



"""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 							_UpperCAmelCase							( a		,unittest.TestCase      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         super().setUp()

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

         _UpperCAmelCase							:  int											=    os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['''vocab_file''']		)
         _UpperCAmelCase							:  int											=    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(A		) + '''\n'''		)
         with open(self.merges_file       ,	'''w'''       ,	encoding='''utf-8'''		) as fp:
             fp.write('''\n'''.join(A		)		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					Dict:
         kwargs.update(self.special_tokens_map		)
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					int:
         kwargs.update(self.special_tokens_map		)
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    '''lower newer'''
         _UpperCAmelCase							:  Optional[Any]											=    '''lower newer'''
         return input_text, output_text





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  List[str]											=    self.tokenizer_class(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map		)
         _UpperCAmelCase							:  str											=    '''lower newer'''
         _UpperCAmelCase							:  List[Any]											=    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         _UpperCAmelCase							:  str											=    tokenizer.tokenize(A		)  # , add_prefix_space=True)
         self.assertListEqual(A       ,	A		)

         _UpperCAmelCase							:  Tuple											=    tokens + [tokenizer.unk_token]
         _UpperCAmelCase							:  List[Any]											=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A		)       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('''Hello world!'''       ,	add_special_tokens=A		)       ,	[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]		)
         self.assertListEqual(
             tokenizer.encode('''Hello world! cécé herlolip 418'''       ,	add_special_tokens=A		)       ,	[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]       ,	)





     @slow
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Optional[int]											=    self.tokenizer_class.from_pretrained('''roberta-base'''		)

         _UpperCAmelCase							:  Union[str, Any]											=    tokenizer.encode('''sequence builders'''       ,	add_special_tokens=A		)
         _UpperCAmelCase							:  Any											=    tokenizer.encode('''multi-sequence build'''       ,	add_special_tokens=A		)

         _UpperCAmelCase							:  str											=    tokenizer.encode(
             '''sequence builders'''       ,	add_special_tokens=A       ,	add_prefix_space=A		)
         _UpperCAmelCase							:  Optional[Any]											=    tokenizer.encode(
             '''sequence builders'''       ,	'''multi-sequence build'''       ,	add_special_tokens=A       ,	add_prefix_space=A		)

         _UpperCAmelCase							:  str											=    tokenizer.build_inputs_with_special_tokens(A		)
         _UpperCAmelCase							:  int											=    tokenizer.build_inputs_with_special_tokens(A       ,	A		)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Dict											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[str]											=    '''Encode this sequence.'''
         _UpperCAmelCase							:  Optional[int]											=    tokenizer.byte_encoder[''' '''.encode('''utf-8'''		)[0]]

         # Testing encoder arguments
         _UpperCAmelCase							:  Optional[int]											=    tokenizer.encode(A       ,	add_special_tokens=A       ,	add_prefix_space=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
         self.assertNotEqual(A       ,	A		)

         _UpperCAmelCase							:  Tuple											=    tokenizer.encode(A       ,	add_special_tokens=A       ,	add_prefix_space=A		)
         _UpperCAmelCase							:  Optional[Any]											=    tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
         self.assertEqual(A       ,	A		)

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

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

         _UpperCAmelCase							:  Union[str, Any]											=    '''Encode <mask> sequence'''
         _UpperCAmelCase							:  str											=    '''Encode <mask>sequence'''

         _UpperCAmelCase							:  List[Any]											=    tokenizer.encode(A		)
         _UpperCAmelCase							:  Tuple											=    encoded.index(A		)
         _UpperCAmelCase							:  List[str]											=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
         self.assertEqual(A       ,	A		)

         _UpperCAmelCase							:  Union[str, Any]											=    tokenizer.encode(A		)
         _UpperCAmelCase							:  int											=    encoded.index(A		)
         _UpperCAmelCase							:  str											=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
         self.assertNotEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         pass





     def  __lowerCAmelCase	(					self		)							->					str:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                 _UpperCAmelCase							:  List[str]											=    self.rust_tokenizer_class.from_pretrained(A       ,	**A		)
                 _UpperCAmelCase							:  List[Any]											=    self.tokenizer_class.from_pretrained(A       ,	**A		)
                 _UpperCAmelCase							:  str											=    '''A, <mask> AllenNLP sentence.'''
                 _UpperCAmelCase							:  int											=    tokenizer_r.encode_plus(A       ,	add_special_tokens=A       ,	return_token_type_ids=A		)
                 _UpperCAmelCase							:  Optional[int]											=    tokenizer_p.encode_plus(A       ,	add_special_tokens=A       ,	return_token_type_ids=A		)

                 # 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''']		)       ,	)

                 _UpperCAmelCase							:  int											=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
                 _UpperCAmelCase							:  List[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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
                 self.assertSequenceEqual(tokens_r['''input_ids''']       ,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)

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





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         for trim_offsets, add_prefix_space in itertools.product([True, False]       ,	repeat=2		):
             _UpperCAmelCase							:  Optional[Any]											=    self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname       ,	use_fast=A       ,	add_prefix_space=A       ,	trim_offsets=A		)

             _UpperCAmelCase							:  Dict											=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
             _UpperCAmelCase							:  Tuple											=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

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

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





     def  __lowerCAmelCase	(					self		)							->					int:
         # 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})'		):
                 _UpperCAmelCase							:  Tuple											=    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                 _UpperCAmelCase							:  List[str]											=    f'{text_of_1_token} {text_of_1_token}'

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

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

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

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

                 _UpperCAmelCase							:  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)),
                 # )

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

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

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







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)

_lowerCAmelCase    :List[str]     =					'▁'

_lowerCAmelCase    :Tuple     =					{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCAmelCase    :List[Any]     =					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_lowerCAmelCase    :Tuple     =					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A="<s>"       ,	A="</s>"       ,	A="</s>"       ,	A="<s>"       ,	A="<unk>"       ,	A="<pad>"       ,	A="<mask>"       ,	A = None       ,	**A       ,	)							->					None:
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         _UpperCAmelCase							:  List[Any]											=    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=A       ,	eos_token=A       ,	unk_token=A       ,	sep_token=A       ,	cls_token=A       ,	pad_token=A       ,	mask_token=A       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**A       ,	)

         _UpperCAmelCase							:  Any											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(A		)		)
         _UpperCAmelCase							:  List[Any]											=    vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         _UpperCAmelCase							:  List[str]											=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         _UpperCAmelCase							:  Any											=    1

         _UpperCAmelCase							:  Optional[Any]											=    len(self.sp_model		) + self.fairseq_offset
         _UpperCAmelCase							:  int											=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Tuple											=    self.__dict__.copy()
         _UpperCAmelCase							:  List[str]											=    None
         _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
         return state





     def __setstate__(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    d

         # for backward compatibility
         if not hasattr(self       ,	'''sp_model_kwargs'''		):
             _UpperCAmelCase							:  Optional[Any]											=    {}

         _UpperCAmelCase							:  Optional[int]											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase							:  Any											=    [self.cls_token_id]
         _UpperCAmelCase							:  Any											=    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					List[int]:

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A       ,	token_ids_a=A       ,	already_has_special_tokens=A		)

         if token_ids_a is None:
             return [1] + ([0] * len(A		)) + [1]
         return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		)) + [1]





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Dict											=    [self.sep_token_id]
         _UpperCAmelCase							:  List[str]											=    [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep		) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    {self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[str]:
         return self.sp_model.encode(A       ,	out_type=A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _UpperCAmelCase							:  Any											=    self.sp_model.PieceToId(A		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         _UpperCAmelCase							:  str											=    ''''''.join(A		).replace(A       ,	''' '''		).strip()
         return out_string





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

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

         return (out_vocab_file,)


