


"""simple docstring"""

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


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


def lowerCAmelCase_      (				UpperCamelCase__	: Path				,   UpperCamelCase__	: list ):






 """simple docstring"""







 __lowercase									=	"""\n""".join(UpperCamelCase__ )
 Path(UpperCamelCase__ ).open("""w""" ).writelines(UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







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





class    lowerCamelCase__						(		_a    ):
      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Optional[int]							):






       '''simple docstring'''


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






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained(A_							)
       __lowercase									=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in ARTICLES							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in SUMMARIES							)
       __lowercase									=	4
       __lowercase									=	LegacySeqaSeqDataset(
           A_      ,       data_dir=A_      ,       type_path="""train"""      ,       max_source_length=2_0      ,       max_target_length=A_      ,       )
       __lowercase									=	DataLoader(A_      ,       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 2_0 >= 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 SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

       __lowercase									=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
       __lowercase									=	tmp_dir.joinpath("""train.source"""							).open().readlines()
       __lowercase									=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
       pack_data_dir(A_      ,       A_      ,       1_2_8      ,       A_							)
       __lowercase									=	{x.name for x in tmp_dir.iterdir()}
       __lowercase									=	{x.name for x in save_dir.iterdir()}
       __lowercase									=	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(A_							) < len(A_							)
       assert len(A_							) == 1
       assert len(packed_examples[0]							) == sum(len(A_							) for x in orig_examples							)
       assert orig_paths == new_paths
      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,       reason="""This test requires fairseq"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       if not FAIRSEQ_AVAILABLE:
        return
       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset(max_len=6_4							)
       __lowercase									=	6_4
       __lowercase									=	ds.make_dynamic_sampler(A_      ,       required_batch_size_multiple=A_							)
       __lowercase									=	[len(A_							) for x in batch_sampler]
       assert len(set(A_							)							) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(A_							) == len(A_							)  # no dropped or added examples
       __lowercase									=	DataLoader(A_      ,       batch_sampler=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2							)
       __lowercase									=	[]
       __lowercase									=	[]
       for batch in data_loader:
        __lowercase									=	batch["""input_ids"""].shape
        __lowercase									=	src_shape[0]
        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
        __lowercase									=	np.product(batch["""input_ids"""].shape							)
        num_src_per_batch.append(A_							)
        if num_src_tokens > (max_tokens * 1.1):
         failures.append(A_							)
       assert num_src_per_batch[0] == max(A_							)
       if failures:
        raise AssertionError(F'''too many tokens in {len(A_							)} batches'''							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset(max_len=5_1_2							)
       __lowercase									=	2
       __lowercase									=	ds.make_sortish_sampler(A_      ,       shuffle=A_							)

       __lowercase									=	DataLoader(A_      ,       batch_size=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2							)
       __lowercase									=	DataLoader(A_      ,       batch_size=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2      ,       sampler=A_							)

       __lowercase									=	tokenizer.pad_token_id

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

       assert sum(count_pad_tokens(A_      ,       k="""labels"""							)							) < sum(count_pad_tokens(A_      ,       k="""labels"""							)							)
       assert sum(count_pad_tokens(A_							)							) < sum(count_pad_tokens(A_							)							)
       assert len(A_							) == len(A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	Union[str, Any]=1_0_0_0      ,       A_	:	Optional[Any]=1_2_8							):






       '''simple docstring'''


       if os.getenv("""USE_REAL_DATA"""      ,       A_							):
        __lowercase									=	"""examples/seq2seq/wmt_en_ro"""
        __lowercase									=	max_len * 2 * 6_4
        if not Path(A_							).joinpath("""train.len"""							).exists():
         save_len_file(A_      ,       A_							)
       else:
        __lowercase									=	"""examples/seq2seq/test_data/wmt_en_ro"""
        __lowercase									=	max_len * 4
        save_len_file(A_      ,       A_							)

       __lowercase									=	AutoTokenizer.from_pretrained(A_							)
       __lowercase									=	SeqaSeqDataset(
           A_      ,       data_dir=A_      ,       type_path="""train"""      ,       max_source_length=A_      ,       max_target_length=A_      ,       n_obs=A_      ,       )
       return ds, max_tokens, tokenizer
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset()
       __lowercase									=	set(DistributedSortishSampler(A_      ,       2_5_6      ,       num_replicas=2      ,       rank=0      ,       add_extra_examples=A_							)							)
       __lowercase									=	set(DistributedSortishSampler(A_      ,       2_5_6      ,       num_replicas=2      ,       rank=1      ,       add_extra_examples=A_							)							)
       assert idsa.intersection(A_							) == set()






      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained(A_      ,       use_fast=A_							)
       if tok_name == MBART_TINY:
        __lowercase									=	SeqaSeqDataset(
            A_      ,       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"""      ,       )
        __lowercase									=	train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        __lowercase									=	SeqaSeqDataset(
            A_      ,       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      ,       )
        __lowercase									=	train_dataset.dataset_kwargs
        assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
        assert len(A_							) == 1 if tok_name == BART_TINY else len(A_							) == 0




"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       UpperCAmelCase__  							=get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__  							=12_8022
UpperCAmelCase__  							=12_8028





@require_sentencepiece
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Dict											=		MaMaaaTokenizer
      a :     Optional[int]											=		False
      a :     str											=		False
      a :     Any											=		True
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       super().setUp()

       __lowercase									=	["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase									=	dict(zip(A_      ,       range(len(A_							)							)							)							)
       __lowercase									=	Path(self.tmpdirname							)
       save_json(A_      ,       save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
        copyfile(A_      ,       save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

       __lowercase									=	MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       **A_	:	List[Any]							):






       '''simple docstring'''


       return MaMaaaTokenizer.from_pretrained(self.tmpdirname      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	"""</s>"""
       __lowercase									=	0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_							)      ,       A_							)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_							)      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	self.get_tokenizer()
       __lowercase									=	list(tokenizer.get_vocab().keys()							)

       self.assertEqual(vocab_keys[0]      ,       """</s>"""							)
       self.assertEqual(vocab_keys[1]      ,       """<unk>"""							)
       self.assertEqual(vocab_keys[-1]      ,       """<s>"""							)
       self.assertEqual(len(A_							)      ,       tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)
      @unittest.skip("""Skip this test while all models are still to be uploaded."""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.get_tokenizer()

       __lowercase									=	tokenizer.tokenize("""This is a test"""							)
       self.assertListEqual(A_      ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_							)      ,       [2, 3, 4, 5, 6]      ,       )

       __lowercase									=	tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
       self.assertListEqual(A_      ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

       __lowercase									=	tokenizer.convert_tokens_to_string(A_							)
       self.assertEqual(A_      ,       """This is a test"""							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	{"""input_ids""": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_      ,       model_name="""facebook/m2m100_418M"""      ,       revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""      ,       )






@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__						(		unittest.TestCase    ):
      a :     Optional[int]											=		"""facebook/m2m100_418M"""
      a :     str											=		[
          """In my opinion, there are two levels of response from the French government.""",
          """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
      ]
      a :     int											=		[
          """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
          """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
      ]

      # fmt: off
      a :     Optional[int]											=		[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	str							):






       '''simple docstring'''


       __lowercase									=	MaMaaaTokenizer.from_pretrained(
           cls.checkpoint_name      ,       src_lang="""en"""      ,       tgt_lang="""fr"""							)
       __lowercase									=	1
       return cls
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)      ,       1_2_8_0_0_6							)
       self.assertEqual(self.tokenizer.get_lang_id("""en"""							)      ,       1_2_8_0_2_2							)
       self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)      ,       1_2_8_0_7_6							)
       self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)      ,       1_2_8_0_6_3							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer.get_vocab()
       self.assertEqual(len(A_							)      ,       self.tokenizer.vocab_size							)
       self.assertEqual(vocab["""<unk>"""]      ,       3							)
       self.assertIn(self.tokenizer.get_lang_token("""en"""							)      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	"""en"""
       __lowercase									=	self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
       self.assertListEqual(self.expected_src_tokens      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       self.assertIn(A_      ,       self.tokenizer.all_special_ids							)
       # fmt: off
       __lowercase									=	[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
       # fmt: on
       __lowercase									=	self.tokenizer.decode(A_      ,       skip_special_tokens=A_							)
       __lowercase									=	self.tokenizer.decode(generated_ids[1:]      ,       skip_special_tokens=A_							)
       self.assertEqual(A_      ,       A_							)
       self.assertNotIn(self.tokenizer.eos_token      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	tempfile.mkdtemp()
       __lowercase									=	self.tokenizer.lang_token_to_id
       self.tokenizer.save_pretrained(A_							)
       __lowercase									=	MaMaaaTokenizer.from_pretrained(A_							)
       self.assertDictEqual(new_tok.lang_token_to_id      ,       A_							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	"""en"""
       __lowercase									=	"""fr"""

       __lowercase									=	self.tokenizer(self.src_text      ,       text_target=self.tgt_text      ,       padding=A_      ,       return_tensors="""pt"""							)

       __lowercase									=	shift_tokens_right(
           batch["""labels"""]      ,       self.tokenizer.pad_token_id      ,       self.tokenizer.eos_token_id							)

       for k in batch:
        __lowercase									=	batch[k].tolist()
       # batch = {k: v.tolist() for k,v in batch.items()}
       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       # batch.decoder_inputs_ids[0][0] ==
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == FR_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	"""mr"""
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""mr"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)

       __lowercase									=	"""zh"""
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""zh"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	"""mr"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""mr"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

       __lowercase									=	"""zh"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""zh"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)






      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer._build_translation_inputs("""A test"""      ,       return_tensors="""pt"""      ,       src_lang="""en"""      ,       tgt_lang="""ar"""							)

       self.assertEqual(
           nested_simplify(A_							)      ,       {
               # en_XX, A, test, EOS
               """input_ids""": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 1_2_8_0_0_6,
           }      ,       )
