




"""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 lowerCAmelCase__      (	SCREAMING_SNAKE_CASE__		):






			'''simple docstring'''



			def __init__(    self    , lowercase=0.01    , lowercase=1000    ):
							_lowerCamelCase	:     Any       =       p_stop
							_lowerCamelCase	:     int       =       max_length






			def __iter__(    self    ):
							_lowerCamelCase	:     Tuple       =       0
							_lowerCamelCase	:     int       =       False
							while not stop and count < self.max_length:
											yield count
											count += 1
											_lowerCamelCase	:     str       =       random.random() < self.p_stop






class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    , lowercase    , lowercase    , lowercase=False    , lowercase=True    ):
							_lowerCamelCase	:     Union[str, Any]       =       [
							    BatchSamplerShard(__lowerCAmelCase    , 2    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    , even_batches=__lowerCAmelCase    )
							    for i in range(2    )
							]
							_lowerCamelCase	:     Dict       =       [list(__lowerCAmelCase    ) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
											self.assertListEqual([len(__lowerCAmelCase    ) for shard in batch_sampler_shards]    , [len(__lowerCAmelCase    ) for e in expected]    )
							self.assertListEqual(__lowerCAmelCase    , __lowerCAmelCase    )
			def    A_       (    self    ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase	:     int       =       BatchSampler(range(24    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     List[str]       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    )

							_lowerCamelCase	:     int       =       BatchSampler(range(24    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							# Expected shouldn't change
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    )

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

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

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

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

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

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

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

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

							_lowerCamelCase	:     Tuple       =       BatchSampler(range(24    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							# Expected shouldn't change
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    )

							# Check the shards when the dataset is not a round multiple of batch size.
							_lowerCamelCase	:     List[Any]       =       BatchSampler(range(22    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Dict       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    )

							_lowerCamelCase	:     int       =       BatchSampler(range(22    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Tuple       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							_lowerCamelCase	:     List[str]       =       BatchSampler(range(21    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Optional[int]       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    )

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

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

							_lowerCamelCase	:     Union[str, Any]       =       BatchSampler(range(2    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Dict       =       [[], []]
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    )
			def    A_       (    self    ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase	:     Optional[Any]       =       BatchSampler(range(24    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     str       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

							_lowerCamelCase	:     List[str]       =       BatchSampler(range(24    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							# Expected shouldn't change
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							_lowerCamelCase	:     Union[str, Any]       =       BatchSampler(range(21    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Tuple       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							_lowerCamelCase	:     Optional[Any]       =       BatchSampler(range(22    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     List[Any]       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

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

							# 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	:     List[str]       =       BatchSampler(range(20    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Any       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

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

							# Check the shards when the dataset is very small.
							_lowerCamelCase	:     Optional[Any]       =       BatchSampler(range(2    )    , batch_size=3    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     List[str]       =       [[[0, 1]], []]
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    )

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

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

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

							_lowerCamelCase	:     str       =       BatchSampler(range(22    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Tuple       =       [
							    [[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(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    , even_batches=__lowerCAmelCase    )

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

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

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

							_lowerCamelCase	:     str       =       BatchSampler(range(2    )    , batch_size=4    , drop_last=__lowerCAmelCase    )
							_lowerCamelCase	:     Any       =       [[], []]
							self.check_batch_sampler_shards(__lowerCAmelCase    , __lowerCAmelCase    , split_batches=__lowerCAmelCase    , even_batches=__lowerCAmelCase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
							_lowerCamelCase	:     List[str]       =       [BatchSamplerShard(__lowerCAmelCase    , 2    , __lowerCAmelCase    , even_batches=__lowerCAmelCase    ) 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    , lowercase    , lowercase    , lowercase    , lowercase=False    , lowercase=2    , lowercase=False    ):
							random.seed(__lowerCAmelCase    )
							_lowerCamelCase	:     List[Any]       =       list(__lowerCAmelCase    )

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

							_lowerCamelCase	:     int       =       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	:     Dict       =       iterable_dataset_lists[0]
							for l in iterable_dataset_lists[1:]:
											self.assertEqual(len(__lowerCAmelCase    )    , len(__lowerCAmelCase    )    )
											self.assertTrue(len(__lowerCAmelCase    ) % shard_batch_size == 0    )

							_lowerCamelCase	:     Optional[int]       =       []
							for idx in range(0    , len(__lowerCAmelCase    )    , __lowerCAmelCase    ):
											for l in iterable_dataset_lists:
															observed += l[idx : idx + shard_batch_size]

							if not drop_last:
											while len(__lowerCAmelCase    ) < len(__lowerCAmelCase    ):
															reference += reference
							self.assertListEqual(__lowerCAmelCase    , reference[: len(__lowerCAmelCase    )]    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       42
							_lowerCamelCase	:     List[str]       =       RandomIterableDataset()

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

							# Edge case with a very small dataset
							_lowerCamelCase	:     Optional[Any]       =       RandomIterableDataset(max_length=2    )

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

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






			def    A_       (    self    ):
							Accelerator()
							_lowerCamelCase	:     Tuple       =       DataLoaderDispatcher(range(16    )    , batch_size=4    )
							for idx, _ in enumerate(__lowerCAmelCase    ):
											self.assertEqual(dataloader.end_of_dataloader    , idx == 3    )

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







'''simple docstring'''

import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
      # Initialising the CNN
      # (Sequential- Building the model layer by layer)
      UpperCamelCase      					=models.Sequential()

      # Step 1 - Convolution
      # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
      # (3,3) is the kernel size (filter matrix)
      classifier.add(
          layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
      )

      # Step 2 - Pooling
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Step 3 - Flattening
      classifier.add(layers.Flatten())

      # Step 4 - Full connection
      classifier.add(layers.Dense(units=128, activation="relu"))
      classifier.add(layers.Dense(units=1, activation="sigmoid"))

      # Compiling the CNN
      classifier.compile(
          optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
      )

      # Part 2 - Fitting the CNN to the images

      # Load Trained model weights

      # from keras.models import load_model
      # regressor=load_model('cnn.h5')

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      UpperCamelCase      					=train_datagen.flow_from_directory(
          "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      UpperCamelCase      					=test_datagen.flow_from_directory(
          "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
      )

      classifier.save("cnn.h5")

      # Part 3 - Making new predictions

      UpperCamelCase      					=tf.keras.preprocessing.image.load_img(
          "dataset/single_prediction/image.png", target_size=(64, 64)
      )
      UpperCamelCase      					=tf.keras.preprocessing.image.img_to_array(test_image)
      UpperCamelCase      					=np.expand_dims(test_image, axis=0)
      UpperCamelCase      					=classifier.predict(test_image)
      # training_set.class_indices
      if result[0][0] == 0:
            UpperCamelCase      					="Normal"
      if result[0][0] == 1:
            UpperCamelCase      					="Abnormality detected"






