import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import WhisperFeatureExtractor

if is_torch_available():
				import torch

__lowercase										= random.Random()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=1.0  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None     ):
				'''simple docstring'''







				if rng is None:
								__UpperCamelCase					:List[Any]        =		global_rng

				__UpperCamelCase					:Optional[int]        =		[]
				for batch_idx in range(shape[0]     ):
								values.append([]     )
								for _ in range(shape[1]     ):
												values[-1].append(rng.random() * scale     )

				return values


@require_torch
@require_torchaudio
class      lowerCamelCase_					(       unittest.TestCase			):






				'''simple docstring'''






				def __init__(     self			,			__lowercase			,			__lowercase=7			,			__lowercase=400			,			__lowercase=2_000			,			__lowercase=10			,			__lowercase=160			,			__lowercase=8			,			__lowercase=0.0			,			__lowercase=4_000			,			__lowercase=False			,			__lowercase=True			,			)	->     List[str]:
								__UpperCamelCase					:Optional[Any]        =		parent
								__UpperCamelCase					:List[Any]        =		batch_size
								__UpperCamelCase					:Optional[Any]        =		min_seq_length
								__UpperCamelCase					:Any        =		max_seq_length
								__UpperCamelCase					:Optional[Any]        =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCamelCase					:List[str]        =		padding_value
								__UpperCamelCase					:Union[str, Any]        =		sampling_rate
								__UpperCamelCase					:int        =		return_attention_mask
								__UpperCamelCase					:Dict        =		do_normalize
								__UpperCamelCase					:Tuple        =		feature_size
								__UpperCamelCase					:List[Any]        =		chunk_length
								__UpperCamelCase					:str        =		hop_length







				def       UpperCamelCase__       (     self)	->     int:
								return {
								    "feature_size": self.feature_size,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								    "do_normalize": self.do_normalize,
								}







				def       UpperCamelCase__       (     self			,			__lowercase=False			,			__lowercase=False)	->     Optional[int]:
								def _flatten(__lowercase):
												return list(itertools.chain(*__lowercase))

								if equal_length:
												__UpperCamelCase					:str        =		[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
								else:
												# make sure that inputs increase in size
												__UpperCamelCase					:Optional[Any]        =		[
												    floats_list((x, self.feature_size))
												    for x in range(self.min_seq_length			,			self.max_seq_length			,			self.seq_length_diff)
												]
								if numpify:
												__UpperCamelCase					:str        =		[np.asarray(__lowercase) for x in speech_inputs]
								return speech_inputs


@require_torch
@require_torchaudio
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






				'''simple docstring'''






				a__     :						Optional[Any]      =				WhisperFeatureExtractor if is_speech_available() else None







				def       UpperCamelCase__       (     self)	->     List[Any]:
								__UpperCamelCase					:int        =		WhisperFeatureExtractionTester(self)







				def       UpperCamelCase__       (     self)	->     Union[str, Any]:
								__UpperCamelCase					:str        =		self.feature_extraction_class(**self.feat_extract_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
												__UpperCamelCase					:List[Any]        =		feat_extract_first.save_pretrained(__lowercase)[0]
												check_json_file_has_correct_format(__lowercase)
												__UpperCamelCase					:List[Any]        =		self.feature_extraction_class.from_pretrained(__lowercase)

								__UpperCamelCase					:str        =		feat_extract_first.to_dict()
								__UpperCamelCase					:Optional[int]        =		feat_extract_second.to_dict()
								__UpperCamelCase					:Any        =		feat_extract_first.mel_filters
								__UpperCamelCase					:int        =		feat_extract_second.mel_filters
								self.assertTrue(np.allclose(__lowercase			,			__lowercase))
								self.assertEqual(__lowercase			,			__lowercase)







				def       UpperCamelCase__       (     self)	->     List[str]:
								__UpperCamelCase					:Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
												__UpperCamelCase					:Optional[int]        =		os.path.join(__lowercase			,			'''feat_extract.json''')
												feat_extract_first.to_json_file(__lowercase)
												__UpperCamelCase					:Optional[int]        =		self.feature_extraction_class.from_json_file(__lowercase)

								__UpperCamelCase					:Union[str, Any]        =		feat_extract_first.to_dict()
								__UpperCamelCase					:Optional[Any]        =		feat_extract_second.to_dict()
								__UpperCamelCase					:Dict        =		feat_extract_first.mel_filters
								__UpperCamelCase					:str        =		feat_extract_second.mel_filters
								self.assertTrue(np.allclose(__lowercase			,			__lowercase))
								self.assertEqual(__lowercase			,			__lowercase)







				def       UpperCamelCase__       (     self)	->     Optional[Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCamelCase					:Any        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
								# create three inputs of length 800, 1000, and 1200
								__UpperCamelCase					:int        =		[floats_list((1, x))[0] for x in range(800			,			1_400			,			200)]
								__UpperCamelCase					:Union[str, Any]        =		[np.asarray(__lowercase) for speech_input in speech_inputs]

								# Test feature size
								__UpperCamelCase					:Union[str, Any]        =		feature_extractor(__lowercase			,			padding='''max_length'''			,			return_tensors='''np''').input_features
								self.assertTrue(input_features.ndim == 3)
								self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
								self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)

								# Test not batched input
								__UpperCamelCase					:Optional[int]        =		feature_extractor(speech_inputs[0]			,			return_tensors='''np''').input_features
								__UpperCamelCase					:int        =		feature_extractor(np_speech_inputs[0]			,			return_tensors='''np''').input_features
								self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

								# Test batched
								__UpperCamelCase					:Dict        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								__UpperCamelCase					:str        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
												self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

								# Test 2-D numpy arrays are batched.
								__UpperCamelCase					:Any        =		[floats_list((1, x))[0] for x in (800, 800, 800)]
								__UpperCamelCase					:str        =		np.asarray(__lowercase)
								__UpperCamelCase					:List[Any]        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								__UpperCamelCase					:int        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
												self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

								# Test truncation required
								__UpperCamelCase					:Optional[Any]        =		[floats_list((1, x))[0] for x in range(200			,			(feature_extractor.n_samples + 500)			,			200)]
								__UpperCamelCase					:Tuple        =		[np.asarray(__lowercase) for speech_input in speech_inputs]

								__UpperCamelCase					:Optional[int]        =		[x[: feature_extractor.n_samples] for x in speech_inputs]
								__UpperCamelCase					:Any        =		[np.asarray(__lowercase) for speech_input in speech_inputs_truncated]

								__UpperCamelCase					:str        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								__UpperCamelCase					:Union[str, Any]        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
								for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
												self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))







				def       UpperCamelCase__       (     self)	->     Union[str, Any]:
								import torch

								__UpperCamelCase					:int        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
								__UpperCamelCase					:List[str]        =		np.random.rand(100			,			32).astype(np.floataa)
								__UpperCamelCase					:List[Any]        =		np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
												__UpperCamelCase					:Any        =		feature_extractor.pad([{'''input_features''': inputs}]			,			return_tensors='''np''')
												self.assertTrue(np_processed.input_features.dtype == np.floataa)
												__UpperCamelCase					:str        =		feature_extractor.pad([{'''input_features''': inputs}]			,			return_tensors='''pt''')
												self.assertTrue(pt_processed.input_features.dtype == torch.floataa)







				def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
								__UpperCamelCase					:Optional[Any]        =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,			'''clean'''			,			split='''validation''')
								# automatic decoding with librispeech
								__UpperCamelCase					:Optional[Any]        =		ds.sort('''id''').select(range(__lowercase))[:num_samples]['''audio''']

								return [x["array"] for x in speech_samples]







				def       UpperCamelCase__       (     self)	->     Any:
								# fmt: off
								__UpperCamelCase					:str        =		torch.tensor(
								    [
								        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
								        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
								        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
								        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
								    ])
								# fmt: on

								__UpperCamelCase					:Optional[Any]        =		self._load_datasamples(1)
								__UpperCamelCase					:Optional[Any]        =		WhisperFeatureExtractor()
								__UpperCamelCase					:Tuple        =		feature_extractor(__lowercase			,			return_tensors='''pt''').input_features
								self.assertEqual(input_features.shape			,			(1, 80, 3_000))
								self.assertTrue(torch.allclose(input_features[0, 0, :30]			,			__lowercase			,			atol=1E-4))







				def       UpperCamelCase__       (     self)	->     Tuple:
								__UpperCamelCase					:int        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
								__UpperCamelCase					:int        =		self._load_datasamples(1)[0]
								__UpperCamelCase					:List[str]        =		((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
								__UpperCamelCase					:Any        =		feat_extract.zero_mean_unit_var_norm([audio]			,			attention_mask=__lowercase)[0]

								self.assertTrue(np.all(np.mean(__lowercase) < 1E-3))
								self.assertTrue(np.all(np.abs(np.var(__lowercase) - 1) < 1E-3))






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowercase										= '''bert-base-cased'''
__lowercase										= '''google/pegasus-xsum'''
__lowercase										= [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase										= ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase										= '''patrickvonplaten/t5-tiny-random'''
__lowercase										= '''sshleifer/bart-tiny-random'''
__lowercase										= '''sshleifer/tiny-mbart'''
__lowercase										= '''sshleifer/tiny-marian-en-de'''



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''\n'''.join(SCREAMING_SNAKE_CASE     )
    Path(SCREAMING_SNAKE_CASE     ).open('''w'''     ).writelines(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.source"""     )  ,      SCREAMING_SNAKE_CASE     )
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.target"""     )  ,      SCREAMING_SNAKE_CASE     )
    return tmp_dir


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    @slow
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:Dict        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:List[Any]        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Optional[int]        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:int        =		4
        __UpperCamelCase					:Any        =		8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        __UpperCamelCase					:str        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			src_lang=__lowercase			,			tgt_lang=__lowercase			,			)
        __UpperCamelCase					:Any        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(__lowercase			,			__lowercase)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            __UpperCamelCase					:Optional[int]        =		shift_tokens_right(batch['''labels''']			,			tokenizer.pad_token_id)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch







    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:int        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Dict        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:Union[str, Any]        =		4
        __UpperCamelCase					:List[str]        =		LegacySeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=20			,			max_target_length=__lowercase			,			)
        __UpperCamelCase					:Dict        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

        __UpperCamelCase					:Union[str, Any]        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        __UpperCamelCase					:str        =		tmp_dir.joinpath('''train.source''').open().readlines()
        __UpperCamelCase					:int        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(__lowercase			,			__lowercase			,			128			,			__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{x.name for x in tmp_dir.iterdir()}
        __UpperCamelCase					:int        =		{x.name for x in save_dir.iterdir()}
        __UpperCamelCase					:Optional[int]        =		save_dir.joinpath('''train.source''').open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(__lowercase) < len(__lowercase)
        assert len(__lowercase) == 1
        assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
        assert orig_paths == new_paths







    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,			reason='''This test requires fairseq''')
    def       UpperCamelCase__       (     self)	->     List[Any]:
        if not FAIRSEQ_AVAILABLE:
            return
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		self._get_dataset(max_len=64)
        __UpperCamelCase					:Union[str, Any]        =		64
        __UpperCamelCase					:Tuple        =		ds.make_dynamic_sampler(__lowercase			,			required_batch_size_multiple=__lowercase)
        __UpperCamelCase					:List[str]        =		[len(__lowercase) for x in batch_sampler]
        assert len(set(__lowercase)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(__lowercase) == len(__lowercase)  # no dropped or added examples
        __UpperCamelCase					:int        =		DataLoader(__lowercase			,			batch_sampler=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:List[str]        =		[]
        __UpperCamelCase					:int        =		[]
        for batch in data_loader:
            __UpperCamelCase					:List[Any]        =		batch['''input_ids'''].shape
            __UpperCamelCase					:Dict        =		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            __UpperCamelCase					:Optional[int]        =		np.product(batch['''input_ids'''].shape)
            num_src_per_batch.append(__lowercase)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(__lowercase)
        assert num_src_per_batch[0] == max(__lowercase)
        if failures:
            raise AssertionError(f"""too many tokens in {len(__lowercase)} batches""")







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._get_dataset(max_len=512)
        __UpperCamelCase					:Any        =		2
        __UpperCamelCase					:List[Any]        =		ds.make_sortish_sampler(__lowercase			,			shuffle=__lowercase)

        __UpperCamelCase					:List[Any]        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:Tuple        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=__lowercase)

        __UpperCamelCase					:int        =		tokenizer.pad_token_id

        def count_pad_tokens(__lowercase			,			__lowercase="input_ids"):
            return [batch[k].eq(__lowercase).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(__lowercase			,			k='''labels''')) < sum(count_pad_tokens(__lowercase			,			k='''labels'''))
        assert sum(count_pad_tokens(__lowercase)) < sum(count_pad_tokens(__lowercase))
        assert len(__lowercase) == len(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase=1_000			,			__lowercase=128)	->     List[Any]:
        if os.getenv('''USE_REAL_DATA'''			,			__lowercase):
            __UpperCamelCase					:Optional[Any]        =		'''examples/seq2seq/wmt_en_ro'''
            __UpperCamelCase					:Dict        =		max_len * 2 * 64
            if not Path(__lowercase).joinpath('''train.len''').exists():
                save_len_file(__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		'''examples/seq2seq/test_data/wmt_en_ro'''
            __UpperCamelCase					:Optional[int]        =		max_len * 4
            save_len_file(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:List[Any]        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			n_obs=__lowercase			,			)
        return ds, max_tokens, tokenizer







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._get_dataset()
        __UpperCamelCase					:List[str]        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=__lowercase))
        __UpperCamelCase					:Tuple        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=__lowercase))
        assert idsa.intersection(__lowercase) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained(__lowercase			,			use_fast=__lowercase)
        if tok_name == MBART_TINY:
            __UpperCamelCase					:Optional[Any]        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			src_lang='''EN'''			,			tgt_lang='''FR'''			,			)
            __UpperCamelCase					:Tuple        =		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __UpperCamelCase					:Tuple        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			)
            __UpperCamelCase					:Optional[int]        =		train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0





