import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase			:							Any     =get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
@require_tokenizers
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       XLMRobertaTokenizer
      _snake_case    		=       XLMRobertaTokenizerFast
      _snake_case    		=       True
      _snake_case    		=       True



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


          """simple docstring"""


          super().setUp()

          # We have a SentencePiece fixture for testing
          _lowerCamelCase     :		Union[str, Any] 						=						XLMRobertaTokenizer(_UpperCamelCase    ,  keep_accents=_UpperCamelCase)
          tokenizer.save_pretrained(self.tmpdirname)



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						"""<pad>"""
          _lowerCamelCase     :		Optional[int] 						=						1

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						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(_UpperCamelCase)    ,  1002)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          self.assertEqual(self.get_tokenizer().vocab_size    ,  1002)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaTokenizer(_UpperCamelCase    ,  keep_accents=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						tokenizer.tokenize("""This is a test""")
          self.assertListEqual(_UpperCamelCase    ,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

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

          _lowerCamelCase     :		List[Any] 						=						tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
          self.assertListEqual(
              _UpperCamelCase    ,  [
                  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     :		Optional[Any] 						=						tokenizer.convert_tokens_to_ids(_UpperCamelCase)
          self.assertListEqual(
              _UpperCamelCase    ,  [
                  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     :		Any 						=						tokenizer.convert_ids_to_tokens(_UpperCamelCase)
          self.assertListEqual(
              _UpperCamelCase    ,  [
                  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 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->str:


          """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

          _lowerCamelCase     :		Tuple 						=						(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                  _lowerCamelCase     :		Dict 						=						self.rust_tokenizer_class.from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)
                  _lowerCamelCase     :		Optional[Any] 						=						self.tokenizer_class.from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)

                  _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

                  _lowerCamelCase     :		Optional[int] 						=						tokenizer_r.save_pretrained(_UpperCamelCase)
                  _lowerCamelCase     :		Optional[int] 						=						tokenizer_p.save_pretrained(_UpperCamelCase)

                  # 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     :		Optional[int] 						=						tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f)
                  self.assertSequenceEqual(_UpperCamelCase    ,  _UpperCamelCase)

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

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

                  shutil.rmtree(_UpperCamelCase)

                  # Save tokenizer rust, legacy_format=True
                  _lowerCamelCase     :		Union[str, Any] 						=						tempfile.mkdtemp()

                  _lowerCamelCase     :		Dict 						=						tokenizer_r.save_pretrained(_UpperCamelCase    ,  legacy_format=_UpperCamelCase)
                  _lowerCamelCase     :		Union[str, Any] 						=						tokenizer_p.save_pretrained(_UpperCamelCase)

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

                  # Checks everything loads correctly in the same way
                  _lowerCamelCase     :		Optional[int] 						=						tokenizer_r.from_pretrained(_UpperCamelCase)
                  _lowerCamelCase     :		Union[str, Any] 						=						tokenizer_p.from_pretrained(_UpperCamelCase)

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

                  shutil.rmtree(_UpperCamelCase)

                  # Save tokenizer rust, legacy_format=False
                  _lowerCamelCase     :		Dict 						=						tempfile.mkdtemp()

                  _lowerCamelCase     :		Union[str, Any] 						=						tokenizer_r.save_pretrained(_UpperCamelCase    ,  legacy_format=_UpperCamelCase)
                  _lowerCamelCase     :		Any 						=						tokenizer_p.save_pretrained(_UpperCamelCase)

                  # 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     :		List[str] 						=						tokenizer_r.from_pretrained(_UpperCamelCase)
                  _lowerCamelCase     :		Dict 						=						tokenizer_p.from_pretrained(_UpperCamelCase)

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

                  shutil.rmtree(_UpperCamelCase)



      @cached_property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[Any]:


          """simple docstring"""


          return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""")



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(_UpperCamelCase    ,  f.name)
              _lowerCamelCase     :		Tuple 						=						XLMRobertaTokenizer(f.name    ,  keep_accents=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						pickle.dumps(_UpperCamelCase)
          pickle.loads(_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          if not self.test_rust_tokenizer:
              return

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer()
          _lowerCamelCase     :		int 						=						self.get_rust_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						"""I was born in 92000, and this is falsé."""

          _lowerCamelCase     :		List[Any] 						=						tokenizer.tokenize(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						rust_tokenizer.tokenize(_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						rust_tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		str 						=						self.get_rust_tokenizer()
          _lowerCamelCase     :		Any 						=						tokenizer.encode(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						rust_tokenizer.encode(_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						"""Hello World!"""
          _lowerCamelCase     :		Tuple 						=						[0, 3_5378, 6661, 38, 2]
          # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
          # xlmr.eval()
          # xlmr.encode(symbols)

          self.assertListEqual(_UpperCamelCase    ,  self.big_tokenizer.encode(_UpperCamelCase))



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						(
              """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
              """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
          )
          _lowerCamelCase     :		Optional[int] 						=						[
              0,
              3293,
              83,
              10,
              4552,
              4989,
              7986,
              678,
              10,
              5915,
              111,
              17_9459,
              12_4850,
              4,
              6044,
              237,
              12,
              6,
              5,
              6,
              4,
              6780,
              705,
              15,
              1388,
              44,
              378,
              1_0114,
              711,
              152,
              20,
              6,
              5,
              2_2376,
              642,
              1221,
              1_5190,
              3_4153,
              450,
              5608,
              959,
              1119,
              5_7702,
              136,
              186,
              47,
              1098,
              2_9367,
              47,
              # 4426, # What fairseq tokenizes from "<unk>": "_<"
              # 3678, # What fairseq tokenizes from "<unk>": "unk"
              # 2740, # What fairseq tokenizes from "<unk>": ">"
              3,  # What we tokenize from "<unk>": "<unk>"
              6,  # Residue from the tokenization: an extra sentencepiece underline
              4,
              6044,
              237,
              6284,
              5_0901,
              528,
              31,
              90,
              34,
              927,
              2,
          ]
          # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
          # xlmr.eval()
          # xlmr.encode(symbols)

          self.assertListEqual(_UpperCamelCase    ,  self.big_tokenizer.encode(_UpperCamelCase))




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						{"""input_ids""": [[0, 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], [0, 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], [0, 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=_UpperCamelCase    ,  model_name="""xlm-roberta-base"""    ,  revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""    ,  )





def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    if n == 0:
        return 0
    _lowerCamelCase     :		Tuple 						=						float("""-inf"""	)
    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max(
            __A   , prices[i - 1] + naive_cut_rod_recursive(n - i   , __A	)	)

    return max_revue







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    _lowerCamelCase     :		Optional[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    return _top_down_cut_rod_recursive(__A   , __A   , __A	)







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        _lowerCamelCase     :		int 						=						float("""-inf"""	)
        for i in range(1   , n + 1	):
            _lowerCamelCase     :		Optional[Any] 						=						max(
                __A   , prices[i - 1] + _top_down_cut_rod_recursive(n - i   , __A   , __A	)   , )

        _lowerCamelCase     :		Optional[Any] 						=						max_revenue

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    _lowerCamelCase     :		List[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    _lowerCamelCase     :		Any 						=						0

    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max_rev[i]
        for j in range(1   , i + 1	):
            _lowerCamelCase     :		List[Any] 						=						max(__A   , prices[j - 1] + max_rev[i - j]	)

        _lowerCamelCase     :		int 						=						max_revenue_i

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if n < 0:
        _lowerCamelCase     :		Any 						=						F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(__A	)

    if n > len(__A	):
        _lowerCamelCase     :		List[Any] 						=						(
            """Each integral piece of rod must have a corresponding price. """
            F"""Got n = {n} but length of prices = {len(__A	)}"""
        )
        raise ValueError(__A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[6, 10, 12, 15, 20, 23]
    _lowerCamelCase     :		List[str] 						=						len(__A	)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    _lowerCamelCase     :		Tuple 						=						36

    _lowerCamelCase     :		Any 						=						top_down_cut_rod(__A   , __A	)
    _lowerCamelCase     :		Dict 						=						bottom_up_cut_rod(__A   , __A	)
    _lowerCamelCase     :		List[str] 						=						naive_cut_rod_recursive(__A   , __A	)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()




