


'''simple docstring'''




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      int    )				->  List[str]:
        lowerCAmelCase     =			inspect.getfile(accelerate.test_utils    )
        lowerCAmelCase     =			os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_script.py']    )
        lowerCAmelCase     =			os.path.sep.join(
            mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_distributed_data_loop.py']    )
        lowerCAmelCase     =			os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_ops.py']    )





     @require_multi_gpu
     def 							__UpperCAmelCase      ( self       :      Any    )				->  List[Any]:
        print(F'''Found {torch.cuda.device_count()} devices.'''    )
        lowerCAmelCase     =			['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(UpperCAmelCase__    ,      env=os.environ.copy()    )





     @require_multi_gpu
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[int]:
        print(F'''Found {torch.cuda.device_count()} devices.'''    )
        lowerCAmelCase     =			['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
        print(F'''Command: {cmd}'''    )
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(UpperCAmelCase__    ,      env=os.environ.copy()    )





     @require_multi_gpu
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Union[str, Any]:
        lowerCAmelCase     =			['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(UpperCAmelCase__    ,      env=os.environ.copy()    )





     @require_multi_gpu
     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''    )
        lowerCAmelCase     =			['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,      cuda_visible_devices='0,1'    ):
           execute_subprocess_async(UpperCAmelCase__    ,      env=os.environ.copy()    )


if __name__ == "__main__":
       __snake_case    						=Accelerator()
       __snake_case    						=(accelerator.state.process_index + 2, 10)
       __snake_case    						=torch.randint(0, 10, shape).to(accelerator.device)

       __snake_case    						=""""""

       __snake_case    						=accelerator.pad_across_processes(tensor)
       if tensora.shape[0] != accelerator.state.num_processes + 1:
              error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
       if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
              error_msg += "Tensors have different values."
       if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
              error_msg += "Padding was not done with the right value (0)."

       __snake_case    						=accelerator.pad_across_processes(tensor, pad_first=True)
       if tensora.shape[0] != accelerator.state.num_processes + 1:
              error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
       __snake_case    						=accelerator.state.num_processes - accelerator.state.process_index - 1
       if not torch.equal(tensora[index:], tensor):
              error_msg += "Tensors have different values."
       if not torch.all(tensora[:index] == 0):
              error_msg += "Padding was not done with the right value (0)."

       # Raise error at the end to make sure we don't stop at the first failure.
       if len(error_msg) > 0:
              raise ValueError(error_msg)










"""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


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    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	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			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()
