




"""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       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			List[str]=0.01						,						lowerCAmelCase				:			str=1000					):
										lowerCAmelCase  =				p_stop
										lowerCAmelCase  =				max_length




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






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			List[Any]=False						,						lowerCAmelCase				:			Tuple=True					):
										lowerCAmelCase  =				[
										    BatchSamplerShard(lowerCAmelCase						,						2						,						lowerCAmelCase						,						split_batches=lowerCAmelCase						,						even_batches=lowerCAmelCase					)
										    for i in range(2					)
										]
										lowerCAmelCase  =				[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   __lowercase (							self				:			Union[str, Any]					):
										# Check the shards when the dataset is a round multiple of total batch size.
										lowerCAmelCase  =				BatchSampler(range(24					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				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  =				BatchSampler(range(21					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(20					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(20					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(2					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[[[0, 1, 0]], [[1, 0, 1]]]
										self.check_batch_sampler_shards(lowerCAmelCase						,						lowerCAmelCase					)

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


						def   __lowercase (							self				:			Union[str, Any]					):
										# Check the shards when the dataset is a round multiple of batch size.
										lowerCAmelCase  =				BatchSampler(range(24					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				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  =				BatchSampler(range(22					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(2					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[[[0, 1]], [[0, 1]]]
										self.check_batch_sampler_shards(lowerCAmelCase						,						lowerCAmelCase						,						split_batches=lowerCAmelCase					)

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


						def   __lowercase (							self				:			Optional[int]					):
										# Check the shards when the dataset is a round multiple of total batch size.
										lowerCAmelCase  =				BatchSampler(range(24					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				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  =				BatchSampler(range(21					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(20					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(20					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(2					)						,						batch_size=3						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[[[0, 1]], []]
										self.check_batch_sampler_shards(lowerCAmelCase						,						lowerCAmelCase						,						even_batches=lowerCAmelCase					)

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


						def   __lowercase (							self				:			Dict					):
										# Check the shards when the dataset is a round multiple of batch size.
										lowerCAmelCase  =				BatchSampler(range(24					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				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  =				BatchSampler(range(22					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(22					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(21					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[
										    [[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  =				BatchSampler(range(2					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				[[[0, 1]], []]
										self.check_batch_sampler_shards(lowerCAmelCase						,						lowerCAmelCase						,						split_batches=lowerCAmelCase						,						even_batches=lowerCAmelCase					)

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


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
										lowerCAmelCase  =				[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   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple=False						,						lowerCAmelCase				:			int=2						,						lowerCAmelCase				:			Tuple=False					):
										random.seed(lowerCAmelCase					)
										lowerCAmelCase  =				list(lowerCAmelCase					)

										lowerCAmelCase  =				[
										    IterableDatasetShard(
										        lowerCAmelCase						,						batch_size=lowerCAmelCase						,						drop_last=lowerCAmelCase						,						num_processes=lowerCAmelCase						,						process_index=lowerCAmelCase						,						split_batches=lowerCAmelCase						,						)
										    for i in range(lowerCAmelCase					)
										]
										lowerCAmelCase  =				[]
										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  =				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  =				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  =				[]
										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   __lowercase (							self				:			int					):
										lowerCAmelCase  =				42
										lowerCAmelCase  =				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  =				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   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				BatchSampler(range(16					)						,						batch_size=4						,						drop_last=lowerCAmelCase					)
										lowerCAmelCase  =				SkipBatchSampler(lowerCAmelCase						,						2					)
										self.assertListEqual(list(lowerCAmelCase					)						,						[[8, 9, 10, 11], [12, 13, 14, 15]]					)


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				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   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				DataLoader(list(range(16					)					)						,						batch_size=4					)
										lowerCAmelCase  =				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   __lowercase (							self				:			str					):
										lowerCAmelCase  =				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   __lowercase (							self				:			str					):
										Accelerator()
										lowerCAmelCase  =				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 argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

				lowerCAmelCase  =				{key.replace("""decoder"""      ,       """model""" ): val for key, val in state_dict.items()}

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       max_position_embeddings=args.max_target_positions      ,       num_layers=args.decoder_layers      ,       attention_heads=args.decoder_attention_heads      ,       ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.decoder_embed_dim      ,       layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="""gelu"""      ,       scale_embedding=not args.no_scale_embedding      ,       tie_word_embeddings=args.share_decoder_input_output_embed      ,       )

				lowerCAmelCase  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





