
from typing import TYPE_CHECKING

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


__snake_case					:							Optional[int]           =							{
    'auto_factory': ['get_values'],
    'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
    'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
    'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
    'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
    'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Tuple           =							[
						    'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
						    'MODEL_FOR_BACKBONE_MAPPING',
						    'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
						    'MODEL_FOR_CAUSAL_LM_MAPPING',
						    'MODEL_FOR_CTC_MAPPING',
						    'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
						    'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
						    'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
						    'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
						    'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
						    'MODEL_FOR_MASKED_LM_MAPPING',
						    'MODEL_FOR_MASK_GENERATION_MAPPING',
						    'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
						    'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
						    'MODEL_FOR_OBJECT_DETECTION_MAPPING',
						    'MODEL_FOR_PRETRAINING_MAPPING',
						    'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
						    'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
						    'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
						    'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
						    'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
						    'MODEL_FOR_TEXT_ENCODING_MAPPING',
						    'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
						    'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_VISION_2_SEQ_MAPPING',
						    'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
						    'MODEL_MAPPING',
						    'MODEL_WITH_LM_HEAD_MAPPING',
						    'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
						    'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
						    'AutoModel',
						    'AutoBackbone',
						    'AutoModelForAudioClassification',
						    'AutoModelForAudioFrameClassification',
						    'AutoModelForAudioXVector',
						    'AutoModelForCausalLM',
						    'AutoModelForCTC',
						    'AutoModelForDepthEstimation',
						    'AutoModelForImageClassification',
						    'AutoModelForImageSegmentation',
						    'AutoModelForInstanceSegmentation',
						    'AutoModelForMaskGeneration',
						    'AutoModelForTextEncoding',
						    'AutoModelForMaskedImageModeling',
						    'AutoModelForMaskedLM',
						    'AutoModelForMultipleChoice',
						    'AutoModelForNextSentencePrediction',
						    'AutoModelForObjectDetection',
						    'AutoModelForPreTraining',
						    'AutoModelForQuestionAnswering',
						    'AutoModelForSemanticSegmentation',
						    'AutoModelForSeq2SeqLM',
						    'AutoModelForSequenceClassification',
						    'AutoModelForSpeechSeq2Seq',
						    'AutoModelForTableQuestionAnswering',
						    'AutoModelForTokenClassification',
						    'AutoModelForUniversalSegmentation',
						    'AutoModelForVideoClassification',
						    'AutoModelForVision2Seq',
						    'AutoModelForVisualQuestionAnswering',
						    'AutoModelForDocumentQuestionAnswering',
						    'AutoModelWithLMHead',
						    'AutoModelForZeroShotImageClassification',
						    'AutoModelForZeroShotObjectDetection',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Tuple           =							[
						    'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
						    'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
						    'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
						    'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
						    'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
						    'TF_MODEL_FOR_MASKED_LM_MAPPING',
						    'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
						    'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
						    'TF_MODEL_FOR_PRETRAINING_MAPPING',
						    'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
						    'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
						    'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
						    'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
						    'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
						    'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
						    'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
						    'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
						    'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
						    'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
						    'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
						    'TF_MODEL_MAPPING',
						    'TF_MODEL_WITH_LM_HEAD_MAPPING',
						    'TFAutoModel',
						    'TFAutoModelForAudioClassification',
						    'TFAutoModelForCausalLM',
						    'TFAutoModelForImageClassification',
						    'TFAutoModelForMaskedImageModeling',
						    'TFAutoModelForMaskedLM',
						    'TFAutoModelForMaskGeneration',
						    'TFAutoModelForMultipleChoice',
						    'TFAutoModelForNextSentencePrediction',
						    'TFAutoModelForPreTraining',
						    'TFAutoModelForDocumentQuestionAnswering',
						    'TFAutoModelForQuestionAnswering',
						    'TFAutoModelForSemanticSegmentation',
						    'TFAutoModelForSeq2SeqLM',
						    'TFAutoModelForSequenceClassification',
						    'TFAutoModelForSpeechSeq2Seq',
						    'TFAutoModelForTableQuestionAnswering',
						    'TFAutoModelForTextEncoding',
						    'TFAutoModelForTokenClassification',
						    'TFAutoModelForVision2Seq',
						    'TFAutoModelForZeroShotImageClassification',
						    'TFAutoModelWithLMHead',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Any           =							[
						    'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
						    'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
						    'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
						    'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
						    'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
						    'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
						    'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
						    'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
						    'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
						    'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
						    'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
						    'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
						    'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
						    'FLAX_MODEL_MAPPING',
						    'FlaxAutoModel',
						    'FlaxAutoModelForCausalLM',
						    'FlaxAutoModelForImageClassification',
						    'FlaxAutoModelForMaskedLM',
						    'FlaxAutoModelForMultipleChoice',
						    'FlaxAutoModelForNextSentencePrediction',
						    'FlaxAutoModelForPreTraining',
						    'FlaxAutoModelForQuestionAnswering',
						    'FlaxAutoModelForSeq2SeqLM',
						    'FlaxAutoModelForSequenceClassification',
						    'FlaxAutoModelForSpeechSeq2Seq',
						    'FlaxAutoModelForTokenClassification',
						    'FlaxAutoModelForVision2Seq',
						]


if TYPE_CHECKING:
						from .auto_factory import get_values
						from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
						from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
						from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
						from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
						from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_auto import (
												    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
												    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
												    MODEL_FOR_BACKBONE_MAPPING,
												    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
												    MODEL_FOR_CAUSAL_LM_MAPPING,
												    MODEL_FOR_CTC_MAPPING,
												    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
												    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
												    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
												    MODEL_FOR_MASK_GENERATION_MAPPING,
												    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
												    MODEL_FOR_MASKED_LM_MAPPING,
												    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    MODEL_FOR_OBJECT_DETECTION_MAPPING,
												    MODEL_FOR_PRETRAINING_MAPPING,
												    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
												    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_TEXT_ENCODING_MAPPING,
												    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
												    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
												    MODEL_FOR_VISION_2_SEQ_MAPPING,
												    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
												    MODEL_MAPPING,
												    MODEL_WITH_LM_HEAD_MAPPING,
												    AutoBackbone,
												    AutoModel,
												    AutoModelForAudioClassification,
												    AutoModelForAudioFrameClassification,
												    AutoModelForAudioXVector,
												    AutoModelForCausalLM,
												    AutoModelForCTC,
												    AutoModelForDepthEstimation,
												    AutoModelForDocumentQuestionAnswering,
												    AutoModelForImageClassification,
												    AutoModelForImageSegmentation,
												    AutoModelForInstanceSegmentation,
												    AutoModelForMaskedImageModeling,
												    AutoModelForMaskedLM,
												    AutoModelForMaskGeneration,
												    AutoModelForMultipleChoice,
												    AutoModelForNextSentencePrediction,
												    AutoModelForObjectDetection,
												    AutoModelForPreTraining,
												    AutoModelForQuestionAnswering,
												    AutoModelForSemanticSegmentation,
												    AutoModelForSeqaSeqLM,
												    AutoModelForSequenceClassification,
												    AutoModelForSpeechSeqaSeq,
												    AutoModelForTableQuestionAnswering,
												    AutoModelForTextEncoding,
												    AutoModelForTokenClassification,
												    AutoModelForUniversalSegmentation,
												    AutoModelForVideoClassification,
												    AutoModelForVisionaSeq,
												    AutoModelForVisualQuestionAnswering,
												    AutoModelForZeroShotImageClassification,
												    AutoModelForZeroShotObjectDetection,
												    AutoModelWithLMHead,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_auto import (
												    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
												    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
												    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
												    TF_MODEL_FOR_MASKED_LM_MAPPING,
												    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    TF_MODEL_FOR_PRETRAINING_MAPPING,
												    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
												    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
												    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
												    TF_MODEL_MAPPING,
												    TF_MODEL_WITH_LM_HEAD_MAPPING,
												    TFAutoModel,
												    TFAutoModelForAudioClassification,
												    TFAutoModelForCausalLM,
												    TFAutoModelForDocumentQuestionAnswering,
												    TFAutoModelForImageClassification,
												    TFAutoModelForMaskedImageModeling,
												    TFAutoModelForMaskedLM,
												    TFAutoModelForMaskGeneration,
												    TFAutoModelForMultipleChoice,
												    TFAutoModelForNextSentencePrediction,
												    TFAutoModelForPreTraining,
												    TFAutoModelForQuestionAnswering,
												    TFAutoModelForSemanticSegmentation,
												    TFAutoModelForSeqaSeqLM,
												    TFAutoModelForSequenceClassification,
												    TFAutoModelForSpeechSeqaSeq,
												    TFAutoModelForTableQuestionAnswering,
												    TFAutoModelForTextEncoding,
												    TFAutoModelForTokenClassification,
												    TFAutoModelForVisionaSeq,
												    TFAutoModelForZeroShotImageClassification,
												    TFAutoModelWithLMHead,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_auto import (
												    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
												    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
												    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
												    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
												    FLAX_MODEL_MAPPING,
												    FlaxAutoModel,
												    FlaxAutoModelForCausalLM,
												    FlaxAutoModelForImageClassification,
												    FlaxAutoModelForMaskedLM,
												    FlaxAutoModelForMultipleChoice,
												    FlaxAutoModelForNextSentencePrediction,
												    FlaxAutoModelForPreTraining,
												    FlaxAutoModelForQuestionAnswering,
												    FlaxAutoModelForSeqaSeqLM,
												    FlaxAutoModelForSequenceClassification,
												    FlaxAutoModelForSpeechSeqaSeq,
												    FlaxAutoModelForTokenClassification,
												    FlaxAutoModelForVisionaSeq,
												)

else:
						import sys

						__snake_case					:							str           =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    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 		A						(  ):




			"""simple docstring"""


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

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    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 		A						(  ):




			"""simple docstring"""


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

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			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()

			UpperCAmelCase__		:Dict 		=    split_dataset_by_node(full_ds.shuffle(seed=42							)     ,						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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





