

import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__lowercase     : Tuple       =						get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowercase     : Optional[Any]       =						get_tests_dir('''fixtures/vocab.json''')
__lowercase     : Tuple       =						get_tests_dir('''fixtures''')



class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      int					=   ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		0





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""    )
      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		str        =		WavaVecaConfig()
          snake_case							:		int        =		AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""    )

          # save in new folder
          model_config.save_pretrained(lowerCamelCase__    )
          processor.save_pretrained(lowerCamelCase__    )

          snake_case							:		Optional[Any]        =		AutoProcessor.from_pretrained(lowerCamelCase__    )

      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with tempfile.TemporaryDirectory() as tmpdirname:
          # copy relevant files
          copyfile(lowerCamelCase__   ,os.path.join(lowerCamelCase__   ,lowerCamelCase__    )    )
          copyfile(lowerCamelCase__   ,os.path.join(lowerCamelCase__   ,"""vocab.json"""    )    )

          snake_case							:		Tuple        =		AutoProcessor.from_pretrained(lowerCamelCase__    )

      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		Any        =		WavaVecaFeatureExtractor()
          snake_case							:		Dict        =		AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""    )

          snake_case							:		int        =		WavaVecaProcessor(lowerCamelCase__   ,lowerCamelCase__    )

          # save in new folder
          processor.save_pretrained(lowerCamelCase__    )

          # drop `processor_class` in tokenizer
          with open(os.path.join(lowerCamelCase__   ,lowerCamelCase__    )   ,"""r"""    ) as f:
              snake_case							:		str        =		json.load(lowerCamelCase__    )
              config_dict.pop("""processor_class"""    )

          with open(os.path.join(lowerCamelCase__   ,lowerCamelCase__    )   ,"""w"""    ) as f:
              f.write(json.dumps(lowerCamelCase__    )    )

          snake_case							:		Dict        =		AutoProcessor.from_pretrained(lowerCamelCase__    )

      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		Dict        =		WavaVecaFeatureExtractor()
          snake_case							:		int        =		AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""    )

          snake_case							:		List[Any]        =		WavaVecaProcessor(lowerCamelCase__   ,lowerCamelCase__    )

          # save in new folder
          processor.save_pretrained(lowerCamelCase__    )

          # drop `processor_class` in feature extractor
          with open(os.path.join(lowerCamelCase__   ,lowerCamelCase__    )   ,"""r"""    ) as f:
              snake_case							:		List[str]        =		json.load(lowerCamelCase__    )
              config_dict.pop("""processor_class"""    )

          with open(os.path.join(lowerCamelCase__   ,lowerCamelCase__    )   ,"""w"""    ) as f:
              f.write(json.dumps(lowerCamelCase__    )    )

          snake_case							:		int        =		AutoProcessor.from_pretrained(lowerCamelCase__    )

      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		List[Any]        =		WavaVecaConfig(processor_class="""Wav2Vec2Processor"""    )
          model_config.save_pretrained(lowerCamelCase__    )
          # copy relevant files
          copyfile(lowerCamelCase__   ,os.path.join(lowerCamelCase__   ,"""vocab.json"""    )    )
          # create emtpy sample processor
          with open(os.path.join(lowerCamelCase__   ,lowerCamelCase__    )   ,"""w"""    ) as f:
              f.write("""{}"""    )

          snake_case							:		Union[str, Any]        =		AutoProcessor.from_pretrained(lowerCamelCase__    )

      self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(lowerCamelCase__    ):
          snake_case							:		Optional[Any]        =		AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""    )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(lowerCamelCase__    ):
          snake_case							:		Any        =		AutoProcessor.from_pretrained(
              """hf-internal-testing/test_dynamic_processor"""   ,trust_remote_code=lowerCamelCase__    )

      snake_case							:		str        =		AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""   ,trust_remote_code=lowerCamelCase__    )
      self.assertTrue(processor.special_attribute_present    )
      self.assertEqual(processor.__class__.__name__   ,"""NewProcessor"""    )

      snake_case							:		Dict        =		processor.feature_extractor
      self.assertTrue(feature_extractor.special_attribute_present    )
      self.assertEqual(feature_extractor.__class__.__name__   ,"""NewFeatureExtractor"""    )

      snake_case							:		int        =		processor.tokenizer
      self.assertTrue(tokenizer.special_attribute_present    )
      if is_tokenizers_available():
          self.assertEqual(tokenizer.__class__.__name__   ,"""NewTokenizerFast"""    )

          # Test we can also load the slow version
          snake_case							:		str        =		AutoProcessor.from_pretrained(
              """hf-internal-testing/test_dynamic_processor"""   ,trust_remote_code=lowerCamelCase__   ,use_fast=lowerCamelCase__    )
          snake_case							:		Tuple        =		new_processor.tokenizer
          self.assertTrue(new_tokenizer.special_attribute_present    )
          self.assertEqual(new_tokenizer.__class__.__name__   ,"""NewTokenizer"""    )
      else:
          self.assertEqual(tokenizer.__class__.__name__   ,"""NewTokenizer"""    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      try:
          AutoConfig.register("""custom"""   ,lowerCamelCase__    )
          AutoFeatureExtractor.register(lowerCamelCase__   ,lowerCamelCase__    )
          AutoTokenizer.register(lowerCamelCase__   ,slow_tokenizer_class=lowerCamelCase__    )
          AutoProcessor.register(lowerCamelCase__   ,lowerCamelCase__    )
          # Trying to register something existing in the Transformers library will raise an error
          with self.assertRaises(lowerCamelCase__    ):
              AutoProcessor.register(lowerCamelCase__   ,lowerCamelCase__    )

          # Now that the config is registered, it can be used as any other config with the auto-API
          snake_case							:		Union[str, Any]        =		CustomFeatureExtractor.from_pretrained(lowerCamelCase__    )

          with tempfile.TemporaryDirectory() as tmp_dir:
              snake_case							:		Any        =		os.path.join(lowerCamelCase__   ,"""vocab.txt"""    )
              with open(lowerCamelCase__   ,"""w"""   ,encoding="""utf-8"""    ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]    )    )
              snake_case							:		Tuple        =		CustomTokenizer(lowerCamelCase__    )

          snake_case							:		Dict        =		CustomProcessor(lowerCamelCase__   ,lowerCamelCase__    )

          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(lowerCamelCase__    )
              snake_case							:		str        =		AutoProcessor.from_pretrained(lowerCamelCase__    )
              self.assertIsInstance(lowerCamelCase__   ,lowerCamelCase__    )

      finally:
          if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
          if CustomConfig in TOKENIZER_MAPPING._extra_content:
              del TOKENIZER_MAPPING._extra_content[CustomConfig]
          if CustomConfig in PROCESSOR_MAPPING._extra_content:
              del PROCESSOR_MAPPING._extra_content[CustomConfig]





  def    snake_case_       (  self    ):

      '''simple docstring'''



      class 						_A     (				__UpperCAmelCase				):





        '''simple docstring'''



        __lowerCamelCase			:      int					=   False







      class 						_A     (				__UpperCAmelCase				):





        '''simple docstring'''



        __lowerCamelCase			:      Union[str, Any]					=   False







      class 						_A     (				__UpperCAmelCase				):





        '''simple docstring'''



        __lowerCamelCase			:      Union[str, Any]					=   '''AutoFeatureExtractor'''
        __lowerCamelCase			:      List[str]					=   '''AutoTokenizer'''
        __lowerCamelCase			:      Optional[Any]					=   False

      try:
          AutoConfig.register("""custom"""   ,lowerCamelCase__    )
          AutoFeatureExtractor.register(lowerCamelCase__   ,lowerCamelCase__    )
          AutoTokenizer.register(lowerCamelCase__   ,slow_tokenizer_class=lowerCamelCase__    )
          AutoProcessor.register(lowerCamelCase__   ,lowerCamelCase__    )
          # If remote code is not set, the default is to use local classes.
          snake_case							:		Union[str, Any]        =		AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""    )
          self.assertEqual(processor.__class__.__name__   ,"""NewProcessor"""    )
          self.assertFalse(processor.special_attribute_present    )
          self.assertFalse(processor.feature_extractor.special_attribute_present    )
          self.assertFalse(processor.tokenizer.special_attribute_present    )

          # If remote code is disabled, we load the local ones.
          snake_case							:		Tuple        =		AutoProcessor.from_pretrained(
              """hf-internal-testing/test_dynamic_processor"""   ,trust_remote_code=lowerCamelCase__    )
          self.assertEqual(processor.__class__.__name__   ,"""NewProcessor"""    )
          self.assertFalse(processor.special_attribute_present    )
          self.assertFalse(processor.feature_extractor.special_attribute_present    )
          self.assertFalse(processor.tokenizer.special_attribute_present    )

          # If remote is enabled, we load from the Hub.
          snake_case							:		Any        =		AutoProcessor.from_pretrained(
              """hf-internal-testing/test_dynamic_processor"""   ,trust_remote_code=lowerCamelCase__    )
          self.assertEqual(processor.__class__.__name__   ,"""NewProcessor"""    )
          self.assertTrue(processor.special_attribute_present    )
          self.assertTrue(processor.feature_extractor.special_attribute_present    )
          self.assertTrue(processor.tokenizer.special_attribute_present    )

      finally:
          if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
          if CustomConfig in TOKENIZER_MAPPING._extra_content:
              del TOKENIZER_MAPPING._extra_content[CustomConfig]
          if CustomConfig in PROCESSOR_MAPPING._extra_content:
              del PROCESSOR_MAPPING._extra_content[CustomConfig]





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )
      self.assertEqual(processor.__class__.__name__   ,"""BertTokenizerFast"""    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""    )
      self.assertEqual(processor.__class__.__name__   ,"""ConvNextImageProcessor"""    )







@is_staging_test
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      int					=   ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']





  @classmethod
  def    snake_case_       (  cls    ):

      '''simple docstring'''
      snake_case							:		str        =		TOKEN
      HfFolder.save_token(lowerCamelCase__    )





  @classmethod
  def    snake_case_       (  cls    ):

      '''simple docstring'''
      try:
          delete_repo(token=cls._token   ,repo_id="""test-processor"""    )
      except HTTPError:
          pass

      try:
          delete_repo(token=cls._token   ,repo_id="""valid_org/test-processor-org"""    )
      except HTTPError:
          pass

      try:
          delete_repo(token=cls._token   ,repo_id="""test-dynamic-processor"""    )
      except HTTPError:
          pass





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		WavaVecaProcessor.from_pretrained(lowerCamelCase__    )
      with tempfile.TemporaryDirectory() as tmp_dir:
          processor.save_pretrained(
              os.path.join(lowerCamelCase__   ,"""test-processor"""    )   ,push_to_hub=lowerCamelCase__   ,use_auth_token=self._token    )

          snake_case							:		Optional[Any]        =		WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""    )
          for k, v in processor.feature_extractor.__dict__.items():
              self.assertEqual(lowerCamelCase__   ,getattr(new_processor.feature_extractor   ,lowerCamelCase__    )    )
          self.assertDictEqual(new_processor.tokenizer.get_vocab()   ,processor.tokenizer.get_vocab()    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		WavaVecaProcessor.from_pretrained(lowerCamelCase__    )

      with tempfile.TemporaryDirectory() as tmp_dir:
          processor.save_pretrained(
              os.path.join(lowerCamelCase__   ,"""test-processor-org"""    )   ,push_to_hub=lowerCamelCase__   ,use_auth_token=self._token   ,organization="""valid_org"""   ,)

          snake_case							:		Optional[Any]        =		WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""    )
          for k, v in processor.feature_extractor.__dict__.items():
              self.assertEqual(lowerCamelCase__   ,getattr(new_processor.feature_extractor   ,lowerCamelCase__    )    )
          self.assertDictEqual(new_processor.tokenizer.get_vocab()   ,processor.tokenizer.get_vocab()    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      CustomFeatureExtractor.register_for_auto_class()
      CustomTokenizer.register_for_auto_class()
      CustomProcessor.register_for_auto_class()

      snake_case							:		List[str]        =		CustomFeatureExtractor.from_pretrained(lowerCamelCase__    )

      with tempfile.TemporaryDirectory() as tmp_dir:
          snake_case							:		Tuple        =		os.path.join(lowerCamelCase__   ,"""vocab.txt"""    )
          with open(lowerCamelCase__   ,"""w"""   ,encoding="""utf-8"""    ) as vocab_writer:
              vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]    )    )
          snake_case							:		Optional[int]        =		CustomTokenizer(lowerCamelCase__    )

      snake_case							:		List[Any]        =		CustomProcessor(lowerCamelCase__   ,lowerCamelCase__    )

      with tempfile.TemporaryDirectory() as tmp_dir:
          create_repo(F"""{USER}/test-dynamic-processor"""   ,token=self._token    )
          snake_case							:		Tuple        =		Repository(lowerCamelCase__   ,clone_from=F"""{USER}/test-dynamic-processor"""   ,token=self._token    )
          processor.save_pretrained(lowerCamelCase__    )

          # This has added the proper auto_map field to the feature extractor config
          self.assertDictEqual(
              processor.feature_extractor.auto_map   ,{
                  """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
                  """AutoProcessor""": """custom_processing.CustomProcessor""",
              }   ,)

          # This has added the proper auto_map field to the tokenizer config
          with open(os.path.join(lowerCamelCase__   ,"""tokenizer_config.json"""    )    ) as f:
              snake_case							:		List[Any]        =		json.load(lowerCamelCase__    )
          self.assertDictEqual(
              tokenizer_config["""auto_map"""]   ,{
                  """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
                  """AutoProcessor""": """custom_processing.CustomProcessor""",
              }   ,)

          # The code has been copied from fixtures
          self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__   ,"""custom_feature_extraction.py"""    )    )    )
          self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__   ,"""custom_tokenization.py"""    )    )    )
          self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__   ,"""custom_processing.py"""    )    )    )

          repo.push_to_hub()

      snake_case							:		int        =		AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""   ,trust_remote_code=lowerCamelCase__    )
      # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
      self.assertEqual(new_processor.__class__.__name__   ,"""CustomProcessor"""    )







"""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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


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

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							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 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file ,     lowerCamelCase__						)
    elif not os.path.isfile(self.vocab_file						):
     with open(lowerCamelCase__ ,     """wb"""						) as fi:
      lowercase__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)