

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		(  __magic_name__):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=0.01 ,					_SCREAMING_SNAKE_CASE=1000					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     p_stop
        SCREAMING_SNAKE_CASE_				:  str           =     max_length



  def __iter__(				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     0
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     False
        while not stop and count < self.max_length:
              yield count
              count += 1
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     random.random() < self.p_stop

class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
            BatchSamplerShard(_SCREAMING_SNAKE_CASE ,					2 ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)
            for i in range(2					)
        ]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [[[0, 1, 0]], [[1, 0, 1]]]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     BatchSampler(range(24					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     [[[0, 1]], [[0, 1]]]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     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.
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(21					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(22					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Tuple           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(20					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [[[0, 1]], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(2					) ,					batch_size=3 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(24					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(22					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  Dict           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [
            [[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					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BatchSampler(range(21					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     [
            [[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.
        SCREAMING_SNAKE_CASE_				:  str           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     [[[0, 1]], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BatchSampler(range(2					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [[], []]
        self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					split_batches=_SCREAMING_SNAKE_CASE ,					even_batches=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        random.seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     list(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [
            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					)
        ]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     []
        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					)					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     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_				:  int           =     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					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     []
        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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     42
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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
        SCREAMING_SNAKE_CASE_				:  List[str]           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     BatchSampler(range(16					) ,					batch_size=4 ,					drop_last=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     SkipBatchSampler(_SCREAMING_SNAKE_CASE ,					2					)
        self.assertListEqual(list(_SCREAMING_SNAKE_CASE					) ,					[[8, 9, 10, 11], [12, 13, 14, 15]]					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(list(range(16					)					) ,					batch_size=4					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        Accelerator()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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					)









from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from ..image_utils import load_image


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

  def __init__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        if self.framework == "tf":
              raise ValueError(f"The {self.__class__} is only available in PyTorch."					)

        requires_backends(self ,					'vision'					)
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

  def __call__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return super().__call__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     load_image(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.IntTensor([[image.height, image.width]]					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.image_processor(images=[image] ,					return_tensors='pt'					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer(text=inputs['words'] ,					boxes=inputs['boxes'] ,					return_tensors='pt'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     target_size
        return inputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     model_inputs.pop('target_size'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.model(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     outputs.__class__({'target_size': target_size, **outputs}					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  str           =     model_inputs['bbox']
        return model_outputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     model_outputs['target_size']
        if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

              def unnormalize(_SCREAMING_SNAKE_CASE					):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ]					)					)

              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model_outputs['logits'].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
              SCREAMING_SNAKE_CASE_				:  str           =     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     [unnormalize(_SCREAMING_SNAKE_CASE					) for bbox in model_outputs['bbox'].squeeze(0					)]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     [dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					) for vals in zip(scores.tolist() ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) if vals[0] > threshold]
        else:
              # This is a regular ForObjectDetectionModel
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.image_processor.post_process_object_detection(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  int           =     raw_annotations[0]
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     raw_annotation['scores']
              SCREAMING_SNAKE_CASE_				:  Any           =     raw_annotation['labels']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     raw_annotation['boxes']

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              SCREAMING_SNAKE_CASE_				:  str           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
                  for vals in zip(raw_annotation['scores'] ,					raw_annotation['labels'] ,					raw_annotation['boxes']					)
              ]

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.framework != "pt":
              raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'					)
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[Any]           =     box.int().tolist()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox






