"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


_a				:		Union[str, Any]	   =       get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

_a				:		Optional[int]	   =       250_004
_a				:		int	   =       250_020
@require_sentencepiece
@require_tokenizers
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[Any]		 =		MBartTokenizer
     _UpperCamelCase							:      Union[str, Any]		 =		MBartTokenizerFast
     _UpperCamelCase							:      Optional[Any]		 =		True
     _UpperCamelCase							:      Dict		 =		True






     def    __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:	Optional[int]        =					MBartTokenizer(a__    ,					keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					MBartTokenizer(a__    ,					keep_accents=a__  )

       _lowerCAmelCase						:	Dict        =					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 [285, 46, 10, 170, 382]]    ,					)

       _lowerCAmelCase						:	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""",
               """.""",
           ]    ,					)
       _lowerCAmelCase						:	str        =					tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__    ,					[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]    ,					)

       _lowerCAmelCase						:	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>""",
               """.""",
           ]    ,					)






     def    __A  (		self  ):
       if not self.test_slow_tokenizer:
         # as we don't have a slow version, we can't compare the outputs between slow and fast versions
         return

       _lowerCAmelCase						:	Union[str, Any]        =					(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
           _lowerCAmelCase						:	Any        =					self.rust_tokenizer_class.from_pretrained(a__    ,					**a__  )
           _lowerCAmelCase						:	Any        =					self.tokenizer_class.from_pretrained(a__    ,					**a__  )

           _lowerCAmelCase						:	Any        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	str        =					tokenizer_r.save_pretrained(a__  )
           _lowerCAmelCase						:	int        =					tokenizer_p.save_pretrained(a__  )

           # Checks it save with the same files + the tokenizer.json file for the fast one
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )
           _lowerCAmelCase						:	Tuple        =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f  )
           self.assertSequenceEqual(a__    ,					a__  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	str        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	List[str]        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )
             # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
             # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

           shutil.rmtree(a__  )

           # Save tokenizer rust, legacy_format=True
           _lowerCAmelCase						:	Any        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	Tuple        =					tokenizer_r.save_pretrained(a__    ,					legacy_format=a__  )
           _lowerCAmelCase						:	Tuple        =					tokenizer_p.save_pretrained(a__  )

           # Checks it save with the same files
           self.assertSequenceEqual(a__    ,					a__  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	List[Any]        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	Union[str, Any]        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )

           shutil.rmtree(a__  )

           # Save tokenizer rust, legacy_format=False
           _lowerCAmelCase						:	Any        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	List[Any]        =					tokenizer_r.save_pretrained(a__    ,					legacy_format=a__  )
           _lowerCAmelCase						:	Dict        =					tokenizer_p.save_pretrained(a__  )

           # Checks it saved the tokenizer.json file
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	Tuple        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	str        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )

           shutil.rmtree(a__  )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__A							(			unittest.TestCase		):
     _UpperCamelCase							:      Tuple		 =		"facebook/mbart-large-en-ro"
     _UpperCamelCase							:      List[str]		 =		[
         " UN Chief Says There Is No Military Solution in Syria",
         " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
     ]
     _UpperCamelCase							:      Optional[Any]		 =		[
         "Şeful ONU declară că nu există o soluţie militară în Siria",
         "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
         " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
         " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
     ]
     _UpperCamelCase							:      str		 =		[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]






     @classmethod
     def    __A  (		cls  ):
       _lowerCAmelCase						:	MBartTokenizer        =					MBartTokenizer.from_pretrained(
           cls.checkpoint_name    ,					src_lang="""en_XX"""    ,					tgt_lang="""ro_RO"""  )
       _lowerCAmelCase						:	int        =					1
       return cls






     def    __A  (		self  ):
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]    ,					250001  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]    ,					250004  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]    ,					250020  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens    ,					a__  )






     def    __A  (		self  ):
       self.assertIn(a__    ,					self.tokenizer.all_special_ids  )
       _lowerCAmelCase						:	Any        =					[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
       _lowerCAmelCase						:	Dict        =					self.tokenizer.decode(a__    ,					skip_special_tokens=a__  )
       _lowerCAmelCase						:	Optional[Any]        =					self.tokenizer.decode(generated_ids[1:]    ,					skip_special_tokens=a__  )
       self.assertEqual(a__    ,					a__  )
       self.assertNotIn(self.tokenizer.eos_token    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					["""this is gunna be a long sentence """ * 20]
       assert isinstance(src_text[0]    ,					a__  )
       _lowerCAmelCase						:	Optional[int]        =					10
       _lowerCAmelCase						:	List[Any]        =					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    __A  (		self  ):
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]  )    ,					[250026, 250001]  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					tempfile.mkdtemp()
       _lowerCAmelCase						:	Dict        =					self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(a__  )
       _lowerCAmelCase						:	int        =					MBartTokenizer.from_pretrained(a__  )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,					a__  )






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.tokenizer(self.src_text    ,					text_target=self.tgt_text    ,					padding=a__    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					shift_tokens_right(batch["""labels"""]    ,					self.tokenizer.pad_token_id  )

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
       assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
       assert batch.decoder_input_ids[1][-1] == 2
       assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.tokenizer(
           self.src_text    ,					text_target=self.tgt_text    ,					padding=a__    ,					truncation=a__    ,					max_length=len(self.expected_src_tokens  )    ,					return_tensors="""pt"""    ,					)

       _lowerCAmelCase						:	Union[str, Any]        =					shift_tokens_right(batch["""labels"""]    ,					self.tokenizer.pad_token_id  )

       self.assertIsInstance(a__    ,					a__  )

       self.assertEqual((2, 14)    ,					batch.input_ids.shape  )
       self.assertEqual((2, 14)    ,					batch.attention_mask.shape  )
       _lowerCAmelCase						:	Union[str, Any]        =					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, EN_CODE]  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.tokenizer(self.src_text    ,					padding=a__    ,					truncation=a__    ,					max_length=3    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	Optional[int]        =					self.tokenizer(
           text_target=self.tgt_text    ,					padding=a__    ,					truncation=a__    ,					max_length=10    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	Any        =					targets["""input_ids"""]
       _lowerCAmelCase						:	Any        =					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]    ,					10  )






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.tokenizer._build_translation_inputs(
           """A test"""    ,					return_tensors="""pt"""    ,					src_lang="""en_XX"""    ,					tgt_lang="""ar_AR"""  )

       self.assertEqual(
           nested_simplify(a__  )    ,					{
               # A, test, EOS, en_XX
               """input_ids""": [[62, 3034, 2, 250004]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 250001,
           }    ,					)





"""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


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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Path      ,_lowerCamelCase						:	list     )	->				str:
  _lowerCAmelCase						:	List[Any]        =					"""\n""".join(_lowerCamelCase     )
  Path(_lowerCamelCase     ).open("""w"""     ).writelines(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Union[str, Any]:
  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 						__A							(			SCREAMING_SNAKE_CASE_		):






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,					)
     @slow
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Any        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Union[str, Any]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	str        =					4
       _lowerCAmelCase						:	Optional[int]        =					8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[int]        =					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       _lowerCAmelCase						:	Optional[int]        =					SeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=a__    ,					max_target_length=a__    ,					src_lang=a__    ,					tgt_lang=a__    ,					)
       _lowerCAmelCase						:	int        =					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
         _lowerCAmelCase						:	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    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Any        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	int        =					4
       _lowerCAmelCase						:	List[str]        =					LegacySeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=20    ,					max_target_length=a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					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 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    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""  )

       _lowerCAmelCase						:	List[Any]        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       _lowerCAmelCase						:	List[Any]        =					tmp_dir.joinpath("""train.source"""  ).open().readlines()
       _lowerCAmelCase						:	str        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       pack_data_dir(a__    ,					a__    ,					128    ,					a__  )
       _lowerCAmelCase						:	List[Any]        =					{x.name for x in tmp_dir.iterdir()}
       _lowerCAmelCase						:	Tuple        =					{x.name for x in save_dir.iterdir()}
       _lowerCAmelCase						:	Union[str, Any]        =					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    __A  (		self  ):
       if not FAIRSEQ_AVAILABLE:
         return
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					self._get_dataset(max_len=64  )
       _lowerCAmelCase						:	Optional[int]        =					64
       _lowerCAmelCase						:	str        =					ds.make_dynamic_sampler(a__    ,					required_batch_size_multiple=a__  )
       _lowerCAmelCase						:	int        =					[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
       _lowerCAmelCase						:	List[str]        =					DataLoader(a__    ,					batch_sampler=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	List[Any]        =					[]
       _lowerCAmelCase						:	Optional[int]        =					[]
       for batch in data_loader:
         _lowerCAmelCase						:	int        =					batch["""input_ids"""].shape
         _lowerCAmelCase						:	Union[str, Any]        =					src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         _lowerCAmelCase						:	List[str]        =					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    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					self._get_dataset(max_len=512  )
       _lowerCAmelCase						:	int        =					2
       _lowerCAmelCase						:	List[str]        =					ds.make_sortish_sampler(a__    ,					shuffle=a__  )

       _lowerCAmelCase						:	Dict        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2    ,					sampler=a__  )

       _lowerCAmelCase						:	int        =					tokenizer.pad_token_id

       def count_pad_tokens(a__    ,					a__="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    __A  (		self    ,					a__=1000    ,					a__=128  ):
       if os.getenv("""USE_REAL_DATA"""    ,					a__  ):
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/wmt_en_ro"""
         _lowerCAmelCase						:	str        =					max_len * 2 * 64
         if not Path(a__  ).joinpath("""train.len"""  ).exists():
           save_len_file(a__    ,					a__  )
       else:
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/test_data/wmt_en_ro"""
         _lowerCAmelCase						:	Dict        =					max_len * 4
         save_len_file(a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Dict        =					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    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					self._get_dataset()
       _lowerCAmelCase						:	Any        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=0    ,					add_extra_examples=a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					set(DistributedSortishSampler(a__    ,					256    ,					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    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					AutoTokenizer.from_pretrained(a__    ,					use_fast=a__  )
       if tok_name == MBART_TINY:
         _lowerCAmelCase						:	Dict        =					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"""    ,					)
         _lowerCAmelCase						:	Optional[Any]        =					train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
         _lowerCAmelCase						:	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    ,					)
         _lowerCAmelCase						:	Tuple        =					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




