


'''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 			_lowercase (	__lowercase							):
   def __init__(						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=0.0_1       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=1000      ) ->							Tuple:
        __snake_case           =						p_stop
        __snake_case           =						max_length





   def __iter__(						self       :      Any      ) ->							Union[str, Any]:
        __snake_case           =						0
        __snake_case           =						False
        while not stop and count < self.max_length:
             yield count
             count += 1
             __snake_case           =						random.random() < self.p_stop




class 			_lowercase (	unittest.TestCase							):
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      str=False       ,   SCREAMING_SNAKE_CASE_       :      str=True      ) ->							Union[str, Any]:
        __snake_case           =						[
            BatchSamplerShard(SCREAMING_SNAKE_CASE_       ,   2       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )
            for i in range(2      )
        ]
        __snake_case           =						[list(SCREAMING_SNAKE_CASE_      ) for batch_sampler_shard in batch_sampler_shards]
        if not split_batches:
             self.assertListEqual([len(SCREAMING_SNAKE_CASE_      ) for shard in batch_sampler_shards]       ,   [len(SCREAMING_SNAKE_CASE_      ) for e in expected]      )
        self.assertListEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							str:
        # Check the shards when the dataset is a round multiple of total batch size.
        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        # Expected shouldn't change
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
        # num_processes batch.
        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        __snake_case           =						BatchSampler(range(20      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(20      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

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

        __snake_case           =						BatchSampler(range(2      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						[[], []]
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[Any]      ) ->							Union[str, Any]:
        # Check the shards when the dataset is a round multiple of batch size.
        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        # Expected shouldn't change
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size.
        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )

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

        __snake_case           =						BatchSampler(range(2      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						[[], []]
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      str      ) ->							str:
        # Check the shards when the dataset is a round multiple of total batch size.
        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        # Expected shouldn't change
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
        # num_processes batch.
        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        __snake_case           =						BatchSampler(range(20      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(20      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

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

        __snake_case           =						BatchSampler(range(2      )       ,   batch_size=3       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						[[], []]
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      int      ) ->							Tuple:
        # Check the shards when the dataset is a round multiple of batch size.
        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(24      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        # Expected shouldn't change
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size.
        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(22      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						BatchSampler(range(21      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )

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

        __snake_case           =						BatchSampler(range(2      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						[[], []]
        self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]      ) ->							Tuple:
        __snake_case           =						[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
        __snake_case           =						[BatchSamplerShard(SCREAMING_SNAKE_CASE_       ,   2       ,   SCREAMING_SNAKE_CASE_       ,   even_batches=SCREAMING_SNAKE_CASE_      ) 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 a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      Any       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      int=False       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=2       ,   SCREAMING_SNAKE_CASE_       :      int=False      ) ->							List[Any]:
        random.seed(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						list(SCREAMING_SNAKE_CASE_      )

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

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

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

        if not drop_last:
             while len(SCREAMING_SNAKE_CASE_      ) < len(SCREAMING_SNAKE_CASE_      ):
                  reference += reference
        self.assertListEqual(SCREAMING_SNAKE_CASE_       ,   reference[: len(SCREAMING_SNAKE_CASE_      )]      )
   def a  (						self       :      Dict      ) ->							Tuple:
        __snake_case           =						42
        __snake_case           =						RandomIterableDataset()

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

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

        self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )
        self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )
        self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )
        self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_       ,   split_batches=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[Any]      ) ->							str:
        __snake_case           =						BatchSampler(range(16      )       ,   batch_size=4       ,   drop_last=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						SkipBatchSampler(SCREAMING_SNAKE_CASE_       ,   2      )
        self.assertListEqual(list(SCREAMING_SNAKE_CASE_      )       ,   [[8, 9, 10, 11], [12, 13, 14, 15]]      )
   def a  (						self       :      str      ) ->							Union[str, Any]:
        __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 a  (						self       :      Any      ) ->							str:
        __snake_case           =						DataLoader(list(range(16      )      )       ,   batch_size=4      )
        __snake_case           =						skip_first_batches(SCREAMING_SNAKE_CASE_       ,   num_batches=2      )
        self.assertListEqual([t.tolist() for t in new_dataloader]       ,   [[8, 9, 10, 11], [12, 13, 14, 15]]      )
   def a  (						self       :      Dict      ) ->							Optional[Any]:
        __snake_case           =						DataLoaderShard(list(range(16      )      )       ,   batch_size=4      )
        for idx, _ in enumerate(SCREAMING_SNAKE_CASE_      ):
             self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3      )

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





   def a  (						self       :      Tuple      ) ->							Dict:
        Accelerator()
        __snake_case           =						DataLoaderDispatcher(range(16      )       ,   batch_size=4      )
        for idx, _ in enumerate(SCREAMING_SNAKE_CASE_      ):
             self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3      )

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







'''simple docstring'''



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)
_UpperCamelCase						:  Optional[Any]							       =				logging.getLogger(__name__)


_UpperCamelCase						:  List[Any]							       =				"Hello world! cécé herlolip"


_UpperCamelCase						:  int							       =				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       snake_case   (	snake_case      : int					,		snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							BertAbsConfig(
        temp_dir='.'					,		finetune_bert=snake_case					,		large=snake_case					,		share_emb=snake_case					,		use_bert_emb=snake_case					,		encoder='bert'					,		max_pos=512					,		enc_layers=6					,		enc_hidden_size=512					,		enc_heads=8					,		enc_ff_size=512					,		enc_dropout=0.2					,		dec_layers=6					,		dec_hidden_size=768					,		dec_heads=8					,		dec_ff_size=2048					,		dec_dropout=0.2					,		)
    lowerCAmelCase 						=							torch.load(snake_case					,		lambda snake_case					,		snake_case			: storage			)
    lowerCAmelCase 						=							AbsSummarizer(snake_case					,		torch.device('cpu'			)					,		snake_case			)
    original.eval()

    lowerCAmelCase 						=							BertAbsSummarizer(snake_case					,		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'			)
    lowerCAmelCase 						=							BertTokenizer.from_pretrained('bert-base-uncased'			)

    # prepare the model inputs
    lowerCAmelCase 						=							tokenizer.encode('This is sample éàalj\'-.'			)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).unsqueeze(0			)
    lowerCAmelCase 						=							tokenizer.encode('This is sample 3 éàalj\'-.'			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).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
    lowerCAmelCase 						=							encoder_input_ids
    lowerCAmelCase 						=							decoder_input_ids
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							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
    lowerCAmelCase 						=							original(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							original.generator(snake_case			)

    lowerCAmelCase 						=							new_model(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							new_model.generator(snake_case			)

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

    lowerCAmelCase 						=							torch.allclose(snake_case					,		snake_case					,		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__":
     _UpperCamelCase						:  List[str]							       =				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.",
     )
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





