import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




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





      '''simple docstring'''






      _snake_case    		=       RobertaTokenizer
      _snake_case    		=       RobertaTokenizerFast
      _snake_case    		=       True
      _snake_case    		=       {'cls_token': '<s>'}



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


          """simple docstring"""


          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _lowerCamelCase     :		Optional[int] 						=						[
              """l""",
              """o""",
              """w""",
              """e""",
              """r""",
              """s""",
              """t""",
              """i""",
              """d""",
              """n""",
              """\u0120""",
              """\u0120l""",
              """\u0120n""",
              """\u0120lo""",
              """\u0120low""",
              """er""",
              """\u0120lowest""",
              """\u0120newer""",
              """\u0120wider""",
              """<unk>""",
          ]
          _lowerCamelCase     :		List[str] 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))
          _lowerCamelCase     :		List[Any] 						=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          _lowerCamelCase     :		Union[str, Any] 						=						{"""unk_token""": """<unk>"""}

          _lowerCamelCase     :		Dict 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          _lowerCamelCase     :		Tuple 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""merges_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")
          with open(self.merges_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write("""\n""".join(_UpperCamelCase))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  **_UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          kwargs.update(self.special_tokens_map)
          return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  **_UpperCamelCase     :				Tuple)				->Union[str, Any]:


          """simple docstring"""


          kwargs.update(self.special_tokens_map)
          return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				str)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						"""lower newer"""
          _lowerCamelCase     :		Dict 						=						"""lower newer"""
          return input_text, output_text



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.tokenizer_class(self.vocab_file    ,  self.merges_file    ,  **self.special_tokens_map)
          _lowerCamelCase     :		List[Any] 						=						"""lower newer"""
          _lowerCamelCase     :		str 						=						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
          _lowerCamelCase     :		Any 						=						tokenizer.tokenize(_UpperCamelCase)  # , add_prefix_space=True)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						tokens + [tokenizer.unk_token]
          _lowerCamelCase     :		Dict 						=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase)    ,  _UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.get_tokenizer()

          self.assertListEqual(tokenizer.encode("""Hello world!"""    ,  add_special_tokens=_UpperCamelCase)    ,  [0, 3_1414, 232, 328, 2])
          self.assertListEqual(
              tokenizer.encode("""Hello world! cécé herlolip 418"""    ,  add_special_tokens=_UpperCamelCase)    ,  [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]    ,  )



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


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.tokenizer_class.from_pretrained("""roberta-base""")

          _lowerCamelCase     :		Optional[Any] 						=						tokenizer.encode("""sequence builders"""    ,  add_special_tokens=_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						tokenizer.encode("""multi-sequence build"""    ,  add_special_tokens=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer.encode(
              """sequence builders"""    ,  add_special_tokens=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						tokenizer.encode(
              """sequence builders"""    ,  """multi-sequence build"""    ,  add_special_tokens=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase    ,  _UpperCamelCase)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						"""Encode this sequence."""
          _lowerCamelCase     :		Any 						=						tokenizer.byte_encoder[""" """.encode("""utf-8""")[0]]

          # Testing encoder arguments
          _lowerCamelCase     :		Any 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase)
          _lowerCamelCase     :		int 						=						tokenizer.convert_ids_to_tokens(encoded[0])[0]
          self.assertNotEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase)
          _lowerCamelCase     :		int 						=						tokenizer.convert_ids_to_tokens(encoded[0])[0]
          self.assertEqual(_UpperCamelCase    ,  _UpperCamelCase)

          tokenizer.add_special_tokens({"""bos_token""": """<s>"""})
          _lowerCamelCase     :		Optional[Any] 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						tokenizer.convert_ids_to_tokens(encoded[1])[0]
          self.assertNotEqual(_UpperCamelCase    ,  _UpperCamelCase)

          # Testing spaces after special tokens
          _lowerCamelCase     :		Optional[Any] 						=						"""<mask>"""
          tokenizer.add_special_tokens(
              {"""mask_token""": AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase)})  # mask token has a left space
          _lowerCamelCase     :		int 						=						tokenizer.convert_tokens_to_ids(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						"""Encode <mask> sequence"""
          _lowerCamelCase     :		Union[str, Any] 						=						"""Encode <mask>sequence"""

          _lowerCamelCase     :		str 						=						tokenizer.encode(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						encoded.index(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
          self.assertEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Any 						=						tokenizer.encode(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						encoded.index(_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
          self.assertNotEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[int]:


          """simple docstring"""


          pass



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


          """simple docstring"""


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                  _lowerCamelCase     :		Union[str, Any] 						=						self.rust_tokenizer_class.from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)
                  _lowerCamelCase     :		Dict 						=						self.tokenizer_class.from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)
                  _lowerCamelCase     :		str 						=						"""A, <mask> AllenNLP sentence."""
                  _lowerCamelCase     :		int 						=						tokenizer_r.encode_plus(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase    ,  return_token_type_ids=_UpperCamelCase)
                  _lowerCamelCase     :		Union[str, Any] 						=						tokenizer_p.encode_plus(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase    ,  return_token_type_ids=_UpperCamelCase)

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r["""token_type_ids"""])    ,  sum(tokens_p["""token_type_ids"""]))

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])    ,  sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])    ,  )

                  _lowerCamelCase     :		Any 						=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
                  _lowerCamelCase     :		Optional[Any] 						=						tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p["""input_ids"""]    ,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                  self.assertSequenceEqual(tokens_r["""input_ids"""]    ,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

                  self.assertSequenceEqual(
                      _UpperCamelCase    ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
                  self.assertSequenceEqual(
                      _UpperCamelCase    ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->str:


          """simple docstring"""


          for trim_offsets, add_prefix_space in itertools.product([True, False]    ,  repeat=2):
              _lowerCamelCase     :		int 						=						self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
              _lowerCamelCase     :		Optional[int] 						=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

              self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]    ,  _UpperCamelCase)

              self.assertEqual(post_processor_state["""add_prefix_space"""]    ,  _UpperCamelCase)
              self.assertEqual(post_processor_state["""trim_offsets"""]    ,  _UpperCamelCase)




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


          """simple docstring"""


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                  _lowerCamelCase     :		int 						=						"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                  _lowerCamelCase     :		str 						=						F"""{text_of_1_token} {text_of_1_token}"""

                  _lowerCamelCase     :		int 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		List[Any] 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (len(_UpperCamelCase) + 1, len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Optional[Any] 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		Optional[Any] 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (len(_UpperCamelCase) + 1, len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Optional[Any] 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		Optional[int] 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (len(_UpperCamelCase), len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Union[str, Any] 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		Optional[int] 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (len(_UpperCamelCase), len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Union[str, Any] 						=						F""" {text}"""

                  # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                  #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                  # )
                  # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                  # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                  # self.assertEqual(
                  #     encoding.offset_mapping[1],
                  #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                  # )

                  _lowerCamelCase     :		Union[str, Any] 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		List[str] 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (1, 1 + len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (1 + len(_UpperCamelCase) + 1, 1 + len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Union[str, Any] 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		int 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, 1 + len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (1 + len(_UpperCamelCase), 1 + len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )

                  _lowerCamelCase     :		Dict 						=						self.rust_tokenizer_class.from_pretrained(
                      _UpperCamelCase    ,  use_fast=_UpperCamelCase    ,  add_prefix_space=_UpperCamelCase    ,  trim_offsets=_UpperCamelCase)
                  _lowerCamelCase     :		Tuple 						=						tokenizer_r(_UpperCamelCase    ,  return_offsets_mapping=_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(encoding.offset_mapping[0]    ,  (0, 1 + len(_UpperCamelCase)))
                  self.assertEqual(
                      encoding.offset_mapping[1]    ,  (1 + len(_UpperCamelCase), 1 + len(_UpperCamelCase) + 1 + len(_UpperCamelCase))    ,  )





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()




