

'''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        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__=0.01		,					UpperCamelCase__=1000							)    ->   int:





           '''simple docstring'''
           A_						       =   p_stop
           A_						       =   max_length







    def __iter__(       self							)    ->   Dict:





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


class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__=False		,					UpperCamelCase__=True							)    ->   int:





           '''simple docstring'''
           A_						       =   [
               BatchSamplerShard(UpperCamelCase__		,					2		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)
               for i in range(2							)
           ]
           A_						       =   [list(UpperCamelCase__							) for batch_sampler_shard in batch_sampler_shards]
           if not split_batches:
                  self.assertListEqual([len(UpperCamelCase__							) for shard in batch_sampler_shards]		,					[len(UpperCamelCase__							) for e in expected]							)
           self.assertListEqual(UpperCamelCase__		,					UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           # Check the shards when the dataset is a round multiple of total batch size.
           A_						       =   BatchSampler(range(24							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

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

           # Check the shards when the dataset is a round multiple of batch size but not total batch size.
           A_						       =   BatchSampler(range(21							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   BatchSampler(range(21							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
           # num_processes batch.
           A_						       =   BatchSampler(range(22							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   BatchSampler(range(22							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
           # num_processes batch.
           A_						       =   BatchSampler(range(20							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   BatchSampler(range(20							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__							)

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

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



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           # Check the shards when the dataset is a round multiple of batch size.
           A_						       =   BatchSampler(range(24							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__							)

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

           # Check the shards when the dataset is not a round multiple of batch size.
           A_						       =   BatchSampler(range(22							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(22							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size or num_processes.
           A_						       =   BatchSampler(range(21							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(21							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__							)

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

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



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           # Check the shards when the dataset is a round multiple of total batch size.
           A_						       =   BatchSampler(range(24							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

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

           # Check the shards when the dataset is a round multiple of batch size but not total batch size.
           A_						       =   BatchSampler(range(21							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(21							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
           # num_processes batch.
           A_						       =   BatchSampler(range(22							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(22							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
           # num_processes batch.
           A_						       =   BatchSampler(range(20							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(20							)		,					batch_size=3		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
               [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							)

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

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



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           # Check the shards when the dataset is a round multiple of batch size.
           A_						       =   BatchSampler(range(24							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)

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

           # Check the shards when the dataset is not a round multiple of batch size.
           A_						       =   BatchSampler(range(22							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(22							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           # Check the shards when the dataset is not a round multiple of batch size or num_processes.
           A_						       =   BatchSampler(range(21							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)

           A_						       =   BatchSampler(range(21							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   [
               [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
               [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
           ]
           self.check_batch_sampler_shards(UpperCamelCase__		,					UpperCamelCase__		,					split_batches=UpperCamelCase__		,					even_batches=UpperCamelCase__							)

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

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



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
           A_						       =   [BatchSamplerShard(UpperCamelCase__		,					2		,					UpperCamelCase__		,					even_batches=UpperCamelCase__							) for i in range(2							)]

           self.assertEqual(len(batch_sampler_shards[0]							)		,					3							)
           self.assertEqual(len(batch_sampler_shards[1]							)		,					2							)

           self.assertListEqual(list(batch_sampler_shards[0]							)		,					[[0, 1, 2], [5, 6, 7, 8], [12, 13]]							)
           self.assertListEqual(list(batch_sampler_shards[1]							)		,					[[3, 4], [9, 10, 11]]							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__=False		,					UpperCamelCase__=2		,					UpperCamelCase__=False							)    ->   Optional[Any]:





           '''simple docstring'''
           random.seed(UpperCamelCase__							)
           A_						       =   list(UpperCamelCase__							)

           A_						       =   [
               IterableDatasetShard(
                   UpperCamelCase__		,					batch_size=UpperCamelCase__		,					drop_last=UpperCamelCase__		,					num_processes=UpperCamelCase__		,					process_index=UpperCamelCase__		,					split_batches=UpperCamelCase__		,					)
               for i in range(UpperCamelCase__							)
           ]
           A_						       =   []
           for iterable_dataset_shard in iterable_dataset_shards:
                  # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
                  random.seed(UpperCamelCase__							)
                  iterable_dataset_lists.append(list(UpperCamelCase__							)							)

           A_						       =   batch_size // num_processes if split_batches else batch_size
           # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
           A_						       =   iterable_dataset_lists[0]
           for l in iterable_dataset_lists[1:]:
                  self.assertEqual(len(UpperCamelCase__							)		,					len(UpperCamelCase__							)							)
                  self.assertTrue(len(UpperCamelCase__							) % shard_batch_size == 0							)

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

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



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   42
           A_						       =   RandomIterableDataset()

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

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

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



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   BatchSampler(range(16							)		,					batch_size=4		,					drop_last=UpperCamelCase__							)
           A_						       =   SkipBatchSampler(UpperCamelCase__		,					2							)
           self.assertListEqual(list(UpperCamelCase__							)		,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   SkipDataLoader(list(range(16							)							)		,					batch_size=4		,					skip_batches=2							)
           self.assertListEqual([t.tolist() for t in dataloader]		,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   DataLoader(list(range(16							)							)		,					batch_size=4							)
           A_						       =   skip_first_batches(UpperCamelCase__		,					num_batches=2							)
           self.assertListEqual([t.tolist() for t in new_dataloader]		,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   DataLoaderShard(list(range(16							)							)		,					batch_size=4							)
           for idx, _ in enumerate(UpperCamelCase__							):
                  self.assertEqual(dataloader.end_of_dataloader		,					idx == 3							)

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







    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           Accelerator()
           A_						       =   DataLoaderDispatcher(range(16							)		,					batch_size=4							)
           for idx, _ in enumerate(UpperCamelCase__							):
                  self.assertEqual(dataloader.end_of_dataloader		,					idx == 3							)

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



'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
