



import shutil
import tempfile
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


A_		:     Optional[Any]        =     get_tests_dir('''fixtures/test_sentencepiece.model''')

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

A_		:     List[str]        =     25_00_04
A_		:     str        =     25_00_20



@require_sentencepiece
@require_tokenizers
class 		_lowercase		(       UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			MBartaaTokenizer
   _UpperCAmelCase     	=			MBartaaTokenizerFast
   _UpperCAmelCase     	=			True
   _UpperCAmelCase     	=			True

   def 		A							(      self			:       str							)       ->				Tuple:


    """simple docstring"""






    super().setUp()

    # We have a SentencePiece fixture for testing
    a						=		MBartaaTokenizer(__lowerCAmelCase							,   src_lang="en_XX"							,   tgt_lang="ro_RO"							,   keep_accents=__lowerCAmelCase							)
    tokenizer.save_pretrained(self.tmpdirname							)

   def 		A							(      self			:       int							)       ->				Dict:


    """simple docstring"""






    a						=		"<s>"
    a						=		0

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase							)							,   __lowerCAmelCase							)

   def 		A							(      self			:       int							)       ->				str:


    """simple docstring"""






    a						=		list(self.get_tokenizer().get_vocab().keys()							)

    self.assertEqual(vocab_keys[0]							,   "<s>"							)
    self.assertEqual(vocab_keys[1]							,   "<pad>"							)
    self.assertEqual(vocab_keys[-1]							,   "<mask>"							)
    self.assertEqual(len(__lowerCAmelCase							)							,   1054							)

   def 		A							(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






    self.assertEqual(self.get_tokenizer().vocab_size							,   1054							)

   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    a						=		MBartaaTokenizer(__lowerCAmelCase							,   src_lang="en_XX"							,   tgt_lang="ro_RO"							,   keep_accents=__lowerCAmelCase							)

    a						=		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]]							,   )

    a						=		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", "."]							,   )
    a						=		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]
        ]							,   )

    a						=		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>", "."]							,   )

   @slow
   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a						=		{"input_ids": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__lowerCAmelCase							,   model_name="facebook/mbart-large-50"							,   revision="d3913889c59cd5c9e456b269c376325eabad57e2"							,   )




   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    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

    a						=		(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
      a						=		self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase							,   **__lowerCAmelCase							)
      a						=		self.tokenizer_class.from_pretrained(__lowerCAmelCase							,   **__lowerCAmelCase							)

      a						=		tempfile.mkdtemp()

      a						=		tokenizer_r.save_pretrained(__lowerCAmelCase							)
      a						=		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							)							)
      a						=		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
      a						=		tokenizer_r.from_pretrained(__lowerCAmelCase							)
      a						=		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
      a						=		tempfile.mkdtemp()

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

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

      # Checks everything loads correctly in the same way
      a						=		tokenizer_r.from_pretrained(__lowerCAmelCase							)
      a						=		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
      a						=		tempfile.mkdtemp()

      a						=		tokenizer_r.save_pretrained(__lowerCAmelCase							,   legacy_format=__lowerCAmelCase							)
      a						=		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
      a						=		tokenizer_r.from_pretrained(__lowerCAmelCase							)
      a						=		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 		_lowercase		(       unittest.TestCase	):
   _UpperCAmelCase     	=			'''facebook/mbart-large-50-one-to-many-mmt'''
   _UpperCAmelCase     	=			[
       ''' 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     	=			[
       '''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     	=			[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]

   @classmethod
   def 		A							(      cls			:       List[str]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		MBartaaTokenizer.from_pretrained(
        cls.checkpoint_name							,   src_lang="en_XX"							,   tgt_lang="ro_RO"							)
    a						=		1
    return cls

   def 		A							(      self			:       Tuple							)       ->				str:


    """simple docstring"""






    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							)
    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]							,   25_0038							)

   def 		A							(      self			:       str							)       ->				str:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							)       ->				Optional[int]:


    """simple docstring"""






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

   def 		A							(      self			:       Any							)       ->				List[str]:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[int]							)       ->				Optional[Any]:


    """simple docstring"""






    self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]							)							,   [25_0053, 25_0001]							)

   def 		A							(      self			:       str							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    a						=		self.tokenizer.fairseq_tokens_to_ids
    self.tokenizer.save_pretrained(__lowerCAmelCase							)
    a						=		MBartaaTokenizer.from_pretrained(__lowerCAmelCase							)
    self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,   __lowerCAmelCase							)

   @require_torch
   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.tokenizer(self.src_text							,   text_target=self.tgt_text							,   padding=__lowerCAmelCase							,   return_tensors="pt"							)
    a						=		shift_tokens_right(batch["labels"]							,   self.tokenizer.pad_token_id							)

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

   @require_torch
   def 		A							(      self			:       str							)       ->				str:


    """simple docstring"""






    a						=		self.tokenizer(
        self.src_text							,   text_target=self.tgt_text							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							,   max_length=len(self.expected_src_tokens							)							,   return_tensors="pt"							,   )
    a						=		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							)
    a						=		batch.input_ids.tolist()[0]
    self.assertListEqual(self.expected_src_tokens							,   __lowerCAmelCase							)
    self.assertEqual(2							,   batch.decoder_input_ids[0, 0]							)  # decoder_start_token_id
    # Test that special tokens are reset
    self.assertEqual(self.tokenizer.prefix_tokens							,   [EN_CODE]							)
    self.assertEqual(self.tokenizer.suffix_tokens							,   [self.tokenizer.eos_token_id]							)

   def 		A							(      self			:       Optional[int]							)       ->				Dict:


    """simple docstring"""






    a						=		self.tokenizer(self.src_text							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							,   max_length=3							,   return_tensors="pt"							)
    a						=		self.tokenizer(
        text_target=self.tgt_text							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							,   max_length=10							,   return_tensors="pt"							)
    a						=		targets["input_ids"]
    a						=		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 		A							(      self			:       Optional[int]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.tokenizer._build_translation_inputs(
        "A test"							,   return_tensors="pt"							,   src_lang="en_XX"							,   tgt_lang="ar_AR"							)

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

