


'''simple docstring'''






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 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		RobertaTokenizer
 _lowerCAmelCase	   =		RobertaTokenizerFast
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		{"""cls_token""": """<s>"""}
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a      =							[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
      ]
      _a      =							dict(zip(__magic_name__ ,			range(len(__magic_name__  )  )  )  )
      _a      =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      _a      =							{'unk_token': '<unk>'}

      _a      =							os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['vocab_file']  )
      _a      =							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(__magic_name__  ) + '\n'  )
      with open(self.merges_file ,			'w' ,			encoding='utf-8'  ) as fp:
           fp.write('\n'.join(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self ,			**__magic_name__  )   ->							List[str]:
      kwargs.update(self.special_tokens_map  )
      return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			**__magic_name__  )   ->							Any:
      kwargs.update(self.special_tokens_map  )
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							'lower newer'
      _a      =							'lower newer'
      return input_text, output_text
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.tokenizer_class(self.vocab_file ,			self.merges_file ,			**self.special_tokens_map  )
      _a      =							'lower newer'
      _a      =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      _a      =							tokenizer.tokenize(__magic_name__  )  # , add_prefix_space=True)
      self.assertListEqual(__magic_name__ ,			__magic_name__  )

      _a      =							tokens + [tokenizer.unk_token]
      _a      =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__  ) ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('Hello world!' ,			add_special_tokens=__magic_name__  ) ,			[0, 3_14_14, 2_32, 3_28, 2]  )
      self.assertListEqual(
          tokenizer.encode('Hello world! cécé herlolip 418' ,			add_special_tokens=__magic_name__  ) ,			[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] ,			)
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.tokenizer_class.from_pretrained('roberta-base'  )

      _a      =							tokenizer.encode('sequence builders' ,			add_special_tokens=__magic_name__  )
      _a      =							tokenizer.encode('multi-sequence build' ,			add_special_tokens=__magic_name__  )

      _a      =							tokenizer.encode(
          'sequence builders' ,			add_special_tokens=__magic_name__ ,			add_prefix_space=__magic_name__  )
      _a      =							tokenizer.encode(
          'sequence builders' ,			'multi-sequence build' ,			add_special_tokens=__magic_name__ ,			add_prefix_space=__magic_name__  )

      _a      =							tokenizer.build_inputs_with_special_tokens(__magic_name__  )
      _a      =							tokenizer.build_inputs_with_special_tokens(__magic_name__ ,			__magic_name__  )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.get_tokenizer()

      _a      =							'Encode this sequence.'
      _a      =							tokenizer.byte_encoder[' '.encode('utf-8'  )[0]]

      # Testing encoder arguments
      _a      =							tokenizer.encode(__magic_name__ ,			add_special_tokens=__magic_name__ ,			add_prefix_space=__magic_name__  )
      _a      =							tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
      self.assertNotEqual(__magic_name__ ,			__magic_name__  )

      _a      =							tokenizer.encode(__magic_name__ ,			add_special_tokens=__magic_name__ ,			add_prefix_space=__magic_name__  )
      _a      =							tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
      self.assertEqual(__magic_name__ ,			__magic_name__  )

      tokenizer.add_special_tokens({'bos_token': '<s>'}  )
      _a      =							tokenizer.encode(__magic_name__ ,			add_special_tokens=__magic_name__  )
      _a      =							tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
      self.assertNotEqual(__magic_name__ ,			__magic_name__  )

      # Testing spaces after special tokens
      _a      =							'<mask>'
      tokenizer.add_special_tokens(
          {'mask_token': AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  )}  )  # mask token has a left space
      _a      =							tokenizer.convert_tokens_to_ids(__magic_name__  )

      _a      =							'Encode <mask> sequence'
      _a      =							'Encode <mask>sequence'

      _a      =							tokenizer.encode(__magic_name__  )
      _a      =							encoded.index(__magic_name__  )
      _a      =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
      self.assertEqual(__magic_name__ ,			__magic_name__  )

      _a      =							tokenizer.encode(__magic_name__  )
      _a      =							encoded.index(__magic_name__  )
      _a      =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
      self.assertNotEqual(__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      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(__magic_name__ ,			**__magic_name__  )
                _a      =							self.tokenizer_class.from_pretrained(__magic_name__ ,			**__magic_name__  )
                _a      =							'A, <mask> AllenNLP sentence.'
                _a      =							tokenizer_r.encode_plus(__magic_name__ ,			add_special_tokens=__magic_name__ ,			return_token_type_ids=__magic_name__  )
                _a      =							tokenizer_p.encode_plus(__magic_name__ ,			add_special_tokens=__magic_name__ ,			return_token_type_ids=__magic_name__  )

                # 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']  ) ,			)

                _a      =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                _a      =							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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )
                self.assertSequenceEqual(tokens_r['input_ids'] ,			[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )

                self.assertSequenceEqual(
                    __magic_name__ ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                self.assertSequenceEqual(
                    __magic_name__ ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      for trim_offsets, add_prefix_space in itertools.product([True, False] ,			repeat=2  ):
           _a      =							self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )

           _a      =							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
           _a      =							json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

           self.assertEqual(pre_tokenizer_state['add_prefix_space'] ,			__magic_name__  )

           self.assertEqual(post_processor_state['add_prefix_space'] ,			__magic_name__  )
           self.assertEqual(post_processor_state['trim_offsets'] ,			__magic_name__  )




 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
      # `trim_offsets`
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'  ):
                _a      =							'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                _a      =							f'{text_of_1_token} {text_of_1_token}'

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(len(__magic_name__  ) + 1, len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(len(__magic_name__  ) + 1, len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(len(__magic_name__  ), len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(len(__magic_name__  ), len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							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)),
                # )

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(1, 1 + len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(1 + len(__magic_name__  ) + 1, 1 + len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, 1 + len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(1 + len(__magic_name__  ), 1 + len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)

                _a      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__ ,			use_fast=__magic_name__ ,			add_prefix_space=__magic_name__ ,			trim_offsets=__magic_name__  )
                _a      =							tokenizer_r(__magic_name__ ,			return_offsets_mapping=__magic_name__ ,			add_special_tokens=__magic_name__  )
                self.assertEqual(encoding.offset_mapping[0] ,			(0, 1 + len(__magic_name__  ))  )
                self.assertEqual(
                    encoding.offset_mapping[1] ,			(1 + len(__magic_name__  ), 1 + len(__magic_name__  ) + 1 + len(__magic_name__  )) ,			)





'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_   :      Union[str, Any]				    =      logging.get_logger(__name__)

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _a      =							d

      # for backward compatibility
      if not hasattr(self ,			'sp_model_kwargs'  ):
           _a      =							{}

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)

