





"""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_   (       )->		Optional[int]:
       _lowerCamelCase    	=					Dataset.from_dict({'i': range(17		)}		)
       _lowerCamelCase    	=					len(snake_case		)
       _lowerCamelCase    	=					3
       _lowerCamelCase    	=					[
           split_dataset_by_node(snake_case						,  rank=snake_case						,  world_size=snake_case		) for rank in range(snake_case		)
       ]
       assert sum(len(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 SCREAMING_SNAKE_CASE_   (       )->		List[Any]:
       def gen():
              return ({"i": i} for i in range(17		))

       _lowerCamelCase    	=					3
       _lowerCamelCase    	=					IterableDataset.from_generator(snake_case		)
       _lowerCamelCase    	=					len(list(snake_case		)		)
       _lowerCamelCase    	=					[
           split_dataset_by_node(snake_case						,  rank=snake_case						,  world_size=snake_case		) for rank in range(snake_case		)
       ]
       assert sum(len(list(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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]		)->		Union[str, Any]:
       def gen(snake_case   :					Optional[int]		):
              for shard in shards:
                     yield from ({"i": i, "shard": shard} for i in range(17		))

       _lowerCamelCase    	=					3
       _lowerCamelCase    	=					shards_per_node * world_size
       _lowerCamelCase    	=					{'shards': [f'shard_{shard_idx}.txt' for shard_idx in range(snake_case		)]}
       _lowerCamelCase    	=					IterableDataset.from_generator(snake_case						,  gen_kwargs=snake_case		)
       _lowerCamelCase    	=					len(list(snake_case		)		)
       assert full_ds.n_shards == world_size * shards_per_node
       _lowerCamelCase    	=					[
           split_dataset_by_node(snake_case						,  rank=snake_case						,  world_size=snake_case		) for rank in range(snake_case		)
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(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 SCREAMING_SNAKE_CASE_   (       )->		List[str]:
       def gen():
              return ({"i": i} for i in range(17		))

       _lowerCamelCase    	=					2
       _lowerCamelCase    	=					IterableDataset.from_generator(snake_case		)
       _lowerCamelCase    	=					len(list(snake_case		)		)

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

       _lowerCamelCase    	=					split_dataset_by_node(full_ds.shuffle(seed=42		)						,  rank=0						,  world_size=snake_case		)
       assert len(list(snake_case		)		) == 1 + full_size // world_size
       with pytest.raises(snake_case		):
              split_dataset_by_node(full_ds.shuffle()						,  rank=0						,  world_size=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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple		)->		Optional[Any]:
       _lowerCamelCase    	=					2
       _lowerCamelCase    	=					get_torch_dist_unique_port()
       _lowerCamelCase    	=					Path(snake_case		).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
       _lowerCamelCase    	=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
       _lowerCamelCase    	=					f'\n        --streaming={streaming}\n    '.split()
       _lowerCamelCase    	=					[sys.executable] + distributed_args + args
       execute_subprocess_async(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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[Any]						,  snake_case   :					List[Any]		)->		int:
       _lowerCamelCase    	=					True
       _lowerCamelCase    	=					get_torch_dist_unique_port()
       _lowerCamelCase    	=					Path(snake_case		).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
       _lowerCamelCase    	=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
       _lowerCamelCase    	=					f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
       _lowerCamelCase    	=					[sys.executable] + distributed_args + args
       execute_subprocess_async(snake_case						,  env=os.environ.copy()		)













"""simple docstring"""


import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					bool = False		)->		Any:
       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(snake_case		)
       np.random.seed(snake_case		)
       torch.manual_seed(snake_case		)
       if is_xpu_available():
              torch.xpu.manual_seed_all(snake_case		)
       elif is_npu_available():
              torch.npu.manual_seed_all(snake_case		)
       else:
              torch.cuda.manual_seed_all(snake_case		)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[RNGType] = None						,  snake_case   :					Optional[torch.Generator] = None		)->		str:
       # Get the proper rng state
       if rng_type == RNGType.TORCH:
              _lowerCamelCase    	=					torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              _lowerCamelCase    	=					torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              _lowerCamelCase    	=					torch.tensor(xm.get_rng_state()		)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              _lowerCamelCase    	=					torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              _lowerCamelCase    	=					torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              _lowerCamelCase    	=					generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       _lowerCamelCase    	=					AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              _lowerCamelCase    	=					rng_state.to(xm.xla_device()		)
              xm.collective_broadcast([rng_state]		)
              xm.mark_step()
              _lowerCamelCase    	=					rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              _lowerCamelCase    	=					rng_state.to(state.device		)
              torch.distributed.broadcast(snake_case						,  0		)
              _lowerCamelCase    	=					rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(snake_case						,  0		)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(snake_case		)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(snake_case		)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()		)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Union[str, RNGType]]						,  snake_case   :					Optional[torch.Generator] = None		)->		int:
       for rng_type in rng_types:
              synchronize_rng_state(RNGType(snake_case		)						,  generator=snake_case		)






