





'''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      UpperCamelCase__   (	):





						"""simple docstring"""


						_lowerCAmelCase							  =							Dataset.from_dict({"""i""": range(17						)}						)
						_lowerCAmelCase							  =							len(_A						)
						_lowerCAmelCase							  =							3
						_lowerCAmelCase							  =							[
						    split_dataset_by_node(_A							,			rank=_A							,			world_size=_A						) for rank in range(_A						)
						]
						assert sum(len(_A						) 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__   (	):





						"""simple docstring"""


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

						_lowerCAmelCase							  =							3
						_lowerCAmelCase							  =							IterableDataset.from_generator(_A						)
						_lowerCAmelCase							  =							len(list(_A						)						)
						_lowerCAmelCase							  =							[
						    split_dataset_by_node(_A							,			rank=_A							,			world_size=_A						) for rank in range(_A						)
						]
						assert sum(len(list(_A						)						) 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__   (	lowerCAmelCase						):





						"""simple docstring"""


						def gen(lowerCAmelCase						):
												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(_A						)]}
						_lowerCAmelCase							  =							IterableDataset.from_generator(_A							,			gen_kwargs=_A						)
						_lowerCAmelCase							  =							len(list(_A						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_lowerCAmelCase							  =							[
						    split_dataset_by_node(_A							,			rank=_A							,			world_size=_A						) for rank in range(_A						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(_A						)						) 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__   (	):





						"""simple docstring"""


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

						_lowerCAmelCase							  =							2
						_lowerCAmelCase							  =							IterableDataset.from_generator(_A						)
						_lowerCAmelCase							  =							len(list(_A						)						)

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

						_lowerCAmelCase							  =							split_dataset_by_node(full_ds.shuffle(seed=42						)							,			rank=0							,			world_size=_A						)
						assert len(list(_A						)						) == 1 + full_size // world_size
						with pytest.raises(_A						):
												split_dataset_by_node(full_ds.shuffle()							,			rank=0							,			world_size=_A						)
@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__   (	lowerCAmelCase						):





						"""simple docstring"""


						_lowerCAmelCase							  =							2
						_lowerCAmelCase							  =							get_torch_dist_unique_port()
						_lowerCAmelCase							  =							Path(_A						).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(_A							,			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__   (	lowerCAmelCase							,			lowerCAmelCase						):





						"""simple docstring"""


						_lowerCAmelCase							  =							True
						_lowerCAmelCase							  =							get_torch_dist_unique_port()
						_lowerCAmelCase							  =							Path(_A						).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(_A							,			env=os.environ.copy()						)













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     str          =[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






