

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						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Optional[int],lowercase_ :       Optional[int]=0.01,lowercase_ :       Union[str, Any]=1_0_0_0		)->    List[str]:





        '''simple docstring'''







        A__          =   p_stop
        A__          =   max_length


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





        '''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 ):







    """simple docstring"""





    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Union[str, Any],lowercase_ :       Optional[Any],lowercase_ :       int=False,lowercase_ :       Dict=True		)->    Tuple:





        '''simple docstring'''







        A__          =   [
            BatchSamplerShard(snake_case__,2,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)
            for i in range(2		)
        ]
        A__          =   [list(snake_case__		) for batch_sampler_shard in batch_sampler_shards]
        if not split_batches:
            self.assertListEqual([len(snake_case__		) for shard in batch_sampler_shards],[len(snake_case__		) for e in expected]		)
        self.assertListEqual(snake_case__,snake_case__		)


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   BatchSampler(range(2_4		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        A__          =   BatchSampler(range(2_4		),batch_size=3,drop_last=snake_case__		)
        # Expected shouldn't change
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        A__          =   BatchSampler(range(2_1		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        A__          =   BatchSampler(range(2_1		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        # 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(2_2		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        A__          =   BatchSampler(range(2_2		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,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.
        A__          =   BatchSampler(range(2_0		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

        A__          =   BatchSampler(range(2_0		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__		)

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

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


    def 						snake_case__     (      self :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   BatchSampler(range(2_4		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

        A__          =   BatchSampler(range(2_4		),batch_size=4,drop_last=snake_case__		)
        # Expected shouldn't change
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

        # Check the shards when the dataset is not a round multiple of batch size.
        A__          =   BatchSampler(range(2_2		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

        A__          =   BatchSampler(range(2_2		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        A__          =   BatchSampler(range(2_1		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

        A__          =   BatchSampler(range(2_1		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__		)

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

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


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





        '''simple docstring'''







        A__          =   BatchSampler(range(2_4		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_4		),batch_size=3,drop_last=snake_case__		)
        # Expected shouldn't change
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        A__          =   BatchSampler(range(2_1		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_1		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        # 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(2_2		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_2		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=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.
        A__          =   BatchSampler(range(2_0		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_0		),batch_size=3,drop_last=snake_case__		)
        A__          =   [
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,even_batches=snake_case__		)

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

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


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





        '''simple docstring'''







        A__          =   BatchSampler(range(2_4		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_4		),batch_size=4,drop_last=snake_case__		)
        # Expected shouldn't change
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

        # Check the shards when the dataset is not a round multiple of batch size.
        A__          =   BatchSampler(range(2_2		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_2		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        A__          =   BatchSampler(range(2_1		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

        A__          =   BatchSampler(range(2_1		),batch_size=4,drop_last=snake_case__		)
        A__          =   [
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(snake_case__,snake_case__,split_batches=snake_case__,even_batches=snake_case__		)

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

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


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        A__          =   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
        A__          =   [BatchSamplerShard(snake_case__,2,snake_case__,even_batches=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], [1_2, 1_3]]		)
        self.assertListEqual(list(batch_sampler_shards[1]		),[[3, 4], [9, 1_0, 1_1]]		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       Tuple,lowercase_ :       Union[str, Any],lowercase_ :       Optional[Any],lowercase_ :       Optional[Any]=False,lowercase_ :       Optional[Any]=2,lowercase_ :       int=False		)->    Dict:





        '''simple docstring'''







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

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

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

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

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


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





        '''simple docstring'''







        A__          =   4_2
        A__          =   RandomIterableDataset()

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

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

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


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        A__          =   BatchSampler(range(1_6		),batch_size=4,drop_last=snake_case__		)
        A__          =   SkipBatchSampler(snake_case__,2		)
        self.assertListEqual(list(snake_case__		),[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   SkipDataLoader(list(range(1_6		)		),batch_size=4,skip_batches=2		)
        self.assertListEqual([t.tolist() for t in dataloader],[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        A__          =   DataLoader(list(range(1_6		)		),batch_size=4		)
        A__          =   skip_first_batches(snake_case__,num_batches=2		)
        self.assertListEqual([t.tolist() for t in new_dataloader],[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]		)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        A__          =   DataLoaderShard(list(range(1_6		)		),batch_size=4		)
        for idx, _ in enumerate(snake_case__		):
            self.assertEqual(dataloader.end_of_dataloader,idx == 3		)

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


    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        Accelerator()
        A__          =   DataLoaderDispatcher(range(1_6		),batch_size=4		)
        for idx, _ in enumerate(snake_case__		):
            self.assertEqual(dataloader.end_of_dataloader,idx == 3		)

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





"""simple docstring"""







import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





