


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(						_a   ):
       def __init__(					self,      lowerCamelCase=0.0_1,      lowerCamelCase=10_00)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		p_stop
           _lowercase       :							Tuple      		=		max_length




       def __iter__(					self)       ->   str:




           """simple docstring"""

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






class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False,      lowerCamelCase=True)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Dict      		=		[
               BatchSamplerShard(lowerCamelCase,      2,      lowerCamelCase,      split_batches=lowerCamelCase,      even_batches=lowerCamelCase)
               for i in range(2)
           ]
           _lowercase       :							str      		=		[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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[str]      		=		BatchSampler(range(24),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							int      		=		[
               [[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)

           _lowercase       :							Union[str, 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.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(21),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							List[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)

           _lowercase       :							Dict      		=		BatchSampler(range(21),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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)

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

           _lowercase       :							List[str]      		=		BatchSampler(range(22),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
           # num_processes batch.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(20),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Dict      		=		[
               [[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)

           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(20),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Tuple      		=		[
               [[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.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(2),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Dict      		=		[[[0, 1, 0]], [[1, 0, 1]]]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							List[str]      		=		BatchSampler(range(2),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Dict      		=		[[], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							str      		=		BatchSampler(range(24),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[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)

           _lowercase       :							List[str]      		=		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.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(22),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							List[Any]      		=		[
               [[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)

           _lowercase       :							Optional[Any]      		=		BatchSampler(range(22),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Dict      		=		[
               [[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.
           _lowercase       :							Any      		=		BatchSampler(range(21),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		[
               [[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)

           _lowercase       :							Tuple      		=		BatchSampler(range(21),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           # Check the shards when the dataset is very small.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(2),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							int      		=		[[[0, 1]], [[0, 1]]]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      split_batches=lowerCamelCase)

           _lowercase       :							Dict      		=		BatchSampler(range(2),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Dict      		=		[[], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      split_batches=lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		BatchSampler(range(24),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							int      		=		[
               [[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)

           _lowercase       :							Any      		=		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.
           _lowercase       :							Any      		=		BatchSampler(range(21),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							List[str]      		=		[
               [[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)

           _lowercase       :							Optional[Any]      		=		BatchSampler(range(21),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Tuple      		=		[
               [[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.
           _lowercase       :							str      		=		BatchSampler(range(22),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           _lowercase       :							str      		=		BatchSampler(range(22),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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.
           _lowercase       :							List[Any]      		=		BatchSampler(range(20),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           _lowercase       :							Dict      		=		BatchSampler(range(20),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							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 very small.
           _lowercase       :							Union[str, Any]      		=		BatchSampler(range(2),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							int      		=		[[[0, 1]], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      even_batches=lowerCamelCase)

           _lowercase       :							Dict      		=		BatchSampler(range(2),      batch_size=3,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		[[], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      even_batches=lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		BatchSampler(range(24),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           _lowercase       :							Optional[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.
           _lowercase       :							int      		=		BatchSampler(range(22),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							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)

           _lowercase       :							List[str]      		=		BatchSampler(range(22),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							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.
           _lowercase       :							List[str]      		=		BatchSampler(range(21),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		[
               [[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)

           _lowercase       :							Any      		=		BatchSampler(range(21),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[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.
           _lowercase       :							Any      		=		BatchSampler(range(2),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							int      		=		[[[0, 1]], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      split_batches=lowerCamelCase,      even_batches=lowerCamelCase)

           _lowercase       :							str      		=		BatchSampler(range(2),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		[[], []]
           self.check_batch_sampler_shards(lowerCamelCase,      lowerCamelCase,      split_batches=lowerCamelCase,      even_batches=lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
           _lowercase       :							List[str]      		=		[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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False,      lowerCamelCase=2,      lowerCamelCase=False)       ->   str:




           """simple docstring"""

           random.seed(lowerCamelCase)
           _lowercase       :							List[str]      		=		list(lowerCamelCase)

           _lowercase       :							Optional[int]      		=		[
               IterableDatasetShard(
                   lowerCamelCase,      batch_size=lowerCamelCase,      drop_last=lowerCamelCase,      num_processes=lowerCamelCase,      process_index=lowerCamelCase,      split_batches=lowerCamelCase,      )
               for i in range(lowerCamelCase)
           ]
           _lowercase       :							Dict      		=		[]
           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))

           _lowercase       :							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
           _lowercase       :							str      		=		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)

           _lowercase       :							Tuple      		=		[]
           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 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		42
           _lowercase       :							int      		=		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
           _lowercase       :							Union[str, 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 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		BatchSampler(range(16),      batch_size=4,      drop_last=lowerCamelCase)
           _lowercase       :							str      		=		SkipBatchSampler(lowerCamelCase,      2)
           self.assertListEqual(list(lowerCamelCase),      [[8, 9, 10, 11], [12, 13, 14, 15]])
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Union[str, 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 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		DataLoader(list(range(16)),      batch_size=4)
           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           Accelerator()
           _lowercase       :							Optional[Any]      		=		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 unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class _lowerCamelCase(						unittest.TestCase   ):
       lowercase_ :	Dict     =			JukeboxTokenizer
       lowercase_ :	Dict     =			{
           """artist""": """Zac Brown Band""",
           """genres""": """Country""",
           """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
       }
       @require_torch
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           import torch

           _lowercase       :							str      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics')
           _lowercase       :							Optional[Any]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                   76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                   44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                   47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                   76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                   30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                   27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                   45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                   41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                   76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                   76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                   64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                   30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                   27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                   34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                   27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                   41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                   44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                   76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                   32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                   40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                   45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                   31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                   45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                   34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                   31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                   40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                   38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                   76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                   41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                   27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                   46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                   41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                   46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                   41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                   40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                   27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                   76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                   41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                   76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                   27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                   34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                   44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                   40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                   46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                   38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                   40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                   27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                   76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                   76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                   76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                   76, 76]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))




       @require_torch
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           import torch

           _lowercase       :							List[str]      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics')
           _lowercase       :							List[str]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                   31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                   31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                   40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                   79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                   77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                   27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                   37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                   32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                   77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                   77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                   46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                   77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                   77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                   77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                   77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                   64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                   40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                   40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                   38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                   31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                   41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                   46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                   41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                   31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                   31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                   44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                   31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                   38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                   40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                   27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                   31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                   34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                   31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                   31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                   45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                   31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                   15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                   11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                   45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                   41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                   44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                   46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                   27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                   35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                   77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                   31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                   41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                   77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                   40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                   77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                   27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                   77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                   77, 77, 77, 77, 77, 77]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))


