"""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 contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							:
     _UpperCamelCase							:      str		 =		None







@experimental
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[int]     )	->				List[Any]:
  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  return _map_with_joblib(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Tuple     )	->				Union[str, Any]:
  _lowerCAmelCase						:	int        =					num_proc if num_proc <= len(_lowerCamelCase     ) else len(_lowerCamelCase     )
  _lowerCAmelCase						:	Union[str, Any]        =					[]  # We organize the splits ourselve (contiguous splits)
  for index in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					len(_lowerCamelCase     ) // num_proc
    _lowerCAmelCase						:	str        =					len(_lowerCamelCase     ) % num_proc
    _lowerCAmelCase						:	Tuple        =					div * index + min(_lowerCamelCase      ,_lowerCamelCase     )
    _lowerCAmelCase						:	int        =					start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)     )

  if len(_lowerCamelCase     ) != sum(len(i[1]     ) for i in split_kwds     ):
    raise ValueError(
        f"Error dividing inputs iterable among processes. "
        f"Total number of objects {len(_lowerCamelCase     )}, "
        f"length: {sum(len(i[1]     ) for i in split_kwds     )}"     )

  logger.info(
      f"Spawning {num_proc} processes for {len(_lowerCamelCase     )} objects in slices of {[len(i[1]     ) for i in split_kwds]}"     )
  _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					None, None
  if not disable_tqdm:
    _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					(RLock(),), tqdm.set_lock
  with Pool(_lowerCamelCase      ,initargs=_lowerCamelCase      ,initializer=_lowerCamelCase     ) as pool:
    _lowerCAmelCase						:	str        =					pool.map(_lowerCamelCase      ,_lowerCamelCase     )
  logger.info(f"Finished {num_proc} processes"     )
  _lowerCAmelCase						:	int        =					[obj for proc_res in mapped for obj in proc_res]
  logger.info(f"Unpacked {len(_lowerCamelCase     )} objects"     )

  return mapped





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	str     )	->				Optional[Any]:
  # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
  # and it requires monkey-patching joblib internal classes which is subject to change
  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name      ,n_jobs=_lowerCamelCase     ):
    return joblib.Parallel()(
        joblib.delayed(_lowerCamelCase     )((function, obj, types, None, True, None)     ) for obj in iterable     )






@experimental
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _lowerCAmelCase						:	Optional[Any]        =					None




