





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=True	,			__lowerCAmelCase="pt" ):
				_snake_case :			Union[str, Any]  						=   {'add_prefix_space': True} if isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) and not line.startswith(' ' ) else {}
				_snake_case :			str  						=   padding_side
				return tokenizer(
				    [line]	,			max_length=__lowerCAmelCase	,			padding='max_length' if pad_to_max_length else None	,			truncation=__lowerCAmelCase	,			return_tensors=__lowerCAmelCase	,			add_special_tokens=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			):
				_snake_case :			List[Any]  						=   input_ids.ne(__lowerCAmelCase ).any(dim=0 )
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		List[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Optional[Any]="train" ,				lowerCamelCase_			:		str=None ,				lowerCamelCase_			:		Optional[Any]=None ,				lowerCamelCase_			:		Dict=None ,				lowerCamelCase_			:		Optional[Any]="" ,				):





										'''simple docstring'''







										super().__init__()
										_snake_case :			int  						=   Path(lowerCamelCase_					).joinpath(type_path + '.source'					)
										_snake_case :			Optional[Any]  						=   Path(lowerCamelCase_					).joinpath(type_path + '.target'					)
										_snake_case :			str  						=   self.get_char_lens(self.src_file					)
										_snake_case :			List[str]  						=   max_source_length
										_snake_case :			List[str]  						=   max_target_length
										assert min(self.src_lens					) > 0, f'''found empty line in {self.src_file}'''
										_snake_case :			int  						=   tokenizer
										_snake_case :			Optional[Any]  						=   prefix
										if n_obs is not None:
														_snake_case :			Union[str, Any]  						=   self.src_lens[:n_obs]
										_snake_case :			Any  						=   src_lang
										_snake_case :			Optional[int]  						=   tgt_lang







						def __len__(    self			:		Dict					):





										'''simple docstring'''







										return len(self.src_lens					)







						def __getitem__(    self			:		Optional[int] ,				lowerCamelCase_			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   index + 1  # linecache starts at 1
										_snake_case :			List[Any]  						=   self.prefix + linecache.getline(str(self.src_file					) ,				lowerCamelCase_					).rstrip('\n'					)
										_snake_case :			int  						=   linecache.getline(str(self.tgt_file					) ,				lowerCamelCase_					).rstrip('\n'					)
										assert source_line, f'''empty source line for index {index}'''
										assert tgt_line, f'''empty tgt line for index {index}'''

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer ,				lowerCamelCase_					):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										_snake_case :			List[Any]  						=   (
										    self.tokenizer.question_encoder if isinstance(self.tokenizer ,				lowerCamelCase_					) else self.tokenizer
										)
										_snake_case :			Tuple  						=   self.tokenizer.generator if isinstance(self.tokenizer ,				lowerCamelCase_					) else self.tokenizer

										_snake_case :			List[str]  						=   encode_line(lowerCamelCase_ ,				lowerCamelCase_ ,				self.max_source_length ,				'right'					)
										_snake_case :			Any  						=   encode_line(lowerCamelCase_ ,				lowerCamelCase_ ,				self.max_target_length ,				'right'					)

										_snake_case :			Optional[int]  						=   source_inputs['input_ids'].squeeze()
										_snake_case :			Optional[Any]  						=   target_inputs['input_ids'].squeeze()
										_snake_case :			Any  						=   source_inputs['attention_mask'].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}







						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										return [len(lowerCamelCase_					) for x in Path(lowerCamelCase_					).open().readlines()]







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   torch.stack([x['input_ids'] for x in batch]					)
										_snake_case :			Union[str, Any]  						=   torch.stack([x['attention_mask'] for x in batch]					)
										_snake_case :			Dict  						=   torch.stack([x['decoder_input_ids'] for x in batch]					)
										_snake_case :			List[str]  						=   (
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer ,				lowerCamelCase_					)
										    else self.tokenizer.pad_token_id
										)
										_snake_case :			Optional[Any]  						=   (
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer ,				lowerCamelCase_					)
										    else self.tokenizer.pad_token_id
										)
										_snake_case :			Tuple  						=   trim_batch(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case						,			_snake_case :			Any  						=   trim_batch(lowerCamelCase_ ,				lowerCamelCase_ ,				attention_mask=lowerCamelCase_					)
										_snake_case :			Any  						=   {
										    'input_ids': source_ids,
										    'attention_mask': source_mask,
										    'decoder_input_ids': y,
										}
										return batch



lowercase_		:				Optional[int]				=						getLogger(__name__)

def       A__(		__lowerCAmelCase ):
				return list(itertools.chain.from_iterable(__lowerCAmelCase ) )

def       A__(		__lowerCAmelCase ):
				_snake_case :			Tuple  						=   get_git_info()
				save_json(__lowerCAmelCase	,			os.path.join(__lowerCAmelCase	,			'git_log.json' ) )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=4	,			**__lowerCAmelCase ):
				with open(__lowerCAmelCase	,			'w' ) as f:
								json.dump(__lowerCAmelCase	,			__lowerCAmelCase	,			indent=__lowerCAmelCase	,			**__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				with open(__lowerCAmelCase ) as f:
								return json.load(__lowerCAmelCase )

def       A__(		):
				_snake_case :			Any  						=   git.Repo(search_parent_directories=__lowerCAmelCase )
				_snake_case :			Tuple  						=   {
				    'repo_id': str(__lowerCAmelCase ),
				    'repo_sha': str(repo.head.object.hexsha ),
				    'repo_branch': str(repo.active_branch ),
				    'hostname': str(socket.gethostname() ),
				}
				return repo_infos

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				return list(map(__lowerCAmelCase	,			__lowerCAmelCase ) )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				with open(__lowerCAmelCase	,			'wb' ) as f:
								return pickle.dump(__lowerCAmelCase	,			__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):

				def remove_articles(__lowerCAmelCase ):
								return re.sub(R'\b(a|an|the)\b'	,			' '	,			__lowerCAmelCase )

				def white_space_fix(__lowerCAmelCase ):
								return " ".join(text.split() )

				def remove_punc(__lowerCAmelCase ):
								_snake_case :			Union[str, Any]  						=   set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(__lowerCAmelCase ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase ) ) ) )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   normalize_answer(__lowerCAmelCase ).split()
				_snake_case :			int  						=   normalize_answer(__lowerCAmelCase ).split()
				_snake_case :			Optional[Any]  						=   Counter(__lowerCAmelCase ) & Counter(__lowerCAmelCase )
				_snake_case :			List[Any]  						=   sum(common.values() )
				if num_same == 0:
								return 0
				_snake_case :			str  						=   1.0 * num_same / len(__lowerCAmelCase )
				_snake_case :			Optional[int]  						=   1.0 * num_same / len(__lowerCAmelCase )
				_snake_case :			Any  						=   (2 * precision * recall) / (precision + recall)
				return fa

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				return normalize_answer(__lowerCAmelCase ) == normalize_answer(__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
				_snake_case :			Dict  						=   0
				for hypo, pred in zip(__lowerCAmelCase	,			__lowerCAmelCase ):
								em += exact_match_score(__lowerCAmelCase	,			__lowerCAmelCase )
				if len(__lowerCAmelCase ) > 0:
								em /= len(__lowerCAmelCase )
				return {"em": em}

def       A__(		__lowerCAmelCase ):
				return model_prefix.startswith('rag' )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				_snake_case :			int  						=   'dropout_rate'
				for p in extra_params:
								if getattr(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
												if not hasattr(__lowerCAmelCase	,			__lowerCAmelCase ) and not hasattr(__lowerCAmelCase	,			equivalent_param[p] ):
																logger.info('config doesn\'t have a `{}` attribute'.format(__lowerCAmelCase ) )
																delattr(__lowerCAmelCase	,			__lowerCAmelCase )
																continue
												_snake_case :			Optional[Any]  						=   p if hasattr(__lowerCAmelCase	,			__lowerCAmelCase ) else equivalent_param[p]
												setattr(__lowerCAmelCase	,			__lowerCAmelCase	,			getattr(__lowerCAmelCase	,			__lowerCAmelCase ) )
												delattr(__lowerCAmelCase	,			__lowerCAmelCase )
				return hparams, config













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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			str  						=   '\n'.join(__lowerCAmelCase )
				Path(__lowerCAmelCase ).open('w' ).writelines(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.source''' )	,			__lowerCAmelCase )
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.target''' )	,			__lowerCAmelCase )
				return tmp_dir


class 			lowercase				(							a_     ):



						"""simple docstring"""

						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			Dict  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Optional[int]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Any  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Dict  						=   4
										_snake_case :			Any  						=   8
										assert max_len_target > max_src_len  # Will be truncated
										assert max_len_source > max_src_len  # Will be truncated
										_snake_case						,			_snake_case :			Optional[Any]  						=   'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
										_snake_case :			int  						=   SeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=lowerCamelCase_ ,				max_target_length=lowerCamelCase_ ,				src_lang=lowerCamelCase_ ,				tgt_lang=lowerCamelCase_ ,				)
										_snake_case :			List[str]  						=   DataLoader(lowerCamelCase_ ,				batch_size=2 ,				collate_fn=train_dataset.collate_fn					)
										for batch in dataloader:
														assert isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
														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
														_snake_case :			List[Any]  						=   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			:		Any ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Dict  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Union[str, Any]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Union[str, Any]  						=   4
										_snake_case :			Optional[int]  						=   LegacySeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=20 ,				max_target_length=lowerCamelCase_ ,				)
										_snake_case :			Dict  						=   DataLoader(lowerCamelCase_ ,				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					):





										'''simple docstring'''







										_snake_case :			int  						=   AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

										_snake_case :			List[str]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										_snake_case :			Any  						=   tmp_dir.joinpath('train.source'					).open().readlines()
										_snake_case :			Optional[Any]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										pack_data_dir(lowerCamelCase_ ,				lowerCamelCase_ ,				1_28 ,				lowerCamelCase_					)
										_snake_case :			Tuple  						=   {x.name for x in tmp_dir.iterdir()}
										_snake_case :			Dict  						=   {x.name for x in save_dir.iterdir()}
										_snake_case :			str  						=   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(lowerCamelCase_					) < len(lowerCamelCase_					)
										assert len(lowerCamelCase_					) == 1
										assert len(packed_examples[0]					) == sum(len(lowerCamelCase_					) 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[str]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			str  						=   self._get_dataset(max_len=5_12					)
										_snake_case :			Optional[Any]  						=   2
										_snake_case :			Dict  						=   ds.make_sortish_sampler(lowerCamelCase_ ,				shuffle=lowerCamelCase_					)

										_snake_case :			int  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2					)
										_snake_case :			str  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2 ,				sampler=lowerCamelCase_					)

										_snake_case :			Tuple  						=   tokenizer.pad_token_id

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

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







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		Tuple=10_00 ,				lowerCamelCase_			:		Tuple=1_28					):





										'''simple docstring'''







										if os.getenv('USE_REAL_DATA' ,				lowerCamelCase_					):
														_snake_case :			Dict  						=   'examples/seq2seq/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 2 * 64
														if not Path(lowerCamelCase_					).joinpath('train.len'					).exists():
																		save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)
										else:
														_snake_case :			Union[str, Any]  						=   'examples/seq2seq/test_data/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 4
														save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)

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







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





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			Any  						=   self._get_dataset()
										_snake_case :			List[str]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=0 ,				add_extra_examples=lowerCamelCase_					)					)
										_snake_case :			Optional[Any]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=1 ,				add_extra_examples=lowerCamelCase_					)					)
										assert idsa.intersection(lowerCamelCase_					) == set()







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_ ,				use_fast=lowerCamelCase_					)
										if tok_name == MBART_TINY:
														_snake_case :			int  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				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' ,				)
														_snake_case :			Optional[Any]  						=   train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
														_snake_case :			Tuple  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				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 ,				)
														_snake_case :			List[Any]  						=   train_dataset.dataset_kwargs
														assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
														assert len(lowerCamelCase_					) == 1 if tok_name == BART_TINY else len(lowerCamelCase_					) == 0






