




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 :      Any   ,					UpperCAmelCase__ :      List[Any]=0.01   ,					UpperCAmelCase__ :      Any=1_000)		->int:





           '''simple docstring'''

           A__ 					=       p_stop
           A__ 					=       max_length





    def __iter__(     self :      int)		->int:





           '''simple docstring'''

           A__ 					=       0
           A__ 					=       False
           while not stop and count < self.max_length:
                  yield count
                  count += 1
                  A__ 					=       random.random() < self.p_stop


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Any=False   ,					UpperCAmelCase__ :      List[str]=True)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       [
               BatchSamplerShard(lowerCAmelCase_   ,					2   ,					lowerCAmelCase_   ,					split_batches=lowerCAmelCase_   ,					even_batches=lowerCAmelCase_)
               for i in range(2)
           ]
           A__ 					=       [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 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       BatchSampler(range(24)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       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.
           A__ 					=       BatchSampler(range(21)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(21)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(22)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(22)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(20)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(20)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(2)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [[[0, 1, 0]], [[1, 0, 1]]]
           self.check_batch_sampler_shards(lowerCAmelCase_   ,					lowerCAmelCase_)

           A__ 					=       BatchSampler(range(2)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [[], []]
           self.check_batch_sampler_shards(lowerCAmelCase_   ,					lowerCAmelCase_)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       BatchSampler(range(24)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       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.
           A__ 					=       BatchSampler(range(22)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(22)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(21)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(21)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(2)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [[[0, 1]], [[0, 1]]]
           self.check_batch_sampler_shards(lowerCAmelCase_   ,					lowerCAmelCase_   ,					split_batches=lowerCAmelCase_)

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[str]:





           '''simple docstring'''

           A__ 					=       BatchSampler(range(24)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       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.
           A__ 					=       BatchSampler(range(21)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(21)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(22)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(22)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(20)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(20)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(2)   ,					batch_size=3   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [[[0, 1]], []]
           self.check_batch_sampler_shards(lowerCAmelCase_   ,					lowerCAmelCase_   ,					even_batches=lowerCAmelCase_)

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''simple docstring'''

           A__ 					=       BatchSampler(range(24)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       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.
           A__ 					=       BatchSampler(range(22)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(22)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(21)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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_)

           A__ 					=       BatchSampler(range(21)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [
               [[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.
           A__ 					=       BatchSampler(range(2)   ,					batch_size=4   ,					drop_last=lowerCAmelCase_)
           A__ 					=       [[[0, 1]], []]
           self.check_batch_sampler_shards(lowerCAmelCase_   ,					lowerCAmelCase_   ,					split_batches=lowerCAmelCase_   ,					even_batches=lowerCAmelCase_)

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

           A__ 					=       [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
           A__ 					=       [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 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Any=False   ,					UpperCAmelCase__ :      List[str]=2   ,					UpperCAmelCase__ :      List[Any]=False)		->Optional[Any]:





           '''simple docstring'''

           random.seed(lowerCAmelCase_)
           A__ 					=       list(lowerCAmelCase_)

           A__ 					=       [
               IterableDatasetShard(
                   lowerCAmelCase_   ,					batch_size=lowerCAmelCase_   ,					drop_last=lowerCAmelCase_   ,					num_processes=lowerCAmelCase_   ,					process_index=lowerCAmelCase_   ,					split_batches=lowerCAmelCase_   ,					)
               for i in range(lowerCAmelCase_)
           ]
           A__ 					=       []
           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_))

           A__ 					=       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
           A__ 					=       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)

           A__ 					=       []
           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 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->str:





           '''simple docstring'''

           A__ 					=       42
           A__ 					=       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
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      str)		->int:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[Any]:





           '''simple docstring'''

           A__ 					=       DataLoader(list(range(16))   ,					batch_size=4)
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->str:





           '''simple docstring'''

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      str)		->Optional[int]:





           '''simple docstring'''

           Accelerator()
           A__ 					=       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 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 				lowercase__		(					lowerCamelCase       ):
				return EnvironmentCommand()






class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    parser.add_parser('env'							)
										download_parser.set_defaults(func=lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    huggingface_hub.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'not installed'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'NA'
										if is_torch_available():
														import torch

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.__version__
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.is_available()

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'not installed'
										if is_transformers_available():
														import transformers

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    transformers.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'not installed'
										if is_accelerate_available():
														import accelerate

														_SCREAMING_SNAKE_CASE   :		str       		=    accelerate.__version__

										_SCREAMING_SNAKE_CASE   :		str       		=    'not installed'
										if is_xformers_available():
														import xformers

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xformers.__version__

										_SCREAMING_SNAKE_CASE   :		Any       		=    {
										    '`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(lowerCAmelCase_							)							)

										return info







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"

