
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 						lowercase    (     lowerCamelCase__     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					Tuple=0.0_1	,							_UpperCamelCase   :					Union[str, Any]=1_000      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       p_stop
												SCREAMING_SNAKE_CASE    			=       max_length





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



												'''simple docstring'''

												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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					str=False	,							_UpperCamelCase   :					List[Any]=True      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [
												    BatchSamplerShard(__lowerCamelCase	,							2	,							__lowerCamelCase	,							split_batches=__lowerCamelCase	,							even_batches=__lowerCamelCase      )
												    for i in range(2      )
												]
												SCREAMING_SNAKE_CASE    			=       [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 	__snake_case(						self   :					List[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(24      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(20      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

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

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




							def 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(24      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

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

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




							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(24      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(20      )	,							batch_size=3	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

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

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




							def 	__snake_case(						self   :					int      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(24      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(22      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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.
												SCREAMING_SNAKE_CASE    			=       BatchSampler(range(21      )	,							batch_size=4	,							drop_last=__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [
												    [[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      )

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

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




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

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



												'''simple docstring'''

												random.seed(__lowerCamelCase      )
												SCREAMING_SNAKE_CASE    			=       list(__lowerCamelCase      )

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

												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(__lowerCamelCase      )	,							len(__lowerCamelCase      )      )
																	self.assertTrue(len(__lowerCamelCase      ) % shard_batch_size == 0      )

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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       42
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[str]      )					->				Tuple:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DataLoader(list(range(16      )      )	,							batch_size=4      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[str]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												Accelerator()
												SCREAMING_SNAKE_CASE    			=       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      )





import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_lowerCamelCase					:   Optional[int]							=		logging.getLogger(__name__)


_lowerCamelCase					:   Optional[int]							=		'''Hello world! cécé herlolip'''


_lowerCamelCase					:   List[Any]							=		namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       BertAbsConfig(
					    temp_dir="."      , finetune_bert=UpperCAmelCase__      , large=UpperCAmelCase__      , share_emb=UpperCAmelCase__      , use_bert_emb=UpperCAmelCase__      , encoder="bert"      , max_pos=5_1_2      , enc_layers=6      , enc_hidden_size=5_1_2      , enc_heads=8      , enc_ff_size=5_1_2      , enc_dropout=0.2      , dec_layers=6      , dec_hidden_size=7_6_8      , dec_heads=8      , dec_ff_size=2_0_4_8      , dec_dropout=0.2      , )
					SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__      , lambda UpperCAmelCase__      , UpperCAmelCase__  : storage  )
					SCREAMING_SNAKE_CASE    			=       AbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )      , UpperCAmelCase__  )
					original.eval()

					SCREAMING_SNAKE_CASE    			=       BertAbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )  )
					new_model.eval()

					# -------------------
					# Convert the weights
					# -------------------

					logging.info("convert the model"  )
					new_model.bert.load_state_dict(original.bert.state_dict()  )
					new_model.decoder.load_state_dict(original.decoder.state_dict()  )
					new_model.generator.load_state_dict(original.generator.state_dict()  )

					# ----------------------------------
					# Make sure the outpus are identical
					# ----------------------------------

					logging.info("Make sure that the models' outputs are identical"  )
					SCREAMING_SNAKE_CASE    			=       BertTokenizer.from_pretrained("bert-base-uncased"  )

					# prepare the model inputs
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample éàalj'-."  )
					encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample 3 éàalj'-."  )
					decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )

					# failsafe to make sure the weights reset does not affect the
					# loaded weights.
					assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight  )  ) == 0

					# forward pass
					SCREAMING_SNAKE_CASE    			=       encoder_input_ids
					SCREAMING_SNAKE_CASE    			=       decoder_input_ids
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None

					# The original model does not apply the geneator layer immediatly but rather in
					# the beam search (where it combines softmax + linear layer). Since we already
					# apply the softmax in our generation process we only apply the linear layer here.
					# We make sure that the outputs of the full stack are identical
					SCREAMING_SNAKE_CASE    			=       original(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       original.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       new_model(
					    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       new_model.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_model - output_original_model  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )
					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_generator - output_original_generator  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )

					SCREAMING_SNAKE_CASE    			=       torch.allclose(UpperCAmelCase__      , UpperCAmelCase__      , atol=1e-3  )
					if are_identical:
										logging.info("all weights are equal up to 1e-3"  )
					else:
										raise ValueError("the weights are different. The new model is likely different from the original one."  )

					# The model has been saved with torch.save(model) and this is bound to the exact
					# directory structure. We save the state_dict instead.
					logging.info("saving the model's state dictionary"  )
					torch.save(
					    new_model.state_dict()      , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"  )


if __name__ == "__main__":
							_lowerCamelCase					:   Union[str, Any]							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--bertabs_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path the official PyTorch dump.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the output PyTorch model.''',
							)
							_lowerCamelCase					:   Any							=		parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)




