






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  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a=0.01       ,			a=10_00					)       ->    Optional[int]:
								snake_case_				=							p_stop
								snake_case_				=							max_length






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






class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a=False       ,			a=True					)       ->    Optional[int]:
								snake_case_				=							[
								    BatchSamplerShard(a       ,			2       ,			a       ,			split_batches=a       ,			even_batches=a					)
								    for i in range(2					)
								]
								snake_case_				=							[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					)       ->    Union[str, Any]:
								# Check the shards when the dataset is a round multiple of total batch size.
								snake_case_				=							BatchSampler(range(24					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							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.
								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(20					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(20					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(2					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(a       ,			a					)

								snake_case_				=							BatchSampler(range(2					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[[], []]
								self.check_batch_sampler_shards(a       ,			a					)




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

								snake_case_				=							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.
								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(2					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(a       ,			a       ,			split_batches=a					)

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




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

								snake_case_				=							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.
								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(20					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(20					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(2					)       ,			batch_size=3       ,			drop_last=a					)
								snake_case_				=							[[[0, 1]], []]
								self.check_batch_sampler_shards(a       ,			a       ,			even_batches=a					)

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




	def 							_UpperCamelCase			(       self					)       ->    Any:
								# Check the shards when the dataset is a round multiple of batch size.
								snake_case_				=							BatchSampler(range(24					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							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.
								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(22					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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					)

								snake_case_				=							BatchSampler(range(21					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[
								    [[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.
								snake_case_				=							BatchSampler(range(2					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							[[[0, 1]], []]
								self.check_batch_sampler_shards(a       ,			a       ,			split_batches=a       ,			even_batches=a					)

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




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								snake_case_				=							[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					)       ->    List[Any]:
								random.seed(a					)
								snake_case_				=							list(a					)

								snake_case_				=							[
								    IterableDatasetShard(
								        a       ,			batch_size=a       ,			drop_last=a       ,			num_processes=a       ,			process_index=a       ,			split_batches=a       ,			)
								    for i in range(a					)
								]
								snake_case_				=							[]
								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					)					)

								snake_case_				=							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
								snake_case_				=							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					)

								snake_case_				=							[]
								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					)       ->    Dict:
								snake_case_				=							42
								snake_case_				=							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
								snake_case_				=							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:
								snake_case_				=							BatchSampler(range(16					)       ,			batch_size=4       ,			drop_last=a					)
								snake_case_				=							SkipBatchSampler(a       ,			2					)
								self.assertListEqual(list(a					)       ,			[[8, 9, 10, 11], [12, 13, 14, 15]]					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							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:
								snake_case_				=							DataLoader(list(range(16					)					)       ,			batch_size=4					)
								snake_case_				=							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					)       ->    List[Any]:
								snake_case_				=							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					)       ->    Union[str, Any]:
								Accelerator()
								snake_case_				=							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					)













import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								snake_case_				=							None
								if self.use_token_type_ids:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size					)

								snake_case_				=							RobertaConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			vocab_size=2					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





