
'''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:
						UpperCAmelCase    					=   None


UpperCAmelCase    					=   logging.get_logger(__name__)


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

UpperCAmelCase    					=   {
    "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",
    },
}

UpperCAmelCase    					=   {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
UpperCAmelCase    					=   ["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 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = MBartTokenizer

				_snake_case		    = []
				_snake_case		    = []




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

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

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

										snake_case       :     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}      )
										snake_case       :     Any =    {
										    lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										snake_case       :     Dict =    src_lang if src_lang is not None else '''en_XX'''
										snake_case       :     str =    self.convert_tokens_to_ids(self._src_lang      )
										snake_case       :     List[Any] =    tgt_lang
										self.set_src_lang_special_tokens(self._src_lang      )




				@property
				def 	__snake_case							(							self	:				Tuple      ):
										return self._src_lang




				@src_lang.setter
				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     List[str] =    new_src_lang
										self.set_src_lang_special_tokens(self._src_lang      )




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




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str = "en_XX",    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    SCREAMING_SNAKE_CASE_	:				str = "ro_RO",    **SCREAMING_SNAKE_CASE_	:				Dict,    ):
										snake_case       :     List[str] =    src_lang
										snake_case       :     int =    tgt_lang
										return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										return self.set_src_lang_special_tokens(self.src_lang      )




				def 	__snake_case							(							self	:				Dict      ):
										return self.set_tgt_lang_special_tokens(self.tgt_lang      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     int =    self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    []
										snake_case       :     Tuple =    [self.eos_token_id, self.cur_lang_code]

										snake_case       :     Optional[Any] =    self.convert_ids_to_tokens(self.prefix_tokens      )
										snake_case       :     Optional[Any] =    self.convert_ids_to_tokens(self.suffix_tokens      )

										snake_case       :     List[str] =    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 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     int =    self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    []
										snake_case       :     Union[str, Any] =    [self.eos_token_id, self.cur_lang_code]

										snake_case       :     Any =    self.convert_ids_to_tokens(self.prefix_tokens      )
										snake_case       :     Optional[Any] =    self.convert_ids_to_tokens(self.suffix_tokens      )

										snake_case       :     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 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = 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(SCREAMING_SNAKE_CASE_      ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
																return
										snake_case       :     Any =    os.path.join(
										    SCREAMING_SNAKE_CASE_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

										return (out_vocab_file,)



'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										warnings.warn(
										    '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
										    ''' Please use LayoutLMv2ImageProcessor instead.''',    SCREAMING_SNAKE_CASE_,    )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )

