
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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.01       ,							SCREAMING_SNAKE_CASE_  :			int=1_0_0_0       )       ->     Tuple:
					lowercase_       =					p_stop
					lowercase_       =					max_length




	def __iter__(       self  :			List[Any]       )       ->     Dict:
					lowercase_       =					0
					lowercase_       =					False
					while not stop and count < self.max_length:
									yield count
									count += 1
									lowercase_       =					random.random() < self.p_stop

class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict=False       ,							SCREAMING_SNAKE_CASE_  :			Dict=True       )       ->     List[str]:
					lowercase_       =					[
					    BatchSamplerShard(SCREAMING_SNAKE_CASE_       ,							2       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
					    for i in range(2       )
					]
					lowercase_       =					[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 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					# Check the shards when the dataset is a round multiple of total batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					# Check the shards when the dataset is a round multiple of batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					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.
					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[[0, 1]], [[0, 1]]]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       )       ->     List[Any]:
					# Check the shards when the dataset is a round multiple of total batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
					    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
					]
					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.
					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[[0, 1]], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[int]:
					# Check the shards when the dataset is a round multiple of batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
					    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
					]
					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.
					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[[0, 1]], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					lowercase_       =					[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
					lowercase_       =					[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], [1_2, 1_3]]       )
					self.assertListEqual(list(batch_sampler_shards[1]       )       ,							[[3, 4], [9, 1_0, 1_1]]       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       ,							SCREAMING_SNAKE_CASE_  :			List[str]=2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       )       ->     Any:
					random.seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[
					    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_       )
					]
					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(SCREAMING_SNAKE_CASE_       )
									iterable_dataset_lists.append(list(SCREAMING_SNAKE_CASE_       )       )

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

					lowercase_       =					[]
					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 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					4_2
					lowercase_       =					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
					lowercase_       =					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 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					BatchSampler(range(1_6       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					SkipBatchSampler(SCREAMING_SNAKE_CASE_       ,							2       )
					self.assertListEqual(list(SCREAMING_SNAKE_CASE_       )       ,							[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]       )
	def 						_lowercase  (       self  :			Any       )       ->     Any:
					lowercase_       =					SkipDataLoader(list(range(1_6       )       )       ,							batch_size=4       ,							skip_batches=2       )
					self.assertListEqual([t.tolist() for t in dataloader]       ,							[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]       )
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					lowercase_       =					DataLoader(list(range(1_6       )       )       ,							batch_size=4       )
					lowercase_       =					skip_first_batches(SCREAMING_SNAKE_CASE_       ,							num_batches=2       )
					self.assertListEqual([t.tolist() for t in new_dataloader]       ,							[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]       )
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					lowercase_       =					DataLoaderShard(list(range(1_6       )       )       ,							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 						_lowercase  (       self  :			Any       )       ->     Any:
					Accelerator()
					lowercase_       =					DataLoaderDispatcher(range(1_6       )       ,							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 typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	int						 = ["input_features", "attention_mask"]


   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=80,       _lowerCamelCase :     int=1_60_00,       _lowerCamelCase :     Any=80,       _lowerCamelCase :     List[str]=0.0,       _lowerCamelCase :     int=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Optional[int]=True,       **_lowerCamelCase :     List[str],       ):

     '''simple docstring'''
     super().__init__(feature_size=_lowerCamelCase,       sampling_rate=_lowerCamelCase,       padding_value=_lowerCamelCase,       **_lowerCamelCase  )
     __A									= num_mel_bins
     __A									= do_ceptral_normalize
     __A									= normalize_means
     __A									= normalize_vars
     __A									= True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     np.ndarray,       ):

     '''simple docstring'''
     __A									= waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     __A									= torch.from_numpy(_lowerCamelCase  ).unsqueeze(0  )
     __A									= ta_kaldi.fbank(_lowerCamelCase,       num_mel_bins=self.num_mel_bins,       sample_frequency=self.sampling_rate  )
     return features.numpy()


   @staticmethod
   def 				_SCREAMING_SNAKE_CASE				(	_lowerCamelCase :     np.ndarray,       _lowerCamelCase :     int,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     float = 0.0,       ):

     '''simple docstring'''
     # make sure we normalize float32 arrays
     if normalize_means:
       __A									= x[:input_length].mean(axis=0  )
       __A									= np.subtract(_lowerCamelCase,       _lowerCamelCase  )
     if normalize_vars:
       __A									= x[:input_length].std(axis=0  )
       __A									= np.divide(_lowerCamelCase,       _lowerCamelCase  )

     if input_length < x.shape[0]:
       __A									= padding_value

     # make sure array is in float32
     __A									= x.astype(np.floataa  )

     return x


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     List[np.ndarray],       _lowerCamelCase :     Optional[np.ndarray] = None  ):

     '''simple docstring'''
     __A									= attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [
         self.utterance_cmvn(_lowerCamelCase,       _lowerCamelCase,       self.normalize_means,       self.normalize_vars,       self.padding_value  )
         for x, n in zip(_lowerCamelCase,       _lowerCamelCase  )
     ]






   def __call__(	self :     Optional[Any],       _lowerCamelCase :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],       _lowerCamelCase :     Union[bool, str, PaddingStrategy] = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     bool = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[Union[str, TensorType]] = None,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[bool] = None,       **_lowerCamelCase :     Optional[Any],       ):

     '''simple docstring'''

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
             f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
             f' {self.sampling_rate} and not {sampling_rate}.'  )
     else:
       logger.warning(
           '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''  )

     __A									= isinstance(_lowerCamelCase,       np.ndarray  ) and len(raw_speech.shape  ) > 1
     if is_batched_numpy and len(raw_speech.shape  ) > 2:
       raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
     __A									= is_batched_numpy or (
         isinstance(_lowerCamelCase,       (list, tuple)  ) and (isinstance(raw_speech[0],       (np.ndarray, tuple, list)  ))
     )

     if is_batched:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowerCamelCase,       np.ndarray  ):
       __A									= np.asarray(_lowerCamelCase,       dtype=np.floataa  )
     elif isinstance(_lowerCamelCase,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
       __A									= raw_speech.astype(np.floataa  )

     # always return batch
     if not is_batched:
       __A									= [raw_speech]

     # extract fbank features
     __A									= [self._extract_fbank_features(_lowerCamelCase  ) for waveform in raw_speech]

     # convert into correct format for padding
     __A									= BatchFeature({'''input_features''': features}  )

     __A									= self.pad(
         _lowerCamelCase,       padding=_lowerCamelCase,       max_length=_lowerCamelCase,       truncation=_lowerCamelCase,       pad_to_multiple_of=_lowerCamelCase,       return_attention_mask=_lowerCamelCase,       **_lowerCamelCase,       )

     # make sure list is in array format
     __A									= padded_inputs.get('''input_features'''  )
     if isinstance(input_features[0],       _lowerCamelCase  ):
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for feature in input_features]

     __A									= padded_inputs.get('''attention_mask'''  )
     if attention_mask is not None:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.intaa  ) for array in attention_mask]

     # Utterance-level cepstral mean and variance normalization
     if self.do_ceptral_normalize:
       __A									= (
           np.array(_lowerCamelCase,       dtype=np.intaa  )
           if self._get_padding_strategies(_lowerCamelCase,       max_length=_lowerCamelCase  ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       __A									= self.normalize(
           padded_inputs['''input_features'''],       attention_mask=_lowerCamelCase  )

     if return_tensors is not None:
       __A									= padded_inputs.convert_to_tensors(_lowerCamelCase  )

     return padded_inputs




