

'''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:
					_lowerCamelCase      					=     None


_lowerCamelCase      					=     logging.get_logger(__name__)


_lowerCamelCase      					=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase      					=     {
    '''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''',
    },
}

_lowerCamelCase      					=     {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
_lowerCamelCase      					=     ['''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 					_snake_case		(_lowercase):
			__A  :	List[Any]        =VOCAB_FILES_NAMES
			__A  :	Dict        =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A  :	Tuple        =PRETRAINED_VOCAB_FILES_MAP
			__A  :	Optional[Any]        =["input_ids", "attention_mask"]
			__A  :	Optional[int]        =MBartTokenizer

			__A  :	Any        =[]
			__A  :	str        =[]






			def __init__(  self			,_snake_case=None			,_snake_case=None			,_snake_case="<s>"			,_snake_case="</s>"			,_snake_case="</s>"			,_snake_case="<s>"			,_snake_case="<unk>"			,_snake_case="<pad>"			,_snake_case="<mask>"			,_snake_case=None			,_snake_case=None			,_snake_case=None			,**_snake_case			,):
				UpperCAmelCase_     :      Tuple						=  AddedToken(_snake_case			,lstrip=_snake_case			,rstrip=_snake_case     ) if isinstance(_snake_case			,_snake_case     ) else mask_token

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

				UpperCAmelCase_     :      Any						=  vocab_file
				UpperCAmelCase_     :      Any						=  False if not self.vocab_file else True

				UpperCAmelCase_     :      List[Any]						=  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}     )
				UpperCAmelCase_     :      Optional[int]						=  {
				    lang_code: self.convert_tokens_to_ids(_snake_case     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				UpperCAmelCase_     :      str						=  src_lang if src_lang is not None else "en_XX"
				UpperCAmelCase_     :      List[str]						=  self.convert_tokens_to_ids(self._src_lang     )
				UpperCAmelCase_     :      int						=  tgt_lang
				self.set_src_lang_special_tokens(self._src_lang     )






			@property
			def 		UpperCamelCase__							(  self     ):
				return self._src_lang






			@src_lang.setter
			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  new_src_lang
				self.set_src_lang_special_tokens(self._src_lang     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				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			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Optional[int]						=  [self.sep_token_id]
				UpperCAmelCase_     :      int						=  [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			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,**_snake_case     ):
				if src_lang is None or tgt_lang is None:
					raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
				UpperCAmelCase_     :      List[str]						=  src_lang
				UpperCAmelCase_     :      int						=  self(_snake_case			,add_special_tokens=_snake_case			,return_tensors=_snake_case			,**_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.convert_tokens_to_ids(_snake_case     )
				UpperCAmelCase_     :      str						=  tgt_lang_id
				return inputs






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = "en_XX"			,_snake_case = None			,_snake_case = "ro_RO"			,**_snake_case			,):
				UpperCAmelCase_     :      Any						=  src_lang
				UpperCAmelCase_     :      List[Any]						=  tgt_lang
				return super().prepare_seqaseq_batch(_snake_case			,_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				return self.set_src_lang_special_tokens(self.src_lang     )






			def 		UpperCamelCase__							(  self     ):
				return self.set_tgt_lang_special_tokens(self.tgt_lang     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Any						=  self.convert_tokens_to_ids(_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  []
				UpperCAmelCase_     :      Optional[Any]						=  [self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     :      Tuple						=  self.convert_ids_to_tokens(self.prefix_tokens     )
				UpperCAmelCase_     :      Optional[int]						=  self.convert_ids_to_tokens(self.suffix_tokens     )

				UpperCAmelCase_     :      Dict						=  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			,_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.convert_tokens_to_ids(_snake_case     )
				UpperCAmelCase_     :      str						=  []
				UpperCAmelCase_     :      int						=  [self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     :      Tuple						=  self.convert_ids_to_tokens(self.prefix_tokens     )
				UpperCAmelCase_     :      Dict						=  self.convert_ids_to_tokens(self.suffix_tokens     )

				UpperCAmelCase_     :      List[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			,_snake_case			,_snake_case = None     ):
				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(_snake_case     ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''     )
					return
				UpperCAmelCase_     :      Optional[int]						=  os.path.join(
				    _snake_case			,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

				return (out_vocab_file,)






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




