


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_  (				)->      int:




						'''simple docstring'''
						__magic_name__ 		=			Dataset.from_dict({'i': range(17     )}     )
						__magic_name__ 		=			len(__snake_case     )
						__magic_name__ 		=			3
						__magic_name__ 		=			[
						    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 							UpperCamelCase_  (				)->      Optional[int]:




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

						__magic_name__ 		=			3
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )
						__magic_name__ 		=			[
						    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 							UpperCamelCase_  (				__snake_case			:						Dict     )->      Dict:




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

						__magic_name__ 		=			3
						__magic_name__ 		=			shards_per_node * world_size
						__magic_name__ 		=			{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(__snake_case     )]}
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case	,			gen_kwargs=__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )
						assert full_ds.n_shards == world_size * shards_per_node
						__magic_name__ 		=			[
						    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 							UpperCamelCase_  (				)->      Dict:




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

						__magic_name__ 		=			2
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )

						__magic_name__ 		=			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()

						__magic_name__ 		=			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 							UpperCamelCase_  (				__snake_case			:						Dict     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			2
						__magic_name__ 		=			get_torch_dist_unique_port()
						__magic_name__ 		=			Path(__snake_case     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						__magic_name__ 		=			F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						__magic_name__ 		=			F'''
        --streaming={streaming}
    '''.split()
						__magic_name__ 		=			[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 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						int     )->      Union[str, Any]:




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





from typing import TYPE_CHECKING

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


UpperCamelCase :				str						    =       {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[int]						    =       ['''BartTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Any						    =       [
					    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''BartForCausalLM''',
					    '''BartForConditionalGeneration''',
					    '''BartForQuestionAnswering''',
					    '''BartForSequenceClassification''',
					    '''BartModel''',
					    '''BartPretrainedModel''',
					    '''PretrainedBartModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Tuple						    =       [
					    '''TFBartForConditionalGeneration''',
					    '''TFBartForSequenceClassification''',
					    '''TFBartModel''',
					    '''TFBartPretrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				List[Any]						    =       [
					    '''FlaxBartDecoderPreTrainedModel''',
					    '''FlaxBartForCausalLM''',
					    '''FlaxBartForConditionalGeneration''',
					    '''FlaxBartForQuestionAnswering''',
					    '''FlaxBartForSequenceClassification''',
					    '''FlaxBartModel''',
					    '''FlaxBartPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
					from .tokenization_bart import BartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bart_fast import BartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bart import (
										    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BartForCausalLM,
										    BartForConditionalGeneration,
										    BartForQuestionAnswering,
										    BartForSequenceClassification,
										    BartModel,
										    BartPretrainedModel,
										    PretrainedBartModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bart import (
										    TFBartForConditionalGeneration,
										    TFBartForSequenceClassification,
										    TFBartModel,
										    TFBartPretrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bart import (
										    FlaxBartDecoderPreTrainedModel,
										    FlaxBartForCausalLM,
										    FlaxBartForConditionalGeneration,
										    FlaxBartForQuestionAnswering,
										    FlaxBartForSequenceClassification,
										    FlaxBartModel,
										    FlaxBartPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase :				Optional[int]						    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

