



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			(					lowercase__):




				def __init__(						self					:						Any,					__lowercase					:						str=0.01,					__lowercase					:						str=1000						):
							lowercase__					  =				p_stop
							lowercase__					  =				max_length




				def __iter__(						self					:						List[str]						):
							lowercase__					  =				0
							lowercase__					  =				False
							while not stop and count < self.max_length:
										yield count
										count += 1
										lowercase__					  =				random.random() < self.p_stop
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						Any,					__lowercase					:						List[str],					__lowercase					:						Any,					__lowercase					:						Any=False,					__lowercase					:						int=True						):
							lowercase__					  =				[
							    BatchSamplerShard(__lowercase,					2,					__lowercase,					split_batches=__lowercase,					even_batches=__lowercase						)
							    for i in range(2						)
							]
							lowercase__					  =				[list(__lowercase						) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
										self.assertListEqual([len(__lowercase						) for shard in batch_sampler_shards],					[len(__lowercase						) for e in expected]						)
							self.assertListEqual(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Optional[int]						):
							# Check the shards when the dataset is a round multiple of total batch size.
							lowercase__					  =				BatchSampler(range(24						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase						)

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

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							lowercase__					  =				BatchSampler(range(21						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase						)

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							lowercase__					  =				BatchSampler(range(22						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase						)

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

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							lowercase__					  =				BatchSampler(range(20						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase						)

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

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

							lowercase__					  =				BatchSampler(range(2						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[[], []]
							self.check_batch_sampler_shards(__lowercase,					__lowercase						)




				def 							A__      (						self					:						List[str]						):
							# Check the shards when the dataset is a round multiple of batch size.
							lowercase__					  =				BatchSampler(range(24						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase						)

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

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

							lowercase__					  =				BatchSampler(range(22						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase						)

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

							lowercase__					  =				BatchSampler(range(21						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase						)

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

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




				def 							A__      (						self					:						List[str]						):
							# Check the shards when the dataset is a round multiple of total batch size.
							lowercase__					  =				BatchSampler(range(24						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					even_batches=__lowercase						)

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

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							lowercase__					  =				BatchSampler(range(21						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					even_batches=__lowercase						)

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							lowercase__					  =				BatchSampler(range(22						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					even_batches=__lowercase						)

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

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							lowercase__					  =				BatchSampler(range(20						),					batch_size=3,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					even_batches=__lowercase						)

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

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

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




				def 							A__      (						self					:						int						):
							# Check the shards when the dataset is a round multiple of batch size.
							lowercase__					  =				BatchSampler(range(24						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase,					even_batches=__lowercase						)

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

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

							lowercase__					  =				BatchSampler(range(22						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase,					even_batches=__lowercase						)

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							lowercase__					  =				BatchSampler(range(21						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase,					even_batches=__lowercase						)

							lowercase__					  =				BatchSampler(range(21						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				[
							    [[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(__lowercase,					__lowercase,					split_batches=__lowercase,					even_batches=__lowercase						)

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

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




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

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

							lowercase__					  =				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
							lowercase__					  =				iterable_dataset_lists[0]
							for l in iterable_dataset_lists[1:]:
										self.assertEqual(len(__lowercase						),					len(__lowercase						)						)
										self.assertTrue(len(__lowercase						) % shard_batch_size == 0						)

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

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




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				42
							lowercase__					  =				RandomIterableDataset()

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

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

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




				def 							A__      (						self					:						int						):
							lowercase__					  =				BatchSampler(range(16						),					batch_size=4,					drop_last=__lowercase						)
							lowercase__					  =				SkipBatchSampler(__lowercase,					2						)
							self.assertListEqual(list(__lowercase						),					[[8, 9, 10, 11], [12, 13, 14, 15]]						)




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




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				DataLoaderShard(list(range(16						)						),					batch_size=4						)
							for idx, _ in enumerate(__lowercase						):
										self.assertEqual(dataloader.end_of_dataloader,					idx == 3						)

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




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

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), len(grid[0]  )
			if (
			    min(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)  )

			lowercase__					  =				0
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row + 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row - 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col + 1						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col - 1						,  SCREAMING_SNAKE_CASE_  )

			visit.remove((row, col)  )
			return count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




