


'''simple docstring'''
import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def __init__( self			,     _UpperCamelCase=0.01			,     _UpperCamelCase=1000   )->      Optional[int]:
            _A				=							p_stop
            _A				=							max_length






       def __iter__( self   )->      List[str]:
            _A				=							0
            _A				=							False
            while not stop and count < self.max_length:
                 yield count
                 count += 1
                 _A				=							random.random() < self.p_stop






class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=False			,     _UpperCamelCase=True   )->      List[str]:
            _A				=							[
                BatchSamplerShard(_UpperCamelCase			,     2			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )
                for i in range(2   )
            ]
            _A				=							[list(_UpperCamelCase   ) for batch_sampler_shard in batch_sampler_shards]
            if not split_batches:
                 self.assertListEqual([len(_UpperCamelCase   ) for shard in batch_sampler_shards]			,     [len(_UpperCamelCase   ) for e in expected]   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Check the shards when the dataset is a round multiple of total batch size.
            _A				=							BatchSampler(range(24   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							BatchSampler(range(24   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            # Expected shouldn't change
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            # Check the shards when the dataset is a round multiple of batch size but not total batch size.
            _A				=							BatchSampler(range(21   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							BatchSampler(range(21   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
            # num_processes batch.
            _A				=							BatchSampler(range(22   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							BatchSampler(range(22   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
            # num_processes batch.
            _A				=							BatchSampler(range(20   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							BatchSampler(range(20   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            # Check the shards when the dataset is very small.
            _A				=							BatchSampler(range(2   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[[[0, 1, 0]], [[1, 0, 1]]]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							BatchSampler(range(2   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[[], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Dict:
            # Check the shards when the dataset is a round multiple of batch size.
            _A				=							BatchSampler(range(24   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(24   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            # Expected shouldn't change
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size.
            _A				=							BatchSampler(range(22   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(22   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size or num_processes.
            _A				=							BatchSampler(range(21   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(21   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            # Check the shards when the dataset is very small.
            _A				=							BatchSampler(range(2   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[[[0, 1]], [[0, 1]]]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(2   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[[], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[str]:
            # Check the shards when the dataset is a round multiple of total batch size.
            _A				=							BatchSampler(range(24   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(24   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            # Expected shouldn't change
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is a round multiple of batch size but not total batch size.
            _A				=							BatchSampler(range(21   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(21   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
            # num_processes batch.
            _A				=							BatchSampler(range(22   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(22   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
            # num_processes batch.
            _A				=							BatchSampler(range(20   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(20   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is very small.
            _A				=							BatchSampler(range(2   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[[[0, 1]], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(2   )			,     batch_size=3			,     drop_last=_UpperCamelCase   )
            _A				=							[[], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Check the shards when the dataset is a round multiple of batch size.
            _A				=							BatchSampler(range(24   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(24   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            # Expected shouldn't change
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size.
            _A				=							BatchSampler(range(22   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(22   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is not a round multiple of batch size or num_processes.
            _A				=							BatchSampler(range(21   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(21   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            # Check the shards when the dataset is very small.
            _A				=							BatchSampler(range(2   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[[[0, 1]], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )

            _A				=							BatchSampler(range(2   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							[[], []]
            self.check_batch_sampler_shards(_UpperCamelCase			,     _UpperCamelCase			,     split_batches=_UpperCamelCase			,     even_batches=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
            _A				=							[BatchSamplerShard(_UpperCamelCase			,     2			,     _UpperCamelCase			,     even_batches=_UpperCamelCase   ) for i in range(2   )]

            self.assertEqual(len(batch_sampler_shards[0]   )			,     3   )
            self.assertEqual(len(batch_sampler_shards[1]   )			,     2   )

            self.assertListEqual(list(batch_sampler_shards[0]   )			,     [[0, 1, 2], [5, 6, 7, 8], [12, 13]]   )
            self.assertListEqual(list(batch_sampler_shards[1]   )			,     [[3, 4], [9, 10, 11]]   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=False			,     _UpperCamelCase=2			,     _UpperCamelCase=False   )->      List[str]:
            random.seed(_UpperCamelCase   )
            _A				=							list(_UpperCamelCase   )

            _A				=							[
                IterableDatasetShard(
                    _UpperCamelCase			,     batch_size=_UpperCamelCase			,     drop_last=_UpperCamelCase			,     num_processes=_UpperCamelCase			,     process_index=_UpperCamelCase			,     split_batches=_UpperCamelCase			,     )
                for i in range(_UpperCamelCase   )
            ]
            _A				=							[]
            for iterable_dataset_shard in iterable_dataset_shards:
                 # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
                 random.seed(_UpperCamelCase   )
                 iterable_dataset_lists.append(list(_UpperCamelCase   )   )

            _A				=							batch_size // num_processes if split_batches else batch_size
            # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
            _A				=							iterable_dataset_lists[0]
            for l in iterable_dataset_lists[1:]:
                 self.assertEqual(len(_UpperCamelCase   )			,     len(_UpperCamelCase   )   )
                 self.assertTrue(len(_UpperCamelCase   ) % shard_batch_size == 0   )

            _A				=							[]
            for idx in range(0			,     len(_UpperCamelCase   )			,     _UpperCamelCase   ):
                 for l in iterable_dataset_lists:
                      observed += l[idx : idx + shard_batch_size]

            if not drop_last:
                 while len(_UpperCamelCase   ) < len(_UpperCamelCase   ):
                      reference += reference
            self.assertListEqual(_UpperCamelCase			,     reference[: len(_UpperCamelCase   )]   )






       def   UpperCamelCase						( self   )->      Dict:
            _A				=							42
            _A				=							RandomIterableDataset()

            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )

            # Edge case with a very small dataset
            _A				=							RandomIterableDataset(max_length=2   )

            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )
            self.check_iterable_dataset_shards(_UpperCamelCase			,     _UpperCamelCase			,     batch_size=4			,     drop_last=_UpperCamelCase			,     split_batches=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							BatchSampler(range(16   )			,     batch_size=4			,     drop_last=_UpperCamelCase   )
            _A				=							SkipBatchSampler(_UpperCamelCase			,     2   )
            self.assertListEqual(list(_UpperCamelCase   )			,     [[8, 9, 10, 11], [12, 13, 14, 15]]   )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							SkipDataLoader(list(range(16   )   )			,     batch_size=4			,     skip_batches=2   )
            self.assertListEqual([t.tolist() for t in dataloader]			,     [[8, 9, 10, 11], [12, 13, 14, 15]]   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							DataLoader(list(range(16   )   )			,     batch_size=4   )
            _A				=							skip_first_batches(_UpperCamelCase			,     num_batches=2   )
            self.assertListEqual([t.tolist() for t in new_dataloader]			,     [[8, 9, 10, 11], [12, 13, 14, 15]]   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							DataLoaderShard(list(range(16   )   )			,     batch_size=4   )
            for idx, _ in enumerate(_UpperCamelCase   ):
                 self.assertEqual(dataloader.end_of_dataloader			,     idx == 3   )

            # Test it also works on the second iteration
            for idx, _ in enumerate(_UpperCamelCase   ):
                 self.assertEqual(dataloader.end_of_dataloader			,     idx == 3   )






       def   UpperCamelCase						( self   )->      Tuple:
            Accelerator()
            _A				=							DataLoaderDispatcher(range(16   )			,     batch_size=4   )
            for idx, _ in enumerate(_UpperCamelCase   ):
                 self.assertEqual(dataloader.end_of_dataloader			,     idx == 3   )

            # Test it also works on the second iteration
            for idx, _ in enumerate(_UpperCamelCase   ):
                 self.assertEqual(dataloader.end_of_dataloader			,     idx == 3   )









'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							Path(__UpperCamelCase		)
     path.parent.mkdir(parents=__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
     if path.exists():
          print(
              F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
          return False
     _A				=							mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
     _A				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							parser.add_parser('default'							,    parents=__UpperCamelCase							,    help=__UpperCamelCase							,    formatter_class=__UpperCamelCase		)
     parser.add_argument(
         '--config_file'							,    default=__UpperCamelCase							,    help=(
             'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
             'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
             'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
             'with \'huggingface\'.'
         )							,    dest='save_location'							,    )

     parser.add_argument(
         '--mixed_precision'							,    choices=['no', 'fp16', 'bf16']							,    type=__UpperCamelCase							,    help='Whether or not to use mixed precision training. '
         'Choose between FP16 and BF16 (bfloat16) training. '
         'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'							,    default='no'							,    )
     parser.set_defaults(func=__UpperCamelCase		)
     return parser

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





