
import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)

class     _UpperCamelCase			(   _A					):






   '''simple docstring'''

   def __init__(    self	:    int  ,		snake_case_	:    Tuple=0.01  ,		snake_case_	:    Tuple=1000 ):
       UpperCamelCase_:							List[str]		=  p_stop
       UpperCamelCase_:							int		=  max_length




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




class     _UpperCamelCase			(   unittest.TestCase					):






   '''simple docstring'''

   def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Tuple  ,		snake_case_	:    Tuple  ,		snake_case_	:    List[str]=False  ,		snake_case_	:    Dict=True ):
       UpperCamelCase_:							str		=  [
           BatchSamplerShard(snake_case_  ,		2  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )
           for i in range(2 )
       ]
       UpperCamelCase_:							Dict		=  [list(snake_case_ ) for batch_sampler_shard in batch_sampler_shards]
       if not split_batches:
           self.assertListEqual([len(snake_case_ ) for shard in batch_sampler_shards]  ,		[len(snake_case_ ) for e in expected] )
       self.assertListEqual(snake_case_  ,		snake_case_ )

   def  lowerCAmelCase__		(    self	:    List[str] ):
       # Check the shards when the dataset is a round multiple of total batch size.
       UpperCamelCase_:							List[str]		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Union[str, Any]		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

       UpperCamelCase_:							Dict		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       # Expected shouldn't change
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       UpperCamelCase_:							Tuple		=  BatchSampler(range(21 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_ )

       UpperCamelCase_:							List[str]		=  BatchSampler(range(21 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							str		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		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_:							List[Any]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_ )

       UpperCamelCase_:							int		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Tuple		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		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_:							str		=  BatchSampler(range(20 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							str		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

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

       # Check the shards when the dataset is very small.
       UpperCamelCase_:							Dict		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							int		=  [[[0, 1, 0]], [[1, 0, 1]]]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

       UpperCamelCase_:							int		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							str		=  [[], []]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

   def  lowerCAmelCase__		(    self	:    Optional[int] ):
       # Check the shards when the dataset is a round multiple of batch size.
       UpperCamelCase_:							str		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

       UpperCamelCase_:							Dict		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       # Expected shouldn't change
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

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

       UpperCamelCase_:							Any		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

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

       UpperCamelCase_:							Any		=  BatchSampler(range(21 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

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

       UpperCamelCase_:							Optional[int]		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Dict		=  [[], []]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

   def  lowerCAmelCase__		(    self	:    Optional[int] ):
       # Check the shards when the dataset is a round multiple of total batch size.
       UpperCamelCase_:							Optional[int]		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Union[str, Any]		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							Tuple		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       # Expected shouldn't change
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       UpperCamelCase_:							str		=  BatchSampler(range(21 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Optional[Any]		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							Tuple		=  BatchSampler(range(21 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							int		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=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_:							Optional[int]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							List[Any]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							List[str]		=  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=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_:							List[Any]		=  BatchSampler(range(20 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							int		=  [
           [[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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

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

       # Check the shards when the dataset is very small.
       UpperCamelCase_:							Tuple		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Optional[int]		=  [[[0, 1]], []]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							Any		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Tuple		=  [[], []]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

   def  lowerCAmelCase__		(    self	:    Any ):
       # Check the shards when the dataset is a round multiple of batch size.
       UpperCamelCase_:							int		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							List[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							Optional[int]		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       # Expected shouldn't change
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

       # Check the shards when the dataset is not a round multiple of batch size.
       UpperCamelCase_:							int		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							Optional[int]		=  [
           [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

       UpperCamelCase_:							Dict		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

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

       UpperCamelCase_:							Any		=  BatchSampler(range(21 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

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

       UpperCamelCase_:							Tuple		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
       UpperCamelCase_:							List[Any]		=  [[], []]
       self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

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

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

       UpperCamelCase_:							List[str]		=  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_:							Dict		=  iterable_dataset_lists[0]
       for l in iterable_dataset_lists[1:]:
           self.assertEqual(len(snake_case_ )  ,		len(snake_case_ ) )
           self.assertTrue(len(snake_case_ ) % shard_batch_size == 0 )

       UpperCamelCase_:							Dict		=  []
       for idx in range(0  ,		len(snake_case_ )  ,		snake_case_ ):
           for l in iterable_dataset_lists:
               observed += l[idx : idx + shard_batch_size]

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

   def  lowerCAmelCase__		(    self	:    List[Any] ):
       UpperCamelCase_:							Union[str, Any]		=  42
       UpperCamelCase_:							Optional[int]		=  RandomIterableDataset()

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

       # Edge case with a very small dataset
       UpperCamelCase_:							Any		=  RandomIterableDataset(max_length=2 )

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

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

   def  lowerCAmelCase__		(    self	:    List[Any] ):
       UpperCamelCase_:							Tuple		=  SkipDataLoader(list(range(16 ) )  ,		batch_size=4  ,		skip_batches=2 )
       self.assertListEqual([t.tolist() for t in dataloader]  ,		[[8, 9, 10, 11], [12, 13, 14, 15]] )

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

   def  lowerCAmelCase__		(    self	:    int ):
       UpperCamelCase_:							int		=  DataLoaderShard(list(range(16 ) )  ,		batch_size=4 )
       for idx, _ in enumerate(snake_case_ ):
           self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )

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




   def  lowerCAmelCase__		(    self	:    Any ):
       Accelerator()
       UpperCamelCase_:							Union[str, Any]		=  DataLoaderDispatcher(range(16 )  ,		batch_size=4 )
       for idx, _ in enumerate(snake_case_ ):
           self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )

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








import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCamelCase_  :	Any 				=					logging.get_logger(__name__)


lowerCamelCase_  :	Optional[int] 				=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_  :	str 				=					{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :	Union[str, Any] 				=					{
    """RUCAIBox/mvp""": 10_24,
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[Any]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  int    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Any    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  int    =	MvpTokenizer

			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    Any=None  ,		snake_case_	:    Any=None  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Tuple="replace"  ,		snake_case_	:    Dict="<s>"  ,		snake_case_	:    Dict="</s>"  ,		snake_case_	:    Tuple="</s>"  ,		snake_case_	:    int="<s>"  ,		snake_case_	:    Tuple="<unk>"  ,		snake_case_	:    Optional[int]="<pad>"  ,		snake_case_	:    Any="<mask>"  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(
							    snake_case_  ,		snake_case_  ,		tokenizer_file=snake_case_  ,		errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		trim_offsets=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Any		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
											UpperCamelCase_:							str		=  getattr(snake_case_  ,		pre_tok_state.pop("""type""" ) )
											UpperCamelCase_:							int		=  add_prefix_space
											UpperCamelCase_:							int		=  pre_tok_class(**snake_case_ )

							UpperCamelCase_:							int		=  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase_:							Tuple		=  """post_processor"""
							UpperCamelCase_:							Optional[int]		=  getattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )
							if tokenizer_component_instance:
											UpperCamelCase_:							Optional[int]		=  json.loads(tokenizer_component_instance.__getstate__() )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															UpperCamelCase_:							Union[str, Any]		=  tuple(state["""sep"""] )
											if "cls" in state:
															UpperCamelCase_:							List[Any]		=  tuple(state["""cls"""] )

											UpperCamelCase_:							List[str]		=  False

											if state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
															UpperCamelCase_:							str		=  add_prefix_space
															UpperCamelCase_:							Union[str, Any]		=  True

											if state.get("""trim_offsets"""  ,		snake_case_ ) != trim_offsets:
															UpperCamelCase_:							Optional[int]		=  trim_offsets
															UpperCamelCase_:							Any		=  True

											if changes_to_apply:
															UpperCamelCase_:							Optional[int]		=  getattr(snake_case_  ,		state.pop("""type""" ) )
															UpperCamelCase_:							Union[str, Any]		=  component_class(**snake_case_ )
															setattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							if self._mask_token is None:
											if self.verbose:
															logger.error("""Using mask_token, but it is not set yet.""" )
											return None
							return str(self._mask_token )

			@mask_token.setter
			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else value
							UpperCamelCase_:							Optional[int]		=  value

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs.""" )

							return super()._batch_encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Tuple ):
							UpperCamelCase_:							Any		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs.""" )

							return super()._encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							UpperCamelCase_:							Optional[int]		=  self._tokenizer.model.save(snake_case_  ,		name=snake_case_ )
							return tuple(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any=None ):
							UpperCamelCase_:							str		=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Tuple		=  [self.sep_token_id]
							UpperCamelCase_:							List[str]		=  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






