






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       (					lowercase__  ):



      """simple docstring"""


      def __init__( self   :     Union[str, Any]						, __lowercase   :     str=0.01						, __lowercase   :     Any=1000   )				-> List[str]:
        __UpperCAmelCase				:      str	 =    p_stop
        __UpperCAmelCase				:      Optional[int]	 =    max_length




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


class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     int						, __lowercase   :     int						, __lowercase   :     Tuple						, __lowercase   :     str=False						, __lowercase   :     Tuple=True   )				-> int:
        __UpperCAmelCase				:      Any	 =    [
            BatchSamplerShard(__lowercase						, 2						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )
            for i in range(2   )
        ]
        __UpperCAmelCase				:      str	 =    [list(__lowercase   ) for batch_sampler_shard in batch_sampler_shards]
        if not split_batches:
          self.assertListEqual([len(__lowercase   ) for shard in batch_sampler_shards]						, [len(__lowercase   ) for e in expected]   )
        self.assertListEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> str:
        __UpperCAmelCase				:      str	 =    BatchSampler(range(24   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    [
            [[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(__lowercase						, __lowercase   )

        __UpperCAmelCase				:      int	 =    BatchSampler(range(24   )						, batch_size=3						, drop_last=__lowercase   )
        # Expected shouldn't change
        self.check_batch_sampler_shards(__lowercase						, __lowercase   )

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        __UpperCAmelCase				:      Union[str, Any]	 =    BatchSampler(range(21   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      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(__lowercase						, __lowercase   )

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

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

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

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        __UpperCAmelCase				:      int	 =    BatchSampler(range(20   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    [
            [[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(__lowercase						, __lowercase   )

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

        # Check the shards when the dataset is very small.
        __UpperCAmelCase				:      Optional[int]	 =    BatchSampler(range(2   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    [[[0, 1, 0]], [[1, 0, 1]]]
        self.check_batch_sampler_shards(__lowercase						, __lowercase   )

        __UpperCAmelCase				:      Dict	 =    BatchSampler(range(2   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    [[], []]
        self.check_batch_sampler_shards(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     int   )				-> Dict:
        __UpperCAmelCase				:      Dict	 =    BatchSampler(range(24   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    BatchSampler(range(24   )						, batch_size=4						, drop_last=__lowercase   )
        # Expected shouldn't change
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, split_batches=__lowercase   )

        # Check the shards when the dataset is not a round multiple of batch size.
        __UpperCAmelCase				:      List[Any]	 =    BatchSampler(range(22   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      str	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase   )

        __UpperCAmelCase				:      List[Any]	 =    BatchSampler(range(22   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      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(__lowercase						, __lowercase						, split_batches=__lowercase   )

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        __UpperCAmelCase				:      int	 =    BatchSampler(range(21   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Dict	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase   )

        __UpperCAmelCase				:      str	 =    BatchSampler(range(21   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[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(__lowercase						, __lowercase						, split_batches=__lowercase   )

        # Check the shards when the dataset is very small.
        __UpperCAmelCase				:      List[str]	 =    BatchSampler(range(2   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Any	 =    [[[0, 1]], [[0, 1]]]
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, split_batches=__lowercase   )

        __UpperCAmelCase				:      Tuple	 =    BatchSampler(range(2   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      str	 =    [[], []]
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, split_batches=__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> Tuple:
        __UpperCAmelCase				:      Optional[int]	 =    BatchSampler(range(24   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      int	 =    [
            [[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(__lowercase						, __lowercase						, even_batches=__lowercase   )

        __UpperCAmelCase				:      int	 =    BatchSampler(range(24   )						, batch_size=3						, drop_last=__lowercase   )
        # Expected shouldn't change
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, even_batches=__lowercase   )

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        __UpperCAmelCase				:      Optional[Any]	 =    BatchSampler(range(21   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      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(__lowercase						, __lowercase						, even_batches=__lowercase   )

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

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

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

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        __UpperCAmelCase				:      Optional[Any]	 =    BatchSampler(range(20   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      int	 =    [
            [[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(__lowercase						, __lowercase						, even_batches=__lowercase   )

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

        # Check the shards when the dataset is very small.
        __UpperCAmelCase				:      Any	 =    BatchSampler(range(2   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      Dict	 =    [[[0, 1]], []]
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, even_batches=__lowercase   )

        __UpperCAmelCase				:      int	 =    BatchSampler(range(2   )						, batch_size=3						, drop_last=__lowercase   )
        __UpperCAmelCase				:      int	 =    [[], []]
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, even_batches=__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> int:
        __UpperCAmelCase				:      Optional[int]	 =    BatchSampler(range(24   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        __UpperCAmelCase				:      List[Any]	 =    BatchSampler(range(24   )						, batch_size=4						, drop_last=__lowercase   )
        # Expected shouldn't change
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        # Check the shards when the dataset is not a round multiple of batch size.
        __UpperCAmelCase				:      Dict	 =    BatchSampler(range(22   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      int	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    BatchSampler(range(22   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        __UpperCAmelCase				:      Tuple	 =    BatchSampler(range(21   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        __UpperCAmelCase				:      Union[str, Any]	 =    BatchSampler(range(21   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    [
            [[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(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

        # Check the shards when the dataset is very small.
        __UpperCAmelCase				:      Optional[int]	 =    BatchSampler(range(2   )						, batch_size=4						, drop_last=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    [[[0, 1]], []]
        self.check_batch_sampler_shards(__lowercase						, __lowercase						, split_batches=__lowercase						, even_batches=__lowercase   )

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

        __UpperCAmelCase				:      Optional[int]	 =    [
            IterableDatasetShard(
                __lowercase						, batch_size=__lowercase						, drop_last=__lowercase						, num_processes=__lowercase						, process_index=__lowercase						, split_batches=__lowercase						, )
            for i in range(__lowercase   )
        ]
        __UpperCAmelCase				:      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(__lowercase   )
          iterable_dataset_lists.append(list(__lowercase   )   )

        __UpperCAmelCase				:      Optional[Any]	 =    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
        __UpperCAmelCase				:      Any	 =    iterable_dataset_lists[0]
        for l in iterable_dataset_lists[1:]:
          self.assertEqual(len(__lowercase   )						, len(__lowercase   )   )
          self.assertTrue(len(__lowercase   ) % shard_batch_size == 0   )

        __UpperCAmelCase				:      Dict	 =    []
        for idx in range(0						, len(__lowercase   )						, __lowercase   ):
          for l in iterable_dataset_lists:
            observed += l[idx : idx + shard_batch_size]

        if not drop_last:
          while len(__lowercase   ) < len(__lowercase   ):
            reference += reference
        self.assertListEqual(__lowercase						, reference[: len(__lowercase   )]   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> int:
        __UpperCAmelCase				:      Union[str, Any]	 =    42
        __UpperCAmelCase				:      List[Any]	 =    RandomIterableDataset()

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

        # Edge case with a very small dataset
        __UpperCAmelCase				:      Tuple	 =    RandomIterableDataset(max_length=2   )

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

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




      def       UpperCAmelCase ( self   :     Optional[int]   )				-> str:
        Accelerator()
        __UpperCAmelCase				:      List[Any]	 =    DataLoaderDispatcher(range(16   )						, batch_size=4   )
        for idx, _ in enumerate(__lowercase   ):
          self.assertEqual(dataloader.end_of_dataloader						, idx == 3   )

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






'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'beit'


     def __init__(   self			,     lowerCamelCase=8192			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=224			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=True			,     lowerCamelCase=[3, 5, 7, 11]			,     lowerCamelCase=[1, 2, 3, 6]			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=256			,     lowerCamelCase=1			,     lowerCamelCase=False			,     lowerCamelCase=255			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							vocab_size
          __A	:       Tuple      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       str      =							hidden_dropout_prob
          __A	:       Dict      =							attention_probs_dropout_prob
          __A	:       int      =							initializer_range
          __A	:       List[str]      =							layer_norm_eps

          __A	:       str      =							image_size
          __A	:       Optional[int]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       List[str]      =							use_mask_token
          __A	:       Union[str, Any]      =							use_absolute_position_embeddings
          __A	:       Optional[int]      =							use_relative_position_bias
          __A	:       int      =							use_shared_relative_position_bias
          __A	:       int      =							layer_scale_init_value
          __A	:       Union[str, Any]      =							drop_path_rate
          __A	:       Dict      =							use_mean_pooling
          # decode head attributes (semantic segmentation)
          __A	:       List[str]      =							out_indices
          __A	:       str      =							pool_scales
          # auxiliary head attributes (semantic segmentation)
          __A	:       List[str]      =							use_auxiliary_head
          __A	:       Tuple      =							auxiliary_loss_weight
          __A	:       Dict      =							auxiliary_channels
          __A	:       Tuple      =							auxiliary_num_convs
          __A	:       List[str]      =							auxiliary_concat_input
          __A	:       int      =							semantic_loss_ignore_index







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				version.parse('1.11' )


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 1E-4

