17	)}	)
   __snake_case					:   int  				=			len(__lowerCAmelCase	)
   __snake_case					:   Dict  				=			3
   __snake_case					:   str  				=			[
       split_dataset_by_node(__lowerCAmelCase	,		rank=__lowerCAmelCase	,		world_size=__lowerCAmelCase	) for rank in range(__lowerCAmelCase	)
   ]
   assert sum(len(__lowerCAmelCase	) 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	(					)     ->			List[str]:







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

   __snake_case					:   Any  				=			3
   __snake_case					:   Any  				=			IterableDataset.from_generator(__lowerCAmelCase	)
   __snake_case					:   str  				=			len(list(__lowerCAmelCase	)	)
   __snake_case					:   Tuple  				=			[
       split_dataset_by_node(__lowerCAmelCase	,		rank=__lowerCAmelCase	,		world_size=__lowerCAmelCase	) for rank in range(__lowerCAmelCase	)
   ]
   assert sum(len(list(__lowerCAmelCase	)	) 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	(					A	:							Optional[Any]	)     ->			Union[str, Any]:







   """simple docstring"""
   def gen(A	:							int	):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(17	))

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







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

   __snake_case					:   List[str]  				=			2
   __snake_case					:   int  				=			IterableDataset.from_generator(__lowerCAmelCase	)
   __snake_case					:   Union[str, Any]  				=			len(list(__lowerCAmelCase	)	)

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

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




@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	(					A	:							Union[str, Any]	)     ->			List[Any]:







   """simple docstring"""
   __snake_case					:   Union[str, Any]  				=			2
   __snake_case					:   Dict  				=			get_torch_dist_unique_port()
   __snake_case					:   int  				=			Path(__lowerCAmelCase	).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
   __snake_case					:   List[Any]  				=			F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
   __snake_case					:   List[str]  				=			F"""
        --streaming={streaming}
    """.split()
   __snake_case					:   Tuple  				=			[sys.executable] + distributed_args + args
   execute_subprocess_async(__lowerCAmelCase	,		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	(					A	:							int	,		A	:							int	)     ->			List[Any]:







   """simple docstring"""
   __snake_case					:   Tuple  				=			True
   __snake_case					:   int  				=			get_torch_dist_unique_port()
   __snake_case					:   Optional[int]  				=			Path(__lowerCAmelCase	).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
   __snake_case					:   List[str]  				=			F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
   __snake_case					:   int  				=			F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
   __snake_case					:   Optional[int]  				=			[sys.executable] + distributed_args + args
   execute_subprocess_async(__lowerCAmelCase	,		env=os.environ.copy()	)



'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



