



"""simple docstring"""


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  List[Any] ):






				'''simple docstring'''



				assert isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''    ,   [False, True] )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Tuple    ,   _SCREAMING_SNAKE_CASE      :  str    ,   _SCREAMING_SNAKE_CASE      :  Any ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE    ,   keep_in_memory=__SCREAMING_SNAKE_CASE ).read()
				_check_json_dataset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize(
    '''features'''    ,   [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,   )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Union[str, Any]    ,   _SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  Optional[Any] ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				_UpperCAmelCase							=			features.copy() if features else default_expected_features
				_UpperCAmelCase							=			(
				    Features({feature: Value(__SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   features=__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				_check_json_dataset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize(
    '''features'''    ,   [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]    ,   )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  int    ,   _SCREAMING_SNAKE_CASE      :  Union[str, Any]    ,   _SCREAMING_SNAKE_CASE      :  List[Any] ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
				_UpperCAmelCase							=			features.copy() if features else default_expected_features
				_UpperCAmelCase							=			(
				    Features({feature: Value(__SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   features=__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				assert isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_3", "col_1", "col_2"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  int    ,   _SCREAMING_SNAKE_CASE      :  Tuple ):






				'''simple docstring'''



				_UpperCAmelCase							=			{'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
				_UpperCAmelCase							=			features.copy()
				_UpperCAmelCase							=			(
				    Features({feature: Value(__SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   features=__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				assert isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_2", "col_3", "col_1"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''split'''    ,   [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  Optional[int] ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE    ,   split=__SCREAMING_SNAKE_CASE ).read()
				_check_json_dataset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''    ,   [str, list] )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  List[Any] ):






				'''simple docstring'''



				if issubclass(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE ):
								_UpperCAmelCase							=			jsonl_path
				elif issubclass(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE ):
								_UpperCAmelCase							=			[jsonl_path]
				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				_check_json_dataset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  str    ,   _SCREAMING_SNAKE_CASE      :  List[Any]=("train",) ):






				'''simple docstring'''



				assert isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				for split in splits:
								_UpperCAmelCase							=			dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''    ,   [False, True] )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  str    ,   _SCREAMING_SNAKE_CASE      :  Optional[Any]    ,   _SCREAMING_SNAKE_CASE      :  Tuple ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_UpperCAmelCase							=			JsonDatasetReader({'''train''': jsonl_path}    ,   cache_dir=__SCREAMING_SNAKE_CASE    ,   keep_in_memory=__SCREAMING_SNAKE_CASE ).read()
				_check_json_datasetdict(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize(
    '''features'''    ,   [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,   )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				_UpperCAmelCase							=			features.copy() if features else default_expected_features
				_UpperCAmelCase							=			(
				    Features({feature: Value(__SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_UpperCAmelCase							=			JsonDatasetReader({'''train''': jsonl_path}    ,   features=__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				_check_json_datasetdict(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize('''split'''    ,   [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Tuple    ,   _SCREAMING_SNAKE_CASE      :  Optional[Any]    ,   _SCREAMING_SNAKE_CASE      :  Optional[int] ):






				'''simple docstring'''



				if split:
								_UpperCAmelCase							=			{split: jsonl_path}
				else:
								_UpperCAmelCase							=			'train'
								_UpperCAmelCase							=			{'train': jsonl_path, 'test': jsonl_path}
				_UpperCAmelCase							=			tmp_path / 'cache'
				_UpperCAmelCase							=			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				_UpperCAmelCase							=			JsonDatasetReader(__SCREAMING_SNAKE_CASE    ,   cache_dir=__SCREAMING_SNAKE_CASE ).read()
				_check_json_datasetdict(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   splits=list(path.keys() ) )
				assert all(dataset[split].split == split for split in path.keys() )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  str ):






				'''simple docstring'''



				return json.load(__SCREAMING_SNAKE_CASE )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  int ):






				'''simple docstring'''



				return [json.loads(__SCREAMING_SNAKE_CASE ) for line in buffer]
class 	_a       :
			"""simple docstring"""



			@pytest.mark.parametrize('''lines, load_json_function''' ,      [(True, load_json_lines), (False, load_json)]      )
			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			Any      )->List[str]:


							with io.BytesIO() as buffer:
											JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      lines=__UpperCamelCase      ).write()
											buffer.seek(0      )
											_UpperCAmelCase							=			load_json_function(__UpperCamelCase      )
							assert isinstance(__UpperCamelCase ,      __UpperCamelCase      )
							assert isinstance(exported_content[0] ,      __UpperCamelCase      )
							assert len(__UpperCamelCase      ) == 1_0



			@pytest.mark.parametrize(
			    '''orient, container, keys, len_at''' ,      [
			        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
			        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
			        ('''index''', dict, set('''0123456789'''      ), None),
			        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
			        ('''values''', list, None, None),
			        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
			    ] ,      )
			def        lowercase__  (						self   :			Union[str, Any] ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			int      )->Optional[Any]:


							with io.BytesIO() as buffer:
											JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      lines=__UpperCamelCase ,      orient=__UpperCamelCase      ).write()
											buffer.seek(0      )
											_UpperCAmelCase							=			load_json(__UpperCamelCase      )
							assert isinstance(__UpperCamelCase ,      __UpperCamelCase      )
							if keys:
											if container is dict:
															assert exported_content.keys() == keys
											else:
															assert exported_content[0].keys() == keys
							else:
											assert not hasattr(__UpperCamelCase ,      '''keys'''      ) and not hasattr(exported_content[0] ,      '''keys'''      )
							if len_at:
											assert len(exported_content[len_at]      ) == 1_0
							else:
											assert len(__UpperCamelCase      ) == 1_0



			@pytest.mark.parametrize('''lines, load_json_function''' ,      [(True, load_json_lines), (False, load_json)]      )
			def        lowercase__  (						self   :			Optional[int] ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			Optional[int]      )->Dict:


							with io.BytesIO() as buffer:
											JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      lines=__UpperCamelCase ,      num_proc=2      ).write()
											buffer.seek(0      )
											_UpperCAmelCase							=			load_json_function(__UpperCamelCase      )
							assert isinstance(__UpperCamelCase ,      __UpperCamelCase      )
							assert isinstance(exported_content[0] ,      __UpperCamelCase      )
							assert len(__UpperCamelCase      ) == 1_0



			@pytest.mark.parametrize(
			    '''orient, container, keys, len_at''' ,      [
			        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
			        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
			        ('''index''', dict, set('''0123456789'''      ), None),
			        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
			        ('''values''', list, None, None),
			        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
			    ] ,      )
			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			List[Any]      )->Any:


							with io.BytesIO() as buffer:
											JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      lines=__UpperCamelCase ,      orient=__UpperCamelCase ,      num_proc=2      ).write()
											buffer.seek(0      )
											_UpperCAmelCase							=			load_json(__UpperCamelCase      )
							assert isinstance(__UpperCamelCase ,      __UpperCamelCase      )
							if keys:
											if container is dict:
															assert exported_content.keys() == keys
											else:
															assert exported_content[0].keys() == keys
							else:
											assert not hasattr(__UpperCamelCase ,      '''keys'''      ) and not hasattr(exported_content[0] ,      '''keys'''      )
							if len_at:
											assert len(exported_content[len_at]      ) == 1_0
							else:
											assert len(__UpperCamelCase      ) == 1_0



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			Dict      )->str:


							with pytest.raises(__UpperCamelCase      ):
											with io.BytesIO() as buffer:
															JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      num_proc=0      )



			@pytest.mark.parametrize('''compression, extension''' ,      [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]      )
			def        lowercase__  (						self   :			Any ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			int      )->int:
							_UpperCAmelCase							=			tmp_path_factory.mktemp('''data'''      ) / F'test.json.{extension}'
							_UpperCAmelCase							=			str(shared_datadir / F'test_file.json.{extension}'      )
							JsonDatasetWriter(__UpperCamelCase ,      __UpperCamelCase ,      compression=__UpperCamelCase      ).write()

							with fsspec.open(__UpperCamelCase ,      '''rb''' ,      compression='''infer'''      ) as f:
											_UpperCAmelCase							=			f.read()
							with fsspec.open(__UpperCamelCase ,      '''rb''' ,      compression='''infer'''      ) as f:
											_UpperCAmelCase							=			f.read()
							assert exported_content == original_content




"""simple docstring"""







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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       __SCREAMING_SNAKE_CASE    	=None


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    	={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE    	={
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE    	={
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE    	=["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  ['input_ids', 'attention_mask']
   lowercase							=  MBartTokenizer

   lowercase							=  []
   lowercase							=  []
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="<unk>"     ,__UpperCamelCase="<pad>"     ,__UpperCamelCase="<mask>"     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				str													=	AddedToken(__UpperCamelCase     ,lstrip=__UpperCamelCase     ,rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else mask_token

        super().__init__(
            vocab_file=__UpperCamelCase     ,tokenizer_file=__UpperCamelCase     ,bos_token=__UpperCamelCase     ,eos_token=__UpperCamelCase     ,sep_token=__UpperCamelCase     ,cls_token=__UpperCamelCase     ,unk_token=__UpperCamelCase     ,pad_token=__UpperCamelCase     ,mask_token=__UpperCamelCase     ,src_lang=__UpperCamelCase     ,tgt_lang=__UpperCamelCase     ,additional_special_tokens=__UpperCamelCase     ,**__UpperCamelCase     ,)

        lowercase_	:				str													=	vocab_file
        lowercase_	:				Optional[Any]													=	False if not self.vocab_file else True

        lowercase_	:				List[str]													=	FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
        lowercase_	:				List[Any]													=	{
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_	:				Dict													=	src_lang if src_lang is not None else 'en_XX'
        lowercase_	:				Union[str, Any]													=	self.convert_tokens_to_ids(self._src_lang			)
        lowercase_	:				int													=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	[self.sep_token_id]
        lowercase_	:				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]
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
        lowercase_	:				Dict													=	src_lang
        lowercase_	:				List[Any]													=	self(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Dict													=	tgt_lang_id
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = "en_XX"     ,__UpperCamelCase = None     ,__UpperCamelCase = "ro_RO"     ,**__UpperCamelCase     ,)	->      BatchEncoding:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	src_lang
        lowercase_	:				List[Any]													=	tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Any													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	[]
        lowercase_	:				int													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				Any													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				int													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				Union[str, Any]													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				str													=	[]
        lowercase_	:				Dict													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				str													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				List[Any]													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				int													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Tuple[str]:


        '''simple docstring'''


        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'			)

        if not os.path.isdir(__UpperCamelCase			):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''			)
             return
        lowercase_	:				Tuple													=	os.path.join(
            __UpperCamelCase     ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(__UpperCamelCase			):
             copyfile(self.vocab_file     ,__UpperCamelCase			)

        return (out_vocab_file,)

