
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(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_=0.01	,      SCREAMING_SNAKE_CASE_=10_00						):
       UpperCamelCase__				=    p_stop
       UpperCamelCase__				=    max_length







  def __iter__(self						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    False
       while not stop and count < self.max_length:
            yield count
            count += 1
            UpperCamelCase__				=    random.random() < self.p_stop






class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=True						):
       UpperCamelCase__				=    [
           BatchSamplerShard(SCREAMING_SNAKE_CASE_	,      2	,      SCREAMING_SNAKE_CASE_	,      split_batches=SCREAMING_SNAKE_CASE_	,      even_batches=SCREAMING_SNAKE_CASE_						)
           for i in range(2						)
       ]
       UpperCamelCase__				=    [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						):
       # Check the shards when the dataset is a round multiple of total batch size.
       UpperCamelCase__				=    BatchSampler(range(24						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    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.
       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(20						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(20						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(2						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [[[0, 1, 0]], [[1, 0, 1]]]
       self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    BatchSampler(range(2						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [[], []]
       self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Check the shards when the dataset is a round multiple of batch size.
       UpperCamelCase__				=    BatchSampler(range(24						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    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.
       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(2						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [[[0, 1]], [[0, 1]]]
       self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      split_batches=SCREAMING_SNAKE_CASE_						)

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







  def 	UpperCAmelCase_ (self						):
       # Check the shards when the dataset is a round multiple of total batch size.
       UpperCamelCase__				=    BatchSampler(range(24						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    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.
       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(20						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(20						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(2						)	,      batch_size=3	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [[[0, 1]], []]
       self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      even_batches=SCREAMING_SNAKE_CASE_						)

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







  def 	UpperCAmelCase_ (self						):
       # Check the shards when the dataset is a round multiple of batch size.
       UpperCamelCase__				=    BatchSampler(range(24						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    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.
       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(22						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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_						)

       UpperCamelCase__				=    BatchSampler(range(21						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [
           [[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.
       UpperCamelCase__				=    BatchSampler(range(2						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [[[0, 1]], []]
       self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      split_batches=SCREAMING_SNAKE_CASE_	,      even_batches=SCREAMING_SNAKE_CASE_						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
       UpperCamelCase__				=    [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						):
       random.seed(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    list(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    [
           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_						)
       ]
       UpperCamelCase__				=    []
       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_						)						)

       UpperCamelCase__				=    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
       UpperCamelCase__				=    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						)

       UpperCamelCase__				=    []
       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						):
       UpperCamelCase__				=    42
       UpperCamelCase__				=    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
       UpperCamelCase__				=    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						):
       UpperCamelCase__				=    BatchSampler(range(16						)	,      batch_size=4	,      drop_last=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    SkipBatchSampler(SCREAMING_SNAKE_CASE_	,      2						)
       self.assertListEqual(list(SCREAMING_SNAKE_CASE_						)	,      [[8, 9, 10, 11], [12, 13, 14, 15]]						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    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						):
       UpperCamelCase__				=    DataLoader(list(range(16						)						)	,      batch_size=4						)
       UpperCamelCase__				=    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						):
       UpperCamelCase__				=    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						):
       Accelerator()
       UpperCamelCase__				=    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





