

"""simple docstring"""






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__  ( lowerCAmelCase_     ):







			def __init__(			self			: str			,  lowercase			: List[Any]=0.0_1			,  lowercase			: List[str]=10_00  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				p_stop
								UpperCAmelCase			:   Optional[int]      =				max_length







			def __iter__(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				0
								UpperCAmelCase			:   str      =				False
								while not stop and count < self.max_length:
													yield count
													count += 1
													UpperCAmelCase			:   Optional[int]      =				random.random() < self.p_stop







class       snake_case__  ( unittest.TestCase     ):







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[str]			,  lowercase			: str			,  lowercase			: str=False			,  lowercase			: str=True  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				[
								    BatchSamplerShard(lowercase			,  2			,  lowercase			,  split_batches=lowercase			,  even_batches=lowercase  )
								    for i in range(2  )
								]
								UpperCAmelCase			:   List[Any]      =				[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 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				BatchSampler(range(24  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[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			:   Dict      =				BatchSampler(range(21  )			,  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], [0, 1, 2]],
								]
								self.check_batch_sampler_shards(lowercase			,  lowercase  )

								UpperCAmelCase			:   int      =				BatchSampler(range(21  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   Optional[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  )

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								UpperCAmelCase			:   Any      =				BatchSampler(range(22  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[str]      =				[
								    [[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			:   Union[str, Any]      =				BatchSampler(range(22  )			,  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 not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								UpperCAmelCase			:   Union[str, Any]      =				BatchSampler(range(20  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   Union[str, 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			:   Union[str, Any]      =				BatchSampler(range(20  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[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  )

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

								UpperCAmelCase			:   Any      =				BatchSampler(range(2  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   Any      =				[[], []]
								self.check_batch_sampler_shards(lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				BatchSampler(range(24  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   Optional[int]      =				[
								    [[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			:   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  )

								# Check the shards when the dataset is not a round multiple of batch size.
								UpperCAmelCase			:   Union[str, Any]      =				BatchSampler(range(22  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   Tuple      =				[
								    [[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[str]      =				BatchSampler(range(22  )			,  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 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, 0]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
								]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  split_batches=lowercase  )

								UpperCAmelCase			:   Dict      =				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  )

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

								UpperCAmelCase			:   Union[str, Any]      =				BatchSampler(range(2  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[str]      =				[[], []]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  split_batches=lowercase  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[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			,  even_batches=lowercase  )

								UpperCAmelCase			:   Optional[Any]      =				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			:   Dict      =				BatchSampler(range(21  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[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			:   List[str]      =				BatchSampler(range(21  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   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(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			:   List[Any]      =				BatchSampler(range(22  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   Any      =				[
								    [[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			:   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 and has not a multiple of
								# num_processes batch.
								UpperCAmelCase			:   Union[str, Any]      =				BatchSampler(range(20  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   List[str]      =				[
								    [[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			:   int      =				BatchSampler(range(20  )			,  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			,  even_batches=lowercase  )

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

								UpperCAmelCase			:   List[Any]      =				BatchSampler(range(2  )			,  batch_size=3			,  drop_last=lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				[[], []]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  even_batches=lowercase  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				BatchSampler(range(24  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   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			:   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			:   Optional[int]      =				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]],
								]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  split_batches=lowercase			,  even_batches=lowercase  )

								UpperCAmelCase			:   Optional[int]      =				BatchSampler(range(22  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   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			:   List[str]      =				BatchSampler(range(21  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   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			:   Tuple      =				BatchSampler(range(21  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   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			:   Tuple      =				[[[0, 1]], []]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  split_batches=lowercase			,  even_batches=lowercase  )

								UpperCAmelCase			:   Optional[int]      =				BatchSampler(range(2  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   Dict      =				[[], []]
								self.check_batch_sampler_shards(lowercase			,  lowercase			,  split_batches=lowercase			,  even_batches=lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



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





								'''simple docstring'''



								random.seed(lowercase  )
								UpperCAmelCase			:   str      =				list(lowercase  )

								UpperCAmelCase			:   Tuple      =				[
								    IterableDatasetShard(
								        lowercase			,  batch_size=lowercase			,  drop_last=lowercase			,  num_processes=lowercase			,  process_index=lowercase			,  split_batches=lowercase			,  )
								    for i in range(lowercase  )
								]
								UpperCAmelCase			:   Tuple      =				[]
								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			:   Optional[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			:   str      =				[]
								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 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				42
								UpperCAmelCase			:   List[str]      =				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			:   List[Any]      =				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 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				BatchSampler(range(16  )			,  batch_size=4			,  drop_last=lowercase  )
								UpperCAmelCase			:   Tuple      =				SkipBatchSampler(lowercase			,  2  )
								self.assertListEqual(list(lowercase  )			,  [[8, 9, 10, 11], [12, 13, 14, 15]]  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				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 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				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 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				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 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								Accelerator()
								UpperCAmelCase			:   int      =				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 __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						BlenderbotSmallConfig
			SCREAMING_SNAKE_CASE__     				=						{}
			SCREAMING_SNAKE_CASE__     				=						'''gelu'''







			def __init__(			self			: Tuple			,  lowercase			: Any			,  lowercase			: Optional[Any]=13			,  lowercase			: Any=7			,  lowercase			: List[Any]=True			,  lowercase			: List[str]=False			,  lowercase			: Optional[Any]=99			,  lowercase			: Union[str, Any]=32			,  lowercase			: List[Any]=2			,  lowercase			: Tuple=4			,  lowercase			: Union[str, Any]=37			,  lowercase			: str=0.1			,  lowercase			: Optional[Any]=0.1			,  lowercase			: Optional[Any]=20			,  lowercase			: str=2			,  lowercase			: int=1			,  lowercase			: Optional[int]=0			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				parent
								UpperCAmelCase			:   Union[str, Any]      =				batch_size
								UpperCAmelCase			:   Tuple      =				seq_length
								UpperCAmelCase			:   Dict      =				is_training
								UpperCAmelCase			:   str      =				use_labels
								UpperCAmelCase			:   Union[str, Any]      =				vocab_size
								UpperCAmelCase			:   Tuple      =				hidden_size
								UpperCAmelCase			:   List[Any]      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				num_attention_heads
								UpperCAmelCase			:   Dict      =				intermediate_size

								UpperCAmelCase			:   Optional[int]      =				hidden_dropout_prob
								UpperCAmelCase			:   Optional[Any]      =				attention_probs_dropout_prob
								UpperCAmelCase			:   Dict      =				max_position_embeddings
								UpperCAmelCase			:   List[Any]      =				eos_token_id
								UpperCAmelCase			:   List[Any]      =				pad_token_id
								UpperCAmelCase			:   Union[str, Any]      =				bos_token_id







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size  )
								UpperCAmelCase			:   Optional[int]      =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )			,  1  )
								UpperCAmelCase			:   Optional[int]      =				tf.concat([input_ids, eos_tensor]			,  axis=1  )

								UpperCAmelCase			:   str      =				ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size  )

								UpperCAmelCase			:   int      =				self.config_cls(
								    vocab_size=self.vocab_size			,  d_model=self.hidden_size			,  encoder_layers=self.num_hidden_layers			,  decoder_layers=self.num_hidden_layers			,  encoder_attention_heads=self.num_attention_heads			,  decoder_attention_heads=self.num_attention_heads			,  encoder_ffn_dim=self.intermediate_size			,  decoder_ffn_dim=self.intermediate_size			,  dropout=self.hidden_dropout_prob			,  attention_dropout=self.attention_probs_dropout_prob			,  max_position_embeddings=self.max_position_embeddings			,  eos_token_ids=[2]			,  bos_token_id=self.bos_token_id			,  pad_token_id=self.pad_token_id			,  decoder_start_token_id=self.pad_token_id			,  **self.config_updates			,  )
								UpperCAmelCase			:   Optional[Any]      =				prepare_blenderbot_small_inputs_dict(lowercase			,  lowercase			,  lowercase  )
								return config, inputs_dict







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				TFBlenderbotSmallModel(config=lowercase  ).get_decoder()
								UpperCAmelCase			:   List[Any]      =				inputs_dict["input_ids"]

								UpperCAmelCase			:   int      =				input_ids[:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["attention_mask"][:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["head_mask"]
								UpperCAmelCase			:   List[Any]      =				1

								# first forward pass
								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  attention_mask=lowercase			,  head_mask=lowercase			,  use_cache=lowercase  )

								UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase			:   Any      =				ids_tensor((self.batch_size, 3)			,  config.vocab_size  )
								UpperCAmelCase			:   Optional[int]      =				tf.cast(ids_tensor((self.batch_size, 3)			,  2  )			,  tf.inta  )

								# append to next input_ids and
								UpperCAmelCase			:   Union[str, Any]      =				tf.concat([input_ids, next_tokens]			,  axis=-1  )
								UpperCAmelCase			:   str      =				tf.concat([attention_mask, next_attn_mask]			,  axis=-1  )

								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  attention_mask=lowercase  )[0]
								UpperCAmelCase			:   List[str]      =				model(lowercase			,  attention_mask=lowercase			,  past_key_values=lowercase  )[0]

								self.parent.assertEqual(next_tokens.shape[1]			,  output_from_past.shape[1]  )

								# select random slice
								UpperCAmelCase			:   List[str]      =				int(ids_tensor((1,)			,  output_from_past.shape[-1]  )  )
								UpperCAmelCase			:   Optional[int]      =				output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase			:   Optional[Any]      =				output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowercase			,  lowercase			,  rtol=1E-3  )



def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Tuple							,      _lowercase       :		Tuple=None							,      _lowercase       :		Any=None							,      _lowercase       :		str=None							,      _lowercase       :		Optional[Any]=None							,      _lowercase       :		Dict=None							,      ):




					'''simple docstring'''






					if attention_mask is None:
										UpperCAmelCase			:   List[str]      =				tf.cast(tf.math.not_equal(_lowercase							,      config.pad_token_id	)							,      tf.inta	)
					if decoder_attention_mask is None:
										UpperCAmelCase			:   str      =				tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape							,      dtype=tf.inta	),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,      config.pad_token_id	)							,      tf.inta	),
										    ]							,      axis=-1							,      )
					if head_mask is None:
										UpperCAmelCase			:   Dict      =				tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
					if decoder_head_mask is None:
										UpperCAmelCase			:   Optional[Any]      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					if cross_attn_head_mask is None:
										UpperCAmelCase			:   str      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}







@require_tf
class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE__     				=						(
			    {
			        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
			        '''feature-extraction''': TFBlenderbotSmallModel,
			        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
			        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
			        '''translation''': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				TFBlenderbotSmallModelTester(self  )
								UpperCAmelCase			:   Dict      =				ConfigTester(self			,  config_class=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.config_tester.run_common_tests()







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowercase  )







@require_tokenizers
@require_tf
class       snake_case__  ( unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						[
			    '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
			    '''  i\'m going to throw up.\nand why is that?'''
			]
			SCREAMING_SNAKE_CASE__     				=						'''facebook/blenderbot_small-90M'''







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.tokenizer(self.src_text			,  return_tensors="tf"  )
								UpperCAmelCase			:   List[Any]      =				self.model.generate(
								    model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2			,  use_cache=lowercase			,  )
								UpperCAmelCase			:   Dict      =				self.tokenizer.batch_decode(generated_ids.numpy()			,  skip_special_tokens=lowercase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)


