


'''simple docstring'''

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 	snake_case       (       snake_case__     ):





  """simple docstring"""




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







   """simple docstring"""
   lowerCamelCase_						=    p_stop
   lowerCamelCase_						=    max_length







  def __iter__(       self	):







   """simple docstring"""
   lowerCamelCase_						=    0
   lowerCamelCase_						=    False
   while not stop and count < self.max_length:
    yield count
    count += 1
    lowerCamelCase_						=    random.random() < self.p_stop





class 	snake_case       (       unittest.TestCase     ):





  """simple docstring"""




  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase=False     ,    UpperCamelCase=True	):







   """simple docstring"""
   lowerCamelCase_						=    [
       BatchSamplerShard(_UpperCamelCase     ,    2     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase     ,    even_batches=_UpperCamelCase	)
       for i in range(2	)
   ]
   lowerCamelCase_						=    [list(_UpperCamelCase	) for batch_sampler_shard in batch_sampler_shards]
   if not split_batches:
    self.assertListEqual([len(_UpperCamelCase	) for shard in batch_sampler_shards]     ,    [len(_UpperCamelCase	) for e in expected]	)
   self.assertListEqual(_UpperCamelCase     ,    _UpperCamelCase	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    BatchSampler(range(24	)     ,    batch_size=3     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase	)

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

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

   lowerCamelCase_						=    BatchSampler(range(2	)     ,    batch_size=3     ,    drop_last=_UpperCamelCase	)
   lowerCamelCase_						=    [[], []]
   self.check_batch_sampler_shards(_UpperCamelCase     ,    _UpperCamelCase	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    BatchSampler(range(24	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase	)

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

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

   lowerCamelCase_						=    BatchSampler(range(22	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase	)

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase	)

   lowerCamelCase_						=    BatchSampler(range(21	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase	)

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

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    BatchSampler(range(24	)     ,    batch_size=3     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

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

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    BatchSampler(range(24	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

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

   lowerCamelCase_						=    BatchSampler(range(22	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase     ,    even_batches=_UpperCamelCase	)

   # 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=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase     ,    even_batches=_UpperCamelCase	)

   lowerCamelCase_						=    BatchSampler(range(21	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   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(_UpperCamelCase     ,    _UpperCamelCase     ,    split_batches=_UpperCamelCase     ,    even_batches=_UpperCamelCase	)

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

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
   lowerCamelCase_						=    [BatchSamplerShard(_UpperCamelCase     ,    2     ,    _UpperCamelCase     ,    even_batches=_UpperCamelCase	) 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     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase=False     ,    UpperCamelCase=2     ,    UpperCamelCase=False	):







   """simple docstring"""
   random.seed(_UpperCamelCase	)
   lowerCamelCase_						=    list(_UpperCamelCase	)

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

   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(_UpperCamelCase	)     ,    len(_UpperCamelCase	)	)
    self.assertTrue(len(_UpperCamelCase	) % shard_batch_size == 0	)

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

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    42
   lowerCamelCase_						=    RandomIterableDataset()

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

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

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    BatchSampler(range(16	)     ,    batch_size=4     ,    drop_last=_UpperCamelCase	)
   lowerCamelCase_						=    SkipBatchSampler(_UpperCamelCase     ,    2	)
   self.assertListEqual(list(_UpperCamelCase	)     ,    [[8, 9, 10, 11], [12, 13, 14, 15]]	)






  def     snake_case						(       self	):







   """simple docstring"""
   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     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    DataLoader(list(range(16	)	)     ,    batch_size=4	)
   lowerCamelCase_						=    skip_first_batches(_UpperCamelCase     ,    num_batches=2	)
   self.assertListEqual([t.tolist() for t in new_dataloader]     ,    [[8, 9, 10, 11], [12, 13, 14, 15]]	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    DataLoaderShard(list(range(16	)	)     ,    batch_size=4	)
   for idx, _ in enumerate(_UpperCamelCase	):
    self.assertEqual(dataloader.end_of_dataloader     ,    idx == 3	)

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







  def     snake_case						(       self	):







   """simple docstring"""
   Accelerator()
   lowerCamelCase_						=    DataLoaderDispatcher(range(16	)     ,    batch_size=4	)
   for idx, _ in enumerate(_UpperCamelCase	):
    self.assertEqual(dataloader.end_of_dataloader     ,    idx == 3	)

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






from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

