





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    _UpperCAmelCase							(		lowerCAmelCase__     ):
      def __init__(		self      ,       a__=0.01      ,       a__=1000       ):
         A_     :				Optional[int]			      =	p_stop
         A_     :				Any			      =	max_length






      def __iter__(		self       ):
         A_     :				Union[str, Any]			      =	0
         A_     :				int			      =	False
         while not stop and count < self.max_length:
            yield count
            count += 1
            A_     :				Optional[Any]			      =	random.random() < self.p_stop






class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__=False      ,       a__=True       ):
         A_     :				Tuple			      =	[
             BatchSamplerShard(_lowerCamelCase      ,       2      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )
             for i in range(2       )
         ]
         A_     :				Tuple			      =	[list(_lowerCamelCase       ) for batch_sampler_shard in batch_sampler_shards]
         if not split_batches:
            self.assertListEqual([len(_lowerCamelCase       ) for shard in batch_sampler_shards]      ,       [len(_lowerCamelCase       ) for e in expected]       )
         self.assertListEqual(_lowerCamelCase      ,       _lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	BatchSampler(range(24       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase       )

         A_     :				Optional[Any]			      =	BatchSampler(range(24       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase       )

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         A_     :				Any			      =	BatchSampler(range(21       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Union[str, Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase       )

         A_     :				List[str]			      =	BatchSampler(range(21       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[Any]			      =	[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase       )

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         A_     :				List[Any]			      =	BatchSampler(range(22       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase       )

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

         # 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_     :				Union[str, Any]			      =	BatchSampler(range(20       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				List[str]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase       )

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

         # Check the shards when the dataset is very small.
         A_     :				Dict			      =	BatchSampler(range(2       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Union[str, Any]			      =	[[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase       )

         A_     :				List[str]			      =	BatchSampler(range(2       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Any			      =	[[], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	BatchSampler(range(24       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[int]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         A_     :				Tuple			      =	BatchSampler(range(24       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         # Check the shards when the dataset is not a round multiple of batch size.
         A_     :				Any			      =	BatchSampler(range(22       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				int			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         A_     :				Tuple			      =	BatchSampler(range(22       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				List[Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         A_     :				str			      =	BatchSampler(range(21       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				Any			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         A_     :				str			      =	BatchSampler(range(21       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				List[Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         # Check the shards when the dataset is very small.
         A_     :				Any			      =	BatchSampler(range(2       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				Tuple			      =	[[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )

         A_     :				Optional[int]			      =	BatchSampler(range(2       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				List[Any]			      =	[[], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	BatchSampler(range(24       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				List[Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				Optional[int]			      =	BatchSampler(range(24       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         A_     :				Optional[Any]			      =	BatchSampler(range(21       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[Any]			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

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

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         A_     :				Union[str, Any]			      =	BatchSampler(range(22       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				Union[str, Any]			      =	BatchSampler(range(22       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				int			      =	[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         # 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_     :				Any			      =	BatchSampler(range(20       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

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

         # Check the shards when the dataset is very small.
         A_     :				Union[str, Any]			      =	BatchSampler(range(2       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				List[str]			      =	[[[0, 1]], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				Tuple			      =	BatchSampler(range(2       )      ,       batch_size=3      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[int]			      =	[[], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	BatchSampler(range(24       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				Any			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				List[Any]			      =	BatchSampler(range(24       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         # Check the shards when the dataset is not a round multiple of batch size.
         A_     :				str			      =	BatchSampler(range(22       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				Union[str, Any]			      =	BatchSampler(range(22       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         A_     :				Optional[int]			      =	BatchSampler(range(21       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				Tuple			      =	BatchSampler(range(21       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				int			      =	[
             [[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(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         # Check the shards when the dataset is very small.
         A_     :				Optional[Any]			      =	BatchSampler(range(2       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				Optional[int]			      =	[[[0, 1]], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )

         A_     :				List[str]			      =	BatchSampler(range(2       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				str			      =	[[], []]
         self.check_batch_sampler_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       even_batches=_lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         A_     :				Union[str, Any]			      =	[BatchSamplerShard(_lowerCamelCase      ,       2      ,       _lowerCamelCase      ,       even_batches=_lowerCamelCase       ) 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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__=False      ,       a__=2      ,       a__=False       ):
         random.seed(_lowerCamelCase       )
         A_     :				Union[str, Any]			      =	list(_lowerCamelCase       )

         A_     :				Union[str, Any]			      =	[
             IterableDatasetShard(
                 _lowerCamelCase      ,       batch_size=_lowerCamelCase      ,       drop_last=_lowerCamelCase      ,       num_processes=_lowerCamelCase      ,       process_index=_lowerCamelCase      ,       split_batches=_lowerCamelCase      ,       )
             for i in range(_lowerCamelCase       )
         ]
         A_     :				Optional[int]			      =	[]
         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(_lowerCamelCase       )
            iterable_dataset_lists.append(list(_lowerCamelCase       )       )

         A_     :				Tuple			      =	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_     :				List[Any]			      =	iterable_dataset_lists[0]
         for l in iterable_dataset_lists[1:]:
            self.assertEqual(len(_lowerCamelCase       )      ,       len(_lowerCamelCase       )       )
            self.assertTrue(len(_lowerCamelCase       ) % shard_batch_size == 0       )

         A_     :				List[str]			      =	[]
         for idx in range(0      ,       len(_lowerCamelCase       )      ,       _lowerCamelCase       ):
            for l in iterable_dataset_lists:
               observed += l[idx : idx + shard_batch_size]

         if not drop_last:
            while len(_lowerCamelCase       ) < len(_lowerCamelCase       ):
               reference += reference
         self.assertListEqual(_lowerCamelCase      ,       reference[: len(_lowerCamelCase       )]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	42
         A_     :				Any			      =	RandomIterableDataset()

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

         # Edge case with a very small dataset
         A_     :				List[Any]			      =	RandomIterableDataset(max_length=2       )

         self.check_iterable_dataset_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       batch_size=4      ,       drop_last=_lowerCamelCase      ,       split_batches=_lowerCamelCase       )
         self.check_iterable_dataset_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       batch_size=4      ,       drop_last=_lowerCamelCase      ,       split_batches=_lowerCamelCase       )
         self.check_iterable_dataset_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       batch_size=4      ,       drop_last=_lowerCamelCase      ,       split_batches=_lowerCamelCase       )
         self.check_iterable_dataset_shards(_lowerCamelCase      ,       _lowerCamelCase      ,       batch_size=4      ,       drop_last=_lowerCamelCase      ,       split_batches=_lowerCamelCase       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	BatchSampler(range(16       )      ,       batch_size=4      ,       drop_last=_lowerCamelCase       )
         A_     :				List[Any]			      =	SkipBatchSampler(_lowerCamelCase      ,       2       )
         self.assertListEqual(list(_lowerCamelCase       )      ,       [[8, 9, 10, 11], [12, 13, 14, 15]]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	DataLoader(list(range(16       )       )      ,       batch_size=4       )
         A_     :				Tuple			      =	skip_first_batches(_lowerCamelCase      ,       num_batches=2       )
         self.assertListEqual([t.tolist() for t in new_dataloader]      ,       [[8, 9, 10, 11], [12, 13, 14, 15]]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	DataLoaderShard(list(range(16       )       )      ,       batch_size=4       )
         for idx, _ in enumerate(_lowerCamelCase       ):
            self.assertEqual(dataloader.end_of_dataloader      ,       idx == 3       )

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






      def 		_lowerCamelCase      (		self       ):
         Accelerator()
         A_     :				List[str]			      =	DataLoaderDispatcher(range(16       )      ,       batch_size=4       )
         for idx, _ in enumerate(_lowerCamelCase       ):
            self.assertEqual(dataloader.end_of_dataloader      ,       idx == 3       )

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













import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	[]
   for i in range(config.num_hidden_layers     ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")     )
      rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")     )
      rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")     )
      rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")     )
      rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")     )

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("""cls_token""", """deit.embeddings.cls_token"""),
           ("""dist_token""", """deit.embeddings.distillation_token"""),
           ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
           ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
           ("""pos_embed""", """deit.embeddings.position_embeddings"""),
       ]     )

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
              ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
              ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
          ]     )

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A_     :				Tuple			      =	[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""     ) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ("""norm.weight""", """deit.layernorm.weight"""),
              ("""norm.bias""", """deit.layernorm.bias"""),
              ("""head.weight""", """cls_classifier.weight"""),
              ("""head.bias""", """cls_classifier.bias"""),
              ("""head_dist.weight""", """distillation_classifier.weight"""),
              ("""head_dist.bias""", """distillation_classifier.bias"""),
          ]     )

   return rename_keys



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   for i in range(config.num_hidden_layers     ):
      if base_model:
         A_     :				str			      =	""""""
      else:
         A_     :				List[str]			      =	"""deit."""
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A_     :				Tuple			      =	state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
      A_     :				str			      =	state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      A_     :				Optional[Any]			      =	in_proj_weight[
          : config.hidden_size, :
      ]
      A_     :				Optional[Any]			      =	in_proj_bias[: config.hidden_size]
      A_     :				Optional[Any]			      =	in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A_     :				int			      =	in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A_     :				str			      =	in_proj_weight[
          -config.hidden_size :, :
      ]
      A_     :				Any			      =	in_proj_bias[-config.hidden_size :]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	dct.pop(_lowerCAmelCase     )
   A_     :				Any			      =	val



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_     :				Union[str, Any]			      =	Image.open(requests.get(_lowerCAmelCase  ,stream=_lowerCAmelCase     ).raw     )
   return im



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	DeiTConfig()
   # all deit models have fine-tuned heads
   A_     :				Optional[Any]			      =	False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A_     :				Optional[int]			      =	1_0_0_0
   A_     :				List[Any]			      =	"""huggingface/label-files"""
   A_     :				str			      =	"""imagenet-1k-id2label.json"""
   A_     :				Union[str, Any]			      =	json.load(open(hf_hub_download(_lowerCAmelCase  ,_lowerCAmelCase  ,repo_type="""dataset"""     )  ,"""r"""     )     )
   A_     :				Optional[Any]			      =	{int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
   A_     :				Union[str, Any]			      =	idalabel
   A_     :				Optional[int]			      =	{v: k for k, v in idalabel.items()}
   A_     :				Dict			      =	int(deit_name[-6:-4]     )
   A_     :				Any			      =	int(deit_name[-3:]     )
   # size of the architecture
   if deit_name[9:].startswith("""tiny"""     ):
      A_     :				str			      =	1_9_2
      A_     :				Dict			      =	7_6_8
      A_     :				List[Any]			      =	1_2
      A_     :				Dict			      =	3
   elif deit_name[9:].startswith("""small"""     ):
      A_     :				Optional[int]			      =	3_8_4
      A_     :				List[Any]			      =	1_5_3_6
      A_     :				Optional[Any]			      =	1_2
      A_     :				List[str]			      =	6
   if deit_name[9:].startswith("""base"""     ):
      pass
   elif deit_name[4:].startswith("""large"""     ):
      A_     :				Dict			      =	1_0_2_4
      A_     :				int			      =	4_0_9_6
      A_     :				Any			      =	2_4
      A_     :				str			      =	1_6

   # load original model from timm
   A_     :				int			      =	timm.create_model(_lowerCAmelCase  ,pretrained=_lowerCAmelCase     )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A_     :				Union[str, Any]			      =	timm_model.state_dict()
   A_     :				Optional[Any]			      =	create_rename_keys(_lowerCAmelCase  ,_lowerCAmelCase     )
   for src, dest in rename_keys:
      rename_key(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
   read_in_q_k_v(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   # load HuggingFace model
   A_     :				List[str]			      =	DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
   model.load_state_dict(_lowerCAmelCase     )

   # Check outputs on an image, prepared by DeiTImageProcessor
   A_     :				List[Any]			      =	int(
       (2_5_6 / 2_2_4) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A_     :				Any			      =	DeiTImageProcessor(size=_lowerCAmelCase  ,crop_size=config.image_size     )
   A_     :				Optional[int]			      =	image_processor(images=prepare_img()  ,return_tensors="""pt"""     )
   A_     :				List[str]			      =	encoding["""pixel_values"""]
   A_     :				List[Any]			      =	model(_lowerCAmelCase     )

   A_     :				Tuple			      =	timm_model(_lowerCAmelCase     )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(_lowerCAmelCase  ,outputs.logits  ,atol=1e-3     )

   Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
   print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(_lowerCAmelCase     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--deit_name""",
          default="""vit_deit_base_distilled_patch16_224""",
          type=str,
          help="""Name of the DeiT timm model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )

      _lowerCAmelCase 				=     parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






