





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     snake_case_		(   a							):






 '''simple docstring'''





 def __init__(  self,		A_=0.01,		A_=1000		)	->       List[str]:
    UpperCAmelCase__						=p_stop
    UpperCAmelCase__						=max_length






 def __iter__(  self		)	->       Dict:
    UpperCAmelCase__						=0
    UpperCAmelCase__						=False
    while not stop and count < self.max_length:
       yield count
       count += 1
       UpperCAmelCase__						=random.random() < self.p_stop

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self,		A_,		A_,		A_=False,		A_=True		)	->       List[Any]:
    UpperCAmelCase__						=[
        BatchSamplerShard(A_,		2,		A_,		split_batches=A_,		even_batches=A_		)
        for i in range(2		)
    ]
    UpperCAmelCase__						=[list(A_		) for batch_sampler_shard in batch_sampler_shards]
    if not split_batches:
       self.assertListEqual([len(A_		) for shard in batch_sampler_shards],		[len(A_		) for e in expected]		)
    self.assertListEqual(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    # Check the shards when the dataset is a round multiple of total batch size.
    UpperCAmelCase__						=BatchSampler(range(24		),		batch_size=3,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_		)

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

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

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

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

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

    # 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__						=BatchSampler(range(20		),		batch_size=3,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_		)

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

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

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

 def  __UpperCAmelCase						(  self		)	->       Dict:
    # Check the shards when the dataset is a round multiple of batch size.
    UpperCAmelCase__						=BatchSampler(range(24		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_		)

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

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

    UpperCAmelCase__						=BatchSampler(range(22		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_		)

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

    UpperCAmelCase__						=BatchSampler(range(21		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_		)

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

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

 def  __UpperCAmelCase						(  self		)	->       Dict:
    # Check the shards when the dataset is a round multiple of total batch size.
    UpperCAmelCase__						=BatchSampler(range(24		),		batch_size=3,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		even_batches=A_		)

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

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

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

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

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

    # 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__						=BatchSampler(range(20		),		batch_size=3,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		even_batches=A_		)

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

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

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

 def  __UpperCAmelCase						(  self		)	->       Dict:
    # Check the shards when the dataset is a round multiple of batch size.
    UpperCAmelCase__						=BatchSampler(range(24		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_,		even_batches=A_		)

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

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

    UpperCAmelCase__						=BatchSampler(range(22		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_,		even_batches=A_		)

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

    UpperCAmelCase__						=BatchSampler(range(21		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=[
        [[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(A_,		A_,		split_batches=A_,		even_batches=A_		)

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

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

 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
    UpperCAmelCase__						=[BatchSamplerShard(A_,		2,		A_,		even_batches=A_		) 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,		A_,		A_,		A_,		A_=False,		A_=2,		A_=False		)	->       Optional[Any]:
    random.seed(A_		)
    UpperCAmelCase__						=list(A_		)

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

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

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

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

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=42
    UpperCAmelCase__						=RandomIterableDataset()

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

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

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

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=BatchSampler(range(16		),		batch_size=4,		drop_last=A_		)
    UpperCAmelCase__						=SkipBatchSampler(A_,		2		)
    self.assertListEqual(list(A_		),		[[8, 9, 10, 11], [12, 13, 14, 15]]		)

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=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		)	->       int:
    UpperCAmelCase__						=DataLoader(list(range(16		)		),		batch_size=4		)
    UpperCAmelCase__						=skip_first_batches(A_,		num_batches=2		)
    self.assertListEqual([t.tolist() for t in new_dataloader],		[[8, 9, 10, 11], [12, 13, 14, 15]]		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=DataLoaderShard(list(range(16		)		),		batch_size=4		)
    for idx, _ in enumerate(A_		):
       self.assertEqual(dataloader.end_of_dataloader,		idx == 3		)

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






 def  __UpperCAmelCase						(  self		)	->       Any:
    Accelerator()
    UpperCAmelCase__						=DataLoaderDispatcher(range(16		),		batch_size=4		)
    for idx, _ in enumerate(A_		):
       self.assertEqual(dataloader.end_of_dataloader,		idx == 3		)

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










from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





 @property
 def  __UpperCAmelCase						(  self		)	->       Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
       UpperCAmelCase__						={0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCAmelCase__						={0: "batch", 1: "sequence"}
    UpperCAmelCase__						={0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("input_ids", dynamic_axis),
            ("attention_mask", dynamic_axis),
            ("token_type_ids", dynamic_axis),
        ]		)



