



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  _A		(  __magic_name__):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=0.01 ,					_SCREAMING_SNAKE_CASE=1000					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     p_stop
        SCREAMING_SNAKE_CASE_				:  str           =     max_length



  def __iter__(				self					):





        """simple docstring"""







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

class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
            BatchSamplerShard(_SCREAMING_SNAKE_CASE ,					2 ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)
            for i in range(2					)
        ]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [[[0, 1, 0]], [[1, 0, 1]]]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     BatchSampler(range(24					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, 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(_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.
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

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

        SCREAMING_SNAKE_CASE_				:  int           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     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.
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, 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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_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.
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [[[0, 1]], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  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(_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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [[[0, 1]], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        random.seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     list(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [
            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					)
        ]
        SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE					)
              iterable_dataset_lists.append(list(_SCREAMING_SNAKE_CASE					)					)

        SCREAMING_SNAKE_CASE_				:  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
        SCREAMING_SNAKE_CASE_				:  int           =     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					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     []
        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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     42
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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
        SCREAMING_SNAKE_CASE_				:  List[str]           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(16					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     SkipBatchSampler(_SCREAMING_SNAKE_CASE ,					2					)
        self.assertListEqual(list(_SCREAMING_SNAKE_CASE					) ,					[[8, 9, 10, 11], [12, 13, 14, 15]]					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(list(range(16					)					) ,					batch_size=4					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        Accelerator()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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					)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  field(default='''automatic-speech-recognition'''   ,     metadata={'''include_in_asdict_even_if_is_default''': True})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''audio''': Audio()})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''transcription''': Value('''string''')})
  SCREAMING_SNAKE_CASE    :      str       =  "audio"
  SCREAMING_SNAKE_CASE    :      str       =  "transcription"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."					)
        if not isinstance(features[self.audio_column] ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
        SCREAMING_SNAKE_CASE_				:  int           =     copy.deepcopy(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.input_schema.copy()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     features[self.audio_column]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_schema
        return task_template



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}






