


"""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   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.01							,					SCREAMING_SNAKE_CASE__							:					str=10_00				):
          lowerCamelCase__           =		p_stop
          lowerCamelCase__           =		max_length


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





class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Any=False							,					SCREAMING_SNAKE_CASE__							:					Dict=True				):
          lowerCamelCase__           =		[
              BatchSamplerShard(SCREAMING_SNAKE_CASE__							,					2							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)
              for i in range(2				)
          ]
          lowerCamelCase__           =		[list(SCREAMING_SNAKE_CASE__				) for batch_sampler_shard in batch_sampler_shards]
          if not split_batches:
                self.assertListEqual([len(SCREAMING_SNAKE_CASE__				) for shard in batch_sampler_shards]							,					[len(SCREAMING_SNAKE_CASE__				) for e in expected]				)
          self.assertListEqual(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          # Check the shards when the dataset is a round multiple of total batch size.
          lowerCamelCase__           =		BatchSampler(range(24				)							,					batch_size=3							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
              [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

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

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

          # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
          # num_processes batch.
          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=3							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
              [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

          # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
          # num_processes batch.
          lowerCamelCase__           =		BatchSampler(range(20				)							,					batch_size=3							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
              [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

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

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

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

          # Check the shards when the dataset is not a round multiple of batch size.
          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          lowerCamelCase__           =		BatchSampler(range(21				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		BatchSampler(range(21				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)

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

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

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

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

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

          # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
          # num_processes batch.
          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=3							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
              [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

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

          # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
          # num_processes batch.
          lowerCamelCase__           =		BatchSampler(range(20				)							,					batch_size=3							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
              [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

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

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

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

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

          # Check the shards when the dataset is not a round multiple of batch size.
          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		BatchSampler(range(22				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          lowerCamelCase__           =		BatchSampler(range(21				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		BatchSampler(range(21				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
              [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
          ]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		BatchSampler(range(2				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[[], []]
          self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
          lowerCamelCase__           =		[BatchSamplerShard(SCREAMING_SNAKE_CASE__							,					2							,					SCREAMING_SNAKE_CASE__							,					even_batches=SCREAMING_SNAKE_CASE__				) for i in range(2				)]

          self.assertEqual(len(batch_sampler_shards[0]				)							,					3				)
          self.assertEqual(len(batch_sampler_shards[1]				)							,					2				)

          self.assertListEqual(list(batch_sampler_shards[0]				)							,					[[0, 1, 2], [5, 6, 7, 8], [12, 13]]				)
          self.assertListEqual(list(batch_sampler_shards[1]				)							,					[[3, 4], [9, 10, 11]]				)
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str=False							,					SCREAMING_SNAKE_CASE__							:					int=2							,					SCREAMING_SNAKE_CASE__							:					List[Any]=False				):
          random.seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		list(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[
              IterableDatasetShard(
                  SCREAMING_SNAKE_CASE__							,					batch_size=SCREAMING_SNAKE_CASE__							,					drop_last=SCREAMING_SNAKE_CASE__							,					num_processes=SCREAMING_SNAKE_CASE__							,					process_index=SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__							,					)
              for i in range(SCREAMING_SNAKE_CASE__				)
          ]
          lowerCamelCase__           =		[]
          for iterable_dataset_shard in iterable_dataset_shards:
                # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
                random.seed(SCREAMING_SNAKE_CASE__				)
                iterable_dataset_lists.append(list(SCREAMING_SNAKE_CASE__				)				)

          lowerCamelCase__           =		batch_size // num_processes if split_batches else batch_size
          # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
          lowerCamelCase__           =		iterable_dataset_lists[0]
          for l in iterable_dataset_lists[1:]:
                self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					len(SCREAMING_SNAKE_CASE__				)				)
                self.assertTrue(len(SCREAMING_SNAKE_CASE__				) % shard_batch_size == 0				)

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

          if not drop_last:
                while len(SCREAMING_SNAKE_CASE__				) < len(SCREAMING_SNAKE_CASE__				):
                      reference += reference
          self.assertListEqual(SCREAMING_SNAKE_CASE__							,					reference[: len(SCREAMING_SNAKE_CASE__				)]				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		42
          lowerCamelCase__           =		RandomIterableDataset()

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

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

          self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)
          self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)
          self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)
          self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__							,					split_batches=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		BatchSampler(range(16				)							,					batch_size=4							,					drop_last=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		SkipBatchSampler(SCREAMING_SNAKE_CASE__							,					2				)
          self.assertListEqual(list(SCREAMING_SNAKE_CASE__				)							,					[[8, 9, 10, 11], [12, 13, 14, 15]]				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          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      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		DataLoader(list(range(16				)				)							,					batch_size=4				)
          lowerCamelCase__           =		skip_first_batches(SCREAMING_SNAKE_CASE__							,					num_batches=2				)
          self.assertListEqual([t.tolist() for t in new_dataloader]							,					[[8, 9, 10, 11], [12, 13, 14, 15]]				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		DataLoaderShard(list(range(16				)				)							,					batch_size=4				)
          for idx, _ in enumerate(SCREAMING_SNAKE_CASE__				):
                self.assertEqual(dataloader.end_of_dataloader							,					idx == 3				)

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


    def      _UpperCamelCase       ( self							:					List[Any]				):
          Accelerator()
          lowerCamelCase__           =		DataLoaderDispatcher(range(16				)							,					batch_size=4				)
          for idx, _ in enumerate(SCREAMING_SNAKE_CASE__				):
                self.assertEqual(dataloader.end_of_dataloader							,					idx == 3				)

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







"""simple docstring"""


from __future__ import annotations

_snake_case          =							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def   snake_case						(				_a:     list[list[int]]		,   _a:     list[int]		,   _a:     list[int]		,   _a:     int		,   _a:     list[list[int]]		,   )->		tuple[list[list[int]], list[list[int]]]:
      '''simple docstring'''


      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the reference grid
      lowerCamelCase__           =		1
      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the action grid

      lowerCamelCase__           =		init[0]
      lowerCamelCase__           =		init[1]
      lowerCamelCase__           =		0
      lowerCamelCase__           =		g + heuristic[x][y]  # cost from starting cell to destination cell
      lowerCamelCase__           =		[[f, g, x, y]]

      lowerCamelCase__           =		False  # flag that is set when search is complete
      lowerCamelCase__           =		False  # flag set if we can't find expand

      while not found and not resign:
            if len(_a							) == 0:
                  raise ValueError('Algorithm is unable to find solution'							)
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  lowerCamelCase__           =		cell.pop()
                  lowerCamelCase__           =		next_cell[2]
                  lowerCamelCase__           =		next_cell[3]
                  lowerCamelCase__           =		next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        lowerCamelCase__           =		True
                  else:
                        for i in range(len(_a							)							):  # to try out different valid actions
                              lowerCamelCase__           =		x + DIRECTIONS[i][0]
                              lowerCamelCase__           =		y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(_a							) and ya >= 0 and ya < len(grid[0]							):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          lowerCamelCase__           =		g + cost
                                          lowerCamelCase__           =		ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]							)
                                          lowerCamelCase__           =		1
                                          lowerCamelCase__           =		i
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		goal[0]
      lowerCamelCase__           =		goal[1]
      invpath.append([x, y]							)  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            lowerCamelCase__           =		x - DIRECTIONS[action[x][y]][0]
            lowerCamelCase__           =		y - DIRECTIONS[action[x][y]][1]
            lowerCamelCase__           =		xa
            lowerCamelCase__           =		ya
            invpath.append([x, y]							)

      lowerCamelCase__           =		[]
      for i in range(len(_a							)							):
            path.append(invpath[len(_a							) - 1 - i]							)
      return path, action


if __name__ == "__main__":
   _snake_case          =							[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   _snake_case          =							[0, 0]
   # all coordinates are given in format [y,x]
   _snake_case          =							[len(grid) - 1, len(grid[0]) - 1]
   _snake_case          =							1

   # the cost map which pushes the path closer to the goal
   _snake_case          =							[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         _snake_case          =							abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            _snake_case          =							99

   _snake_case      ,					_snake_case          =							search(grid, init, goal, cost, heuristic)

   print("ACTION MAP")
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])



