




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__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]=0.01       ,     SCREAMING_SNAKE_CASE__					:					Dict=1_000       )				->	Optional[int]:
         lowerCAmelCase__          =			p_stop
         lowerCAmelCase__          =			max_length


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






class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       )				->	Dict:
         lowerCAmelCase__          =			[
             BatchSamplerShard(SCREAMING_SNAKE_CASE__       ,     2       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )
             for i in range(2       )
         ]
         lowerCAmelCase__          =			[list(SCREAMING_SNAKE_CASE__       ) for batch_sampler_shard in batch_sampler_shards]
         if not split_batches:
              self.assertListEqual([len(SCREAMING_SNAKE_CASE__       ) for shard in batch_sampler_shards]       ,     [len(SCREAMING_SNAKE_CASE__       ) for e in expected]       )
         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         # Check the shards when the dataset is a round multiple of total batch size.
         lowerCAmelCase__          =			BatchSampler(range(24       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         lowerCAmelCase__          =			BatchSampler(range(21       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

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

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

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         lowerCAmelCase__          =			BatchSampler(range(20       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

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

         lowerCAmelCase__          =			BatchSampler(range(2       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[[], []]
         self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       )				->	Tuple:
         # Check the shards when the dataset is a round multiple of batch size.
         lowerCAmelCase__          =			BatchSampler(range(24       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       )

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

         # Check the shards when the dataset is not a round multiple of batch size.
         lowerCAmelCase__          =			BatchSampler(range(22       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			BatchSampler(range(22       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			BatchSampler(range(21       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       )

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

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


    def        a				(	self					:					Any       )				->	Optional[int]:
         # Check the shards when the dataset is a round multiple of total batch size.
         lowerCAmelCase__          =			BatchSampler(range(24       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         lowerCAmelCase__          =			BatchSampler(range(21       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         lowerCAmelCase__          =			BatchSampler(range(22       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         lowerCAmelCase__          =			BatchSampler(range(20       )       ,     batch_size=3       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

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

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


    def        a				(	self					:					Dict       )				->	str:
         # Check the shards when the dataset is a round multiple of batch size.
         lowerCAmelCase__          =			BatchSampler(range(24       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

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

         lowerCAmelCase__          =			BatchSampler(range(22       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         lowerCAmelCase__          =			BatchSampler(range(21       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			BatchSampler(range(21       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[
             [[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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       )

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

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


    def        a				(	self					:					str       )				->	Union[str, Any]:
         lowerCAmelCase__          =			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         lowerCAmelCase__          =			[BatchSamplerShard(SCREAMING_SNAKE_CASE__       ,     2       ,     SCREAMING_SNAKE_CASE__       ,     even_batches=SCREAMING_SNAKE_CASE__       ) 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        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=False       ,     SCREAMING_SNAKE_CASE__					:					int=2       ,     SCREAMING_SNAKE_CASE__					:					Any=False       )				->	Dict:
         random.seed(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[
             IterableDatasetShard(
                 SCREAMING_SNAKE_CASE__       ,     batch_size=SCREAMING_SNAKE_CASE__       ,     drop_last=SCREAMING_SNAKE_CASE__       ,     num_processes=SCREAMING_SNAKE_CASE__       ,     process_index=SCREAMING_SNAKE_CASE__       ,     split_batches=SCREAMING_SNAKE_CASE__       ,     )
             for i in range(SCREAMING_SNAKE_CASE__       )
         ]
         lowerCAmelCase__          =			[]
         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(SCREAMING_SNAKE_CASE__       )
              iterable_dataset_lists.append(list(SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			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
         lowerCAmelCase__          =			iterable_dataset_lists[0]
         for l in iterable_dataset_lists[1:]:
              self.assertEqual(len(SCREAMING_SNAKE_CASE__       )       ,     len(SCREAMING_SNAKE_CASE__       )       )
              self.assertTrue(len(SCREAMING_SNAKE_CASE__       ) % shard_batch_size == 0       )

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

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


    def        a				(	self					:					str       )				->	Any:
         lowerCAmelCase__          =			42
         lowerCAmelCase__          =			RandomIterableDataset()

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

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

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


    def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			BatchSampler(range(16       )       ,     batch_size=4       ,     drop_last=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			SkipBatchSampler(SCREAMING_SNAKE_CASE__       ,     2       )
         self.assertListEqual(list(SCREAMING_SNAKE_CASE__       )       ,     [[8, 9, 10, 11], [12, 13, 14, 15]]       )


    def        a				(	self					:					int       )				->	Tuple:
         lowerCAmelCase__          =			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        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			DataLoader(list(range(16       )       )       ,     batch_size=4       )
         lowerCAmelCase__          =			skip_first_batches(SCREAMING_SNAKE_CASE__       ,     num_batches=2       )
         self.assertListEqual([t.tolist() for t in new_dataloader]       ,     [[8, 9, 10, 11], [12, 13, 14, 15]]       )


    def        a				(	self					:					Union[str, Any]       )				->	str:
         lowerCAmelCase__          =			DataLoaderShard(list(range(16       )       )       ,     batch_size=4       )
         for idx, _ in enumerate(SCREAMING_SNAKE_CASE__       ):
              self.assertEqual(dataloader.end_of_dataloader       ,     idx == 3       )

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


    def        a				(	self					:					Any       )				->	str:
         Accelerator()
         lowerCAmelCase__          =			DataLoaderDispatcher(range(16       )       ,     batch_size=4       )
         for idx, _ in enumerate(SCREAMING_SNAKE_CASE__       ):
              self.assertEqual(dataloader.end_of_dataloader       ,     idx == 3       )

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






from math import pi, sqrt



def 							_A       (						lowerCAmelCase_					: float      ):
     """simple docstring"""

     if num <= 0:
          raise ValueError("math domain error"      )
     if num > 171.5:
          raise OverflowError("math range error"      )
     elif num - int(lowerCAmelCase_      ) not in (0, 0.5):
          raise NotImplementedError("num must be an integer or a half-integer"      )
     elif num == 0.5:
          return sqrt(lowerCAmelCase_      )
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )



def 							_A       (						):
     """simple docstring"""

     assert gamma(0.5      ) == sqrt(lowerCAmelCase_      )
     assert gamma(1      ) == 1.0
     assert gamma(2      ) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCamelCase								=					1.0
     while num:
          UpperCamelCase								=					float(input('Gamma of: '))
          print(F"""gamma({num}) = {gamma(num)}""")
          print('\nEnter 0 to exit...')
