






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      (__lowercase			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=0.01				,		_UpperCamelCase=1_0_0_0   )	->							List[str]:
							UpperCAmelCase_    :      Any            =						p_stop
							UpperCAmelCase_    :      Dict            =						max_length



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





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=True   )	->							int:
							UpperCAmelCase_    :      List[Any]            =						[
							    BatchSamplerShard(_a				,		2				,		_a				,		split_batches=_a				,		even_batches=_a   )
							    for i in range(2   )
							]
							UpperCAmelCase_    :      Optional[int]            =						[list(_a   ) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
												self.assertListEqual([len(_a   ) for shard in batch_sampler_shards]				,		[len(_a   ) for e in expected]   )
							self.assertListEqual(_a				,		_a   )



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							# Check the shards when the dataset is a round multiple of total batch size.
							UpperCAmelCase_    :      List[str]            =						BatchSampler(range(2_4   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a   )

							UpperCAmelCase_    :      str            =						BatchSampler(range(2_4   )				,		batch_size=3				,		drop_last=_a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(_a				,		_a   )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							UpperCAmelCase_    :      Any            =						BatchSampler(range(2_1   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      str            =						[
							    [[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(_a				,		_a   )

							UpperCAmelCase_    :      Any            =						BatchSampler(range(2_1   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Union[str, Any]            =						[
							    [[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(_a				,		_a   )

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2_2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a   )

							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2_2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      str            =						[
							    [[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(_a				,		_a   )

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							UpperCAmelCase_    :      Optional[Any]            =						BatchSampler(range(2_0   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a   )

							UpperCAmelCase_    :      Union[str, Any]            =						BatchSampler(range(2_0   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[Any]            =						[
							    [[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(_a				,		_a   )

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

							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Dict            =						[[], []]
							self.check_batch_sampler_shards(_a				,		_a   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# Check the shards when the dataset is a round multiple of batch size.
							UpperCAmelCase_    :      Any            =						BatchSampler(range(2_4   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[Any]            =						[
							    [[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(_a				,		_a				,		split_batches=_a   )

							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2_4   )				,		batch_size=4				,		drop_last=_a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(_a				,		_a				,		split_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size.
							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2_2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      int            =						[
							    [[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(_a				,		_a				,		split_batches=_a   )

							UpperCAmelCase_    :      Any            =						BatchSampler(range(2_2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a				,		split_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2_1   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Dict            =						[
							    [[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(_a				,		_a				,		split_batches=_a   )

							UpperCAmelCase_    :      int            =						BatchSampler(range(2_1   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a				,		split_batches=_a   )

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

							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      List[Any]            =						[[], []]
							self.check_batch_sampler_shards(_a				,		_a				,		split_batches=_a   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# Check the shards when the dataset is a round multiple of total batch size.
							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2_4   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[Any]            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							UpperCAmelCase_    :      Any            =						BatchSampler(range(2_4   )				,		batch_size=3				,		drop_last=_a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(_a				,		_a				,		even_batches=_a   )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2_1   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Tuple            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							UpperCAmelCase_    :      str            =						BatchSampler(range(2_1   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[int]            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2_2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      List[str]            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							UpperCAmelCase_    :      str            =						BatchSampler(range(2_2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Dict            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
							# num_processes batch.
							UpperCAmelCase_    :      Optional[int]            =						BatchSampler(range(2_0   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Tuple            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

							UpperCAmelCase_    :      List[Any]            =						BatchSampler(range(2_0   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      str            =						[
							    [[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(_a				,		_a				,		even_batches=_a   )

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

							UpperCAmelCase_    :      Optional[Any]            =						BatchSampler(range(2   )				,		batch_size=3				,		drop_last=_a   )
							UpperCAmelCase_    :      Tuple            =						[[], []]
							self.check_batch_sampler_shards(_a				,		_a				,		even_batches=_a   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							# Check the shards when the dataset is a round multiple of batch size.
							UpperCAmelCase_    :      Tuple            =						BatchSampler(range(2_4   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      str            =						[
							    [[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(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

							UpperCAmelCase_    :      Union[str, Any]            =						BatchSampler(range(2_4   )				,		batch_size=4				,		drop_last=_a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size.
							UpperCAmelCase_    :      Optional[Any]            =						BatchSampler(range(2_2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[int]            =						[
							    [[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(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

							UpperCAmelCase_    :      int            =						BatchSampler(range(2_2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[Any]            =						[
							    [[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(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2_1   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Dict            =						[
							    [[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(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

							UpperCAmelCase_    :      Optional[int]            =						BatchSampler(range(2_1   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Any            =						[
							    [[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(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )

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

							UpperCAmelCase_    :      Dict            =						BatchSampler(range(2   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[int]            =						[[], []]
							self.check_batch_sampler_shards(_a				,		_a				,		split_batches=_a				,		even_batches=_a   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      int            =						[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
							UpperCAmelCase_    :      str            =						[BatchSamplerShard(_a				,		2				,		_a				,		even_batches=_a   ) 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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=2				,		_UpperCamelCase=False   )	->							List[Any]:
							random.seed(_a   )
							UpperCAmelCase_    :      str            =						list(_a   )

							UpperCAmelCase_    :      Dict            =						[
							    IterableDatasetShard(
							        _a				,		batch_size=_a				,		drop_last=_a				,		num_processes=_a				,		process_index=_a				,		split_batches=_a				,		)
							    for i in range(_a   )
							]
							UpperCAmelCase_    :      Optional[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(_a   )
												iterable_dataset_lists.append(list(_a   )   )

							UpperCAmelCase_    :      str            =						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
							UpperCAmelCase_    :      List[str]            =						iterable_dataset_lists[0]
							for l in iterable_dataset_lists[1:]:
												self.assertEqual(len(_a   )				,		len(_a   )   )
												self.assertTrue(len(_a   ) % shard_batch_size == 0   )

							UpperCAmelCase_    :      Any            =						[]
							for idx in range(0				,		len(_a   )				,		_a   ):
												for l in iterable_dataset_lists:
																	observed += l[idx : idx + shard_batch_size]

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      List[Any]            =						4_2
							UpperCAmelCase_    :      Union[str, Any]            =						RandomIterableDataset()

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

							# Edge case with a very small dataset
							UpperCAmelCase_    :      Union[str, Any]            =						RandomIterableDataset(max_length=2   )

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



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      List[Any]            =						BatchSampler(range(1_6   )				,		batch_size=4				,		drop_last=_a   )
							UpperCAmelCase_    :      Optional[int]            =						SkipBatchSampler(_a				,		2   )
							self.assertListEqual(list(_a   )				,		[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Dict            =						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 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Tuple            =						DataLoader(list(range(1_6   )   )				,		batch_size=4   )
							UpperCAmelCase_    :      List[str]            =						skip_first_batches(_a				,		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 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      int            =						DataLoaderShard(list(range(1_6   )   )				,		batch_size=4   )
							for idx, _ in enumerate(_a   ):
												self.assertEqual(dataloader.end_of_dataloader				,		idx == 3   )

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							Accelerator()
							UpperCAmelCase_    :      Optional[int]            =						DataLoaderDispatcher(range(1_6   )				,		batch_size=4   )
							for idx, _ in enumerate(_a   ):
												self.assertEqual(dataloader.end_of_dataloader				,		idx == 3   )

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









import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			np.ndarray		,__a      :			Union[int, Iterable[int]]		,__a      :			bool		,__a      :			int	)			->     Tuple[int, int]:

					"""simple docstring"""

					def constraint_to_multiple_of(__a      :			List[str]		,__a      :			Dict		,__a      :			Any=0		,__a      :			int=None	):
										_a	:    Dict         =		round(val / multiple	) * multiple

										if max_val is not None and x > max_val:
															_a	:    Any         =		math.floor(val / multiple	) * multiple

										if x < min_val:
															_a	:    Dict         =		math.ceil(val / multiple	) * multiple

										return x

					_a	:    Union[str, Any]         =		(output_size, output_size) if isinstance(__a		,__a	) else output_size

					_a      ,					_a	:    List[Any]         =		get_image_size(__a	)
					_a      ,					_a	:    Any         =		output_size

					# determine new height and width
					_a	:    Union[str, Any]         =		output_height / input_height
					_a	:    Tuple         =		output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width	) < abs(1 - scale_height	):
															# fit width
															_a	:    Optional[Any]         =		scale_width
										else:
															# fit height
															_a	:    Tuple         =		scale_height

					_a	:    Optional[Any]         =		constraint_to_multiple_of(scale_height * input_height		,multiple=__a	)
					_a	:    int         =		constraint_to_multiple_of(scale_width * input_width		,multiple=__a	)

					return (new_height, new_width)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = False       ,							_a = 1       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a	:    Optional[Any]         =		get_size_dict(_a       )
												_a	:    Any         =		do_resize
												_a	:    Dict         =		size
												_a	:    str         =		keep_aspect_ratio
												_a	:    Any         =		ensure_multiple_of
												_a	:    Optional[Any]         =		resample
												_a	:    List[Any]         =		do_rescale
												_a	:    int         =		rescale_factor
												_a	:    Any         =		do_normalize
												_a	:    Union[str, Any]         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a	:    List[str]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = False       ,							_a = 1       ,							_a = PILImageResampling.BICUBIC       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    str         =		get_size_dict(_a       )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
												_a	:    Optional[Any]         =		get_resize_output_image_size(
												    _a       ,							output_size=(size['''height'''], size['''width'''])       ,							keep_aspect_ratio=_a       ,							multiple=_a       ,							)
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												return normalize(_a       ,							mean=_a       ,							std=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							**_a       ,							)    ->    PIL.Image.Image:
												_a	:    Optional[int]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    Union[str, Any]         =		size if size is not None else self.size
												_a	:    str         =		get_size_dict(_a       )
												_a	:    List[Any]         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_a	:    Optional[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_a	:    str         =		resample if resample is not None else self.resample
												_a	:    str         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    Union[str, Any]         =		rescale_factor if rescale_factor is not None else self.rescale_factor
												_a	:    List[Any]         =		do_normalize if do_normalize is not None else self.do_normalize
												_a	:    str         =		image_mean if image_mean is not None else self.image_mean
												_a	:    Tuple         =		image_std if image_std is not None else self.image_std

												_a	:    Dict         =		make_list_of_images(_a       )

												if not valid_images(_a       ):
																	raise ValueError(
																	    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																	    '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

												if do_resize and size is None or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

												if do_rescale and rescale_factor is None:
																	raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

												# All transformations expect numpy arrays.
												_a	:    Dict         =		[to_numpy_array(_a       ) for image in images]

												if do_resize:
																	_a	:    int         =		[self.resize(image=_a       ,							size=_a       ,							resample=_a       ) for image in images]

												if do_rescale:
																	_a	:    Optional[Any]         =		[self.rescale(image=_a       ,							scale=_a       ) for image in images]

												if do_normalize:
																	_a	:    Optional[int]         =		[self.normalize(image=_a       ,							mean=_a       ,							std=_a       ) for image in images]

												_a	:    int         =		[to_channel_dimension_format(_a       ,							_a       ) for image in images]

												_a	:    Tuple         =		{'''pixel_values''': images}
												return BatchFeature(data=_a       ,							tensor_type=_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Any:
												_a	:    Optional[Any]         =		outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(_a       ) != len(_a       ):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

																	if is_torch_tensor(_a       ):
																						_a	:    List[Any]         =		target_sizes.numpy()

																	_a	:    str         =		[]

																	for idx in range(len(_a       )       ):
																						_a	:    str         =		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=_a       )
																						_a	:    Union[str, Any]         =		resized_logits[0].argmax(dim=0       )
																						semantic_segmentation.append(_a       )
												else:
																	_a	:    Tuple         =		logits.argmax(dim=1       )
																	_a	:    Union[str, Any]         =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

												return semantic_segmentation



