






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     "\n".join(_UpperCamelCase							)
   Path(_UpperCamelCase							).open("w"							).writelines(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """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        _UpperCAmelCase						(		snake_case			):




    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]  ,     )
    @slow
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    str           =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       lowercase_	:    Dict           =     max(len(tokenizer.encode(a			)			) for a in ARTICLES			)
       lowercase_	:    Any           =     max(len(tokenizer.encode(a			)			) for a in SUMMARIES			)
       lowercase_	:    Optional[Any]           =     4
       lowercase_	:    int           =     8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       lowercase_	:    int           =     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
       lowercase_	:    Union[str, Any]           =     SeqaSeqDataset(
           a  ,     data_dir=a  ,     type_path="train"  ,     max_source_length=a  ,     max_target_length=a  ,     src_lang=a  ,     tgt_lang=a  ,     )
       lowercase_	:    Optional[Any]           =     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_	:    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 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    Optional[Any]           =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       lowercase_	:    Any           =     max(len(tokenizer.encode(a			)			) for a in ARTICLES			)
       lowercase_	:    Optional[Any]           =     max(len(tokenizer.encode(a			)			) for a in SUMMARIES			)
       lowercase_	:    Any           =     4
       lowercase_	:    Tuple           =     LegacySeqaSeqDataset(
           a  ,     data_dir=a  ,     type_path="train"  ,     max_source_length=2_0  ,     max_target_length=a  ,     )
       lowercase_	:    str           =     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 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Dict           =     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"			)

       lowercase_	:    int           =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       lowercase_	:    Optional[Any]           =     tmp_dir.joinpath("train.source"			).open().readlines()
       lowercase_	:    str           =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       pack_data_dir(a  ,     a  ,     1_2_8  ,     a			)
       lowercase_	:    str           =     {x.name for x in tmp_dir.iterdir()}
       lowercase_	:    Optional[int]           =     {x.name for x in save_dir.iterdir()}
       lowercase_	:    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(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 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       if not FAIRSEQ_AVAILABLE:
          return
       lowercase_	:    List[str]           =     self._get_dataset(max_len=6_4			)
       lowercase_	:    Union[str, Any]           =     6_4
       lowercase_	:    str           =     ds.make_dynamic_sampler(a  ,     required_batch_size_multiple=a			)
       lowercase_	:    Any           =     [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_	:    Dict           =     DataLoader(a  ,     batch_sampler=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2			)
       lowercase_	:    Any           =     []
       lowercase_	:    Tuple           =     []
       for batch in data_loader:
          lowercase_	:    List[str]           =     batch["input_ids"].shape
          lowercase_	:    int           =     src_shape[0]
          assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
          lowercase_	:    Optional[Any]           =     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 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    str           =     self._get_dataset(max_len=5_1_2			)
       lowercase_	:    Optional[Any]           =     2
       lowercase_	:    str           =     ds.make_sortish_sampler(a  ,     shuffle=a			)

       lowercase_	:    Any           =     DataLoader(a  ,     batch_size=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2			)
       lowercase_	:    Dict           =     DataLoader(a  ,     batch_size=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2  ,     sampler=a			)

       lowercase_	:    int           =     tokenizer.pad_token_id

       def count_pad_tokens(a  :       Dict  ,     a  :       Tuple="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 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Optional[int]=1_0_0_0  ,     a  :       Dict=1_2_8			):






       '''simple docstring'''






       if os.getenv("USE_REAL_DATA"  ,     a			):
          lowercase_	:    Optional[int]           =     "examples/seq2seq/wmt_en_ro"
          lowercase_	:    List[str]           =     max_len * 2 * 6_4
          if not Path(a			).joinpath("train.len"			).exists():
             save_len_file(a  ,     a			)
       else:
          lowercase_	:    str           =     "examples/seq2seq/test_data/wmt_en_ro"
          lowercase_	:    Optional[Any]           =     max_len * 4
          save_len_file(a  ,     a			)

       lowercase_	:    List[Any]           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    Optional[int]           =     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 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     self._get_dataset()
       lowercase_	:    int           =     set(DistributedSortishSampler(a  ,     2_5_6  ,     num_replicas=2  ,     rank=0  ,     add_extra_examples=a			)			)
       lowercase_	:    List[Any]           =     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 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     AutoTokenizer.from_pretrained(a  ,     use_fast=a			)
       if tok_name == MBART_TINY:
          lowercase_	:    List[Any]           =     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_	:    Tuple           =     train_dataset.dataset_kwargs
          assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
          lowercase_	:    List[str]           =     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_	:    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(a			) == 1 if tok_name == BART_TINY else len(a			) == 0












'''simple docstring'''
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ =			get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
 from transformers.models.plbart.modeling_plbart import shift_tokens_right

UpperCamelCase__ =			50003
UpperCamelCase__ =			50002


@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Optional[int]		       =    PLBartTokenizer
    __lowerCamelCase:   Any		       =    None
    __lowerCamelCase:   Dict		       =    False




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       super().setUp()

       # We have a SentencePiece fixture for testing
       lowercase_	:    Any           =     PLBartTokenizer(a  ,     language_codes="base"  ,     keep_accents=a			)
       tokenizer.save_pretrained(self.tmpdirname			)




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     PLBartTokenizer(a  ,     language_codes="base"  ,     keep_accents=a			)

       lowercase_	:    List[str]           =     tokenizer.tokenize("This is a test"			)
       self.assertListEqual(a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]			)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a			)  ,     [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,     )

       lowercase_	:    List[str]           =     tokenizer.tokenize("I was born in 92000, and this is falsé."			)
       self.assertListEqual(
           a  ,     [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]  ,     )
       lowercase_	:    Union[str, Any]           =     tokenizer.convert_tokens_to_ids(a			)
       self.assertListEqual(
           a  ,     [
               value + tokenizer.fairseq_offset
               for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
           ]  ,     )

       lowercase_	:    Optional[int]           =     tokenizer.convert_ids_to_tokens(a			)
       self.assertListEqual(
           a  ,     [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]  ,     )

       lowercase_	:    Dict           =     tokenizer.vocab_size
       lowercase_	:    str           =     [tokenizer.convert_ids_to_tokens(a			) for x in range(end - 4  ,     a			)]

       self.assertListEqual(a  ,     ["__java__", "__python__", "__en_XX__", "<mask>"]			)

       lowercase_	:    int           =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
       lowercase_	:    str           =     tokenizer(a			).input_ids
       self.assertEqual(
           tokenizer.decode(a  ,     skip_special_tokens=a  ,     clean_up_tokenization_spaces=a			)  ,     a  ,     )





    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    str           =     PLBartTokenizer(a  ,     language_codes="multi"  ,     keep_accents=a			)

       lowercase_	:    List[str]           =     tokenizer.tokenize("This is a test"			)
       self.assertListEqual(a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]			)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a			)  ,     [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,     )

       lowercase_	:    Optional[Any]           =     tokenizer.tokenize("I was born in 92000, and this is falsé."			)
       self.assertListEqual(
           a  ,     [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]  ,     )
       lowercase_	:    Optional[Any]           =     tokenizer.convert_tokens_to_ids(a			)
       self.assertListEqual(
           a  ,     [
               value + tokenizer.fairseq_offset
               for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
           ]  ,     )

       lowercase_	:    List[str]           =     tokenizer.convert_ids_to_tokens(a			)
       self.assertListEqual(
           a  ,     [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]  ,     )
       lowercase_	:    Dict           =     tokenizer.vocab_size
       lowercase_	:    Union[str, Any]           =     [tokenizer.convert_ids_to_tokens(a			) for x in range(end - 7  ,     a			)]

       self.assertListEqual(
           a  ,     ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]			)
       lowercase_	:    Any           =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
       lowercase_	:    List[Any]           =     tokenizer(a			).input_ids
       self.assertEqual(
           tokenizer.decode(a  ,     skip_special_tokens=a  ,     clean_up_tokenization_spaces=a			)  ,     a  ,     )


@require_torch
@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase						(		unittest.TestCase			):
    __lowerCamelCase:   int		       =    'uclanlp/plbart-python-en_XX'
    __lowerCamelCase:   Tuple		       =    [
        'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
        'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
    ]
    __lowerCamelCase:   List[str]		       =    [
        'Returns the maximum value of a b c.',
        'Sums the values of a b c.',
    ]
    __lowerCamelCase:   List[str]		       =    [
        134,
        5452,
        3_3460,
        3_3441,
        3_3463,
        3_3465,
        3_3463,
        3_3449,
        988,
        20,
        3_3456,
        19,
        3_3456,
        771,
        39,
        4258,
        889,
        3318,
        3_3441,
        3_3463,
        3_3465,
        3_3463,
        3_3449,
        2471,
        2,
        PYTHON_CODE,
    ]




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       str			):






       '''simple docstring'''






       lowercase_	:    PLBartTokenizer           =     PLBartTokenizer.from_pretrained(
           cls.checkpoint_name  ,     language_codes="base"  ,     src_lang="python"  ,     tgt_lang="en_XX"			)
       lowercase_	:    List[str]           =     1
       return cls




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]  ,     5_0_0_0_1			)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]  ,     5_0_0_0_2			)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]  ,     5_0_0_0_3			)




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    int           =     self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
       self.assertListEqual(self.expected_src_tokens  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       self.assertIn(a  ,     self.tokenizer.all_special_ids			)
       lowercase_	:    List[Any]           =     [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
       lowercase_	:    Optional[int]           =     self.tokenizer.decode(a  ,     skip_special_tokens=a			)
       lowercase_	:    Any           =     self.tokenizer.decode(generated_ids[1:]  ,     skip_special_tokens=a			)
       self.assertEqual(a  ,     a			)
       self.assertNotIn(self.tokenizer.eos_token  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 2_0]
       self.assertIsInstance(src_text[0]  ,     a			)
       lowercase_	:    Tuple           =     1_0
       lowercase_	:    int           =     self.tokenizer(a  ,     max_length=a  ,     truncation=a			).input_ids[0]
       self.assertEqual(ids[-2]  ,     2			)
       self.assertEqual(ids[-1]  ,     a			)
       self.assertEqual(len(a			)  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]			)  ,     [5_0_0_0_4, 5_0_0_0_1]			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     tempfile.mkdtemp()
       lowercase_	:    List[str]           =     self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(a			)
       lowercase_	:    Tuple           =     PLBartTokenizer.from_pretrained(a			)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,     a			)




    @require_torch
    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     self.tokenizer(self.src_text  ,     text_target=self.tgt_text  ,     padding=a  ,     return_tensors="pt"			)
       lowercase_	:    List[str]           =     shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id			)

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       self.assertEqual(batch.input_ids[1][-2:].tolist()  ,     [2, PYTHON_CODE]			)
       self.assertEqual(batch.decoder_input_ids[1][0]  ,     a			)
       self.assertEqual(batch.decoder_input_ids[1][-1]  ,     2			)
       self.assertEqual(batch.labels[1][-2:].tolist()  ,     [2, EN_CODE]			)




    @require_torch
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     self.tokenizer(
           self.src_text  ,     text_target=self.tgt_text  ,     padding=a  ,     truncation=a  ,     max_length=len(self.expected_src_tokens			)  ,     return_tensors="pt"  ,     )
       lowercase_	:    Dict           =     shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id			)

       self.assertIsInstance(a  ,     a			)

       self.assertEqual((2, 2_6)  ,     batch.input_ids.shape			)
       self.assertEqual((2, 2_6)  ,     batch.attention_mask.shape			)
       lowercase_	:    Dict           =     batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens  ,     a			)
       self.assertEqual(2  ,     batch.decoder_input_ids[0, -1]			)  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens  ,     []			)
       self.assertEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id, PYTHON_CODE]			)




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.tokenizer(self.src_text  ,     padding=a  ,     truncation=a  ,     max_length=3  ,     return_tensors="pt"			)
       lowercase_	:    List[str]           =     self.tokenizer(
           text_target=self.tgt_text  ,     padding=a  ,     truncation=a  ,     max_length=1_0  ,     return_tensors="pt"			)
       lowercase_	:    Dict           =     targets["input_ids"]
       lowercase_	:    str           =     shift_tokens_right(a  ,     self.tokenizer.pad_token_id			)

       self.assertEqual(batch.input_ids.shape[1]  ,     3			)
       self.assertEqual(batch.decoder_input_ids.shape[1]  ,     1_0			)





    @require_torch
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.tokenizer._build_translation_inputs(
           "A test"  ,     return_tensors="pt"  ,     src_lang="en_XX"  ,     tgt_lang="java"			)

       self.assertEqual(
           nested_simplify(a			)  ,     {
               # A, test, EOS, en_XX
               "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
               "attention_mask": [[1, 1, 1, 1]],
               # java
               "forced_bos_token_id": 5_0_0_0_1,
           }  ,     )




