"""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


UpperCAmelCase__  		=						get_tests_dir("""fixtures/test_sentencepiece.model""")


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

UpperCAmelCase__  		=						2_5_0_0_0_4
UpperCAmelCase__  		=						2_5_0_0_2_0


@require_sentencepiece
@require_tokenizers
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							MBartTokenizer
    _snake_case     :  Dict     							=							MBartTokenizerFast
    _snake_case     :  int     							=							True
    _snake_case     :  Dict     							=							True


    def      lowerCAmelCase_  (					self	:  str					):
      super().setUp()

      # We have a SentencePiece fixture for testing
      _UpperCAmelCase						 =      MBartTokenizer(__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase					)
      tokenizer.save_pretrained(self.tmpdirname					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      MBartTokenizer(__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase					)

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

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)   ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,				)

      _UpperCAmelCase						 =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              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""",
              """.""",
          ]   ,				)
      _UpperCAmelCase						 =      tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              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 ^
          ]   ,				)

      _UpperCAmelCase						 =      tokenizer.convert_ids_to_tokens(__lowerCAmelCase					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              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      lowerCAmelCase_  (					self	:  int					):
      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

      _UpperCAmelCase						 =      (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})'''					):
          _UpperCAmelCase						 =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,				**__lowerCAmelCase					)
          _UpperCAmelCase						 =      self.tokenizer_class.from_pretrained(__lowerCAmelCase   ,				**__lowerCAmelCase					)

          _UpperCAmelCase						 =      tempfile.mkdtemp()

          _UpperCAmelCase						 =      tokenizer_r.save_pretrained(__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.save_pretrained(__lowerCAmelCase					)

          # 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					)					)
          _UpperCAmelCase						 =      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f					)
          self.assertSequenceEqual(__lowerCAmelCase   ,				__lowerCAmelCase					)

          # Checks everything loads correctly in the same way
          _UpperCAmelCase						 =      tokenizer_r.from_pretrained(__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

          shutil.rmtree(__lowerCAmelCase					)

          # Save tokenizer rust, legacy_format=True
          _UpperCAmelCase						 =      tempfile.mkdtemp()

          _UpperCAmelCase						 =      tokenizer_r.save_pretrained(__lowerCAmelCase   ,				legacy_format=__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.save_pretrained(__lowerCAmelCase					)

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

          # Checks everything loads correctly in the same way
          _UpperCAmelCase						 =      tokenizer_r.from_pretrained(__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

          shutil.rmtree(__lowerCAmelCase					)

          # Save tokenizer rust, legacy_format=False
          _UpperCAmelCase						 =      tempfile.mkdtemp()

          _UpperCAmelCase						 =      tokenizer_r.save_pretrained(__lowerCAmelCase   ,				legacy_format=__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.save_pretrained(__lowerCAmelCase					)

          # 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
          _UpperCAmelCase						 =      tokenizer_r.from_pretrained(__lowerCAmelCase					)
          _UpperCAmelCase						 =      tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

          shutil.rmtree(__lowerCAmelCase					)



@require_torch
@require_sentencepiece
@require_tokenizers
class        a				(		unittest.TestCase   ):
    _snake_case     :  List[str]     							=							'facebook/mbart-large-en-ro'
    _snake_case     :  Union[str, Any]     							=							[
        ' 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.',
    ]
    _snake_case     :  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.',
    ]
    _snake_case     :  Dict     							=							[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[Any]					):
      _UpperCAmelCase						 =      MBartTokenizer.from_pretrained(
          cls.checkpoint_name   ,				src_lang="""en_XX"""   ,				tgt_lang="""ro_RO"""					)
      _UpperCAmelCase						 =      1
      return cls


    def      lowerCAmelCase_  (					self	:  Any					):
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]   ,				25_0001					)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]   ,				25_0004					)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]   ,				25_0020					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
      self.assertListEqual(self.expected_src_tokens   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      self.assertIn(__lowerCAmelCase   ,				self.tokenizer.all_special_ids					)
      _UpperCAmelCase						 =      [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
      _UpperCAmelCase						 =      self.tokenizer.decode(__lowerCAmelCase   ,				skip_special_tokens=__lowerCAmelCase					)
      _UpperCAmelCase						 =      self.tokenizer.decode(generated_ids[1:]   ,				skip_special_tokens=__lowerCAmelCase					)
      self.assertEqual(__lowerCAmelCase   ,				__lowerCAmelCase					)
      self.assertNotIn(self.tokenizer.eos_token   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      ["""this is gunna be a long sentence """ * 20]
      assert isinstance(src_text[0]   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      10
      _UpperCAmelCase						 =      self.tokenizer(__lowerCAmelCase   ,				max_length=__lowerCAmelCase   ,				truncation=__lowerCAmelCase					).input_ids[0]
      self.assertEqual(ids[-2]   ,				2					)
      self.assertEqual(ids[-1]   ,				__lowerCAmelCase					)
      self.assertEqual(len(__lowerCAmelCase					)   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  int					):
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]					)   ,				[25_0026, 25_0001]					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      tempfile.mkdtemp()
      _UpperCAmelCase						 =      self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      MBartTokenizer.from_pretrained(__lowerCAmelCase					)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,				__lowerCAmelCase					)


    @require_torch
    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.tokenizer(self.src_text   ,				text_target=self.tgt_text   ,				padding=__lowerCAmelCase   ,				return_tensors="""pt"""					)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.tokenizer(
          self.src_text   ,				text_target=self.tgt_text   ,				padding=__lowerCAmelCase   ,				truncation=__lowerCAmelCase   ,				max_length=len(self.expected_src_tokens					)   ,				return_tensors="""pt"""   ,				)

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

      self.assertIsInstance(__lowerCAmelCase   ,				__lowerCAmelCase					)

      self.assertEqual((2, 14)   ,				batch.input_ids.shape					)
      self.assertEqual((2, 14)   ,				batch.attention_mask.shape					)
      _UpperCAmelCase						 =      batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens   ,				__lowerCAmelCase					)
      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      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      self.tokenizer(self.src_text   ,				padding=__lowerCAmelCase   ,				truncation=__lowerCAmelCase   ,				max_length=3   ,				return_tensors="""pt"""					)
      _UpperCAmelCase						 =      self.tokenizer(
          text_target=self.tgt_text   ,				padding=__lowerCAmelCase   ,				truncation=__lowerCAmelCase   ,				max_length=10   ,				return_tensors="""pt"""					)
      _UpperCAmelCase						 =      targets["""input_ids"""]
      _UpperCAmelCase						 =      shift_tokens_right(__lowerCAmelCase   ,				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      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.tokenizer._build_translation_inputs(
          """A test"""   ,				return_tensors="""pt"""   ,				src_lang="""en_XX"""   ,				tgt_lang="""ar_AR"""					)

      self.assertEqual(
          nested_simplify(__lowerCAmelCase					)   ,				{
              # A, test, EOS, en_XX
              """input_ids""": [[62, 3034, 2, 25_0004]],
              """attention_mask""": [[1, 1, 1, 1]],
              # ar_AR
              """forced_bos_token_id""": 25_0001,
          }   ,				)

"""simple docstring"""


from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  for part_id in partition_order:
    _UpperCAmelCase						 =      df.where(f'''SPARK_PARTITION_ID() = {part_id}'''		).collect()
    for row_idx, row in enumerate(lowercase		):
      expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict())		)
  return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(1_00		).repartition(1		)
  _UpperCAmelCase						 =      Spark(lowercase		)
  # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
  # that each partition can hold 2 rows.
  spark_builder._repartition_df_if_needed(max_shard_size=16		)
  # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
  assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(10		).repartition(2		)
  _UpperCAmelCase						 =      [1, 0]
  _UpperCAmelCase						 =      _generate_iterable_examples(lowercase   ,lowercase		)  # Reverse the partitions.
  _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,lowercase		)

  for i, (row_id, row_dict) in enumerate(generate_fn()		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      expected_row_ids_and_row_dicts[i]
    assert row_id == expected_row_id
    assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(10		).repartition(1		)
  _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		)
  assert it.n_shards == 1
  for i, (row_id, row_dict) in enumerate(lowercase		):
    assert row_id == f'''0_{i}'''
    assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(30		).repartition(3		)
  # Mock the generator so that shuffle reverses the partition indices.
  with patch("""numpy.random.Generator"""		) as generator_mock:
    _UpperCAmelCase						 =      lambda lowercase		: x.reverse()
    _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,[2, 1, 0]		)

    _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		).shuffle_data_sources(lowercase		)
    assert shuffled_it.n_shards == 3
    for i, (row_id, row_dict) in enumerate(lowercase		):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      expected_row_ids_and_row_dicts[i]
      assert row_id == expected_row_id
      assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(20		).repartition(4		)

  # Partitions 0 and 2
  _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		).shard_data_sources(worker_id=0   ,num_workers=2		)
  assert shard_it_a.n_shards == 2
  _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,[0, 2]		)
  for i, (row_id, row_dict) in enumerate(lowercase		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      expected_row_ids_and_row_dicts_a[i]
    assert row_id == expected_row_id
    assert row_dict == expected_row_dict

  # Partitions 1 and 3
  _UpperCAmelCase						 =      SparkExamplesIterable(lowercase		).shard_data_sources(worker_id=1   ,num_workers=2		)
  assert shard_it_a.n_shards == 2
  _UpperCAmelCase						 =      _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase   ,[1, 3]		)
  for i, (row_id, row_dict) in enumerate(lowercase		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      expected_row_ids_and_row_dicts_a[i]
    assert row_id == expected_row_id
    assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      pyspark.sql.SparkSession.builder.master("""local[*]"""		).appName("""pyspark"""		).getOrCreate()
  _UpperCAmelCase						 =      spark.range(1_00		).repartition(1		)
  _UpperCAmelCase						 =      Spark(lowercase		)
  # Choose a small max_shard_size for maximum partitioning.
  spark_builder._repartition_df_if_needed(max_shard_size=1		)
  # The new number of partitions should not be greater than the number of rows.
  assert spark_builder.df.rdd.getNumPartitions() == 1_00
