


"""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 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       Dict    ,    UpperCAmelCase__    :       List[str]=0.01    ,    UpperCAmelCase__    :       Optional[int]=1_0_0_0     ) ->			Tuple:
		__SCREAMING_SNAKE_CASE		     =       p_stop
		__SCREAMING_SNAKE_CASE		     =       max_length




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




class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





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




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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_4     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		# Expected shouldn't change
		self.check_batch_sampler_shards(UpperCAmelCase__    ,    UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_1     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__     )

		# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
		# num_processes batch.
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__     )

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__     )

		# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
		# num_processes batch.
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_0     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__     )

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_0     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [[], []]
		self.check_batch_sampler_shards(UpperCAmelCase__    ,    UpperCAmelCase__     )




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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_4     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		# Expected shouldn't change
		self.check_batch_sampler_shards(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_1     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__     )

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

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




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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_4     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		# Expected shouldn't change
		self.check_batch_sampler_shards(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_1     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
		# num_processes batch.
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
		# num_processes batch.
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_0     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_0     )    ,    batch_size=3    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

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

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




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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_4     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		# Expected shouldn't change
		self.check_batch_sampler_shards(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		# Check the shards when the dataset is not a round multiple of batch size.
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_2     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

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

		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(2_1     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       [
		    [[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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    split_batches=UpperCAmelCase__    ,    even_batches=UpperCAmelCase__     )

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

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




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

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

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

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

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




	def 						UpperCAmelCase_   (				self    :       str     ) ->			str:
		__SCREAMING_SNAKE_CASE		     =       4_2
		__SCREAMING_SNAKE_CASE		     =       RandomIterableDataset()

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

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

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




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Union[str, Any]:
		__SCREAMING_SNAKE_CASE		     =       BatchSampler(range(1_6     )    ,    batch_size=4    ,    drop_last=UpperCAmelCase__     )
		__SCREAMING_SNAKE_CASE		     =       SkipBatchSampler(UpperCAmelCase__    ,    2     )
		self.assertListEqual(list(UpperCAmelCase__     )    ,    [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]     )




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

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




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

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




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
