



"""simple docstring"""

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase__       :    Any				       =      4
lowercase__       :    Optional[Any]				       =      3




class 			_UpperCAmelCase			(     snake_case__):
 pass





def __lowercase     (				_a       ):
    for shard in shards:
        for i in range(lowerCAmelCase__       ):
            yield {"i": i, "shard": shard}




def __lowercase     (				):
    snake_case_					:	Union[str, Any]							=      int(os.environ['''RANK''']       )
    snake_case_					:	str							=      int(os.environ['''WORLD_SIZE''']       )

    snake_case_					:	List[Any]							=      ArgumentParser()
    parser.add_argument('''--streaming'''						,     type=lowerCAmelCase__       )
    parser.add_argument('''--local_rank'''						,     type=lowerCAmelCase__       )
    parser.add_argument('''--num_workers'''						,     type=lowerCAmelCase__						,     default=0       )
    snake_case_					:	List[str]							=      parser.parse_args()
    snake_case_					:	int							=      args.streaming
    snake_case_					:	int							=      args.num_workers

    snake_case_					:	int							=      {'shards': [f"shard_{shard_idx}" for shard_idx in range(lowerCAmelCase__       )]}
    snake_case_					:	Any							=      IterableDataset.from_generator(lowerCAmelCase__						,     gen_kwargs=lowerCAmelCase__       )
    if not streaming:
        snake_case_					:	Union[str, Any]							=      Dataset.from_list(list(lowerCAmelCase__       )       )

    snake_case_					:	Union[str, Any]							=      split_dataset_by_node(lowerCAmelCase__						,     rank=lowerCAmelCase__						,     world_size=lowerCAmelCase__       )
    snake_case_					:	Tuple							=      torch.utils.data.DataLoader(lowerCAmelCase__						,     num_workers=lowerCAmelCase__       )

    snake_case_					:	Union[str, Any]							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
    snake_case_					:	List[str]							=      full_size // world_size
    expected_local_size += int(rank < (full_size % world_size)       )

    snake_case_					:	Union[str, Any]							=      sum(1 for _ in dataloader       )
    if local_size != expected_local_size:
        raise FailedTestError(f"local_size {local_size} != expected_local_size {expected_local_size}"       )


if __name__ == "__main__":
     main()












from typing import TYPE_CHECKING

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


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
