


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

import sentencepiece as spm

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


lowerCAmelCase__			:      List[str]								=logging.get_logger(__name__)

lowerCAmelCase__			:      Dict								='▁'

lowerCAmelCase__			:      List[str]								={'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase__			:      Optional[int]								={
    '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'
        ),
    }
}

lowerCAmelCase__			:      Dict								={
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__			:      List[Any]								=['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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						VOCAB_FILES_NAMES
							_UpperCAmelCase      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      =						PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      =						["""input_ids""", """attention_mask"""]

							_UpperCAmelCase      =						[]
							_UpperCAmelCase      =						[]






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__="<s>"			,						lowerCAmelCase__="</s>"			,						lowerCAmelCase__="</s>"			,						lowerCAmelCase__="<s>"			,						lowerCAmelCase__="<unk>"			,						lowerCAmelCase__="<pad>"			,						lowerCAmelCase__="<mask>"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else mask_token

										SCREAMING_SNAKE_CASE_		:     Any					=					{} 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__			,						tokenizer_file=lowerCAmelCase__			,						src_lang=lowerCAmelCase__			,						tgt_lang=lowerCAmelCase__			,						additional_special_tokens=lowerCAmelCase__			,						sp_model_kwargs=self.sp_model_kwargs			,						**lowerCAmelCase__			,						)

										SCREAMING_SNAKE_CASE_		:     Tuple					=					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(str(lowerCAmelCase__       )       )
										SCREAMING_SNAKE_CASE_		:     Union[str, 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
										SCREAMING_SNAKE_CASE_		:     Any					=					{'<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
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					1

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(self.sp_model       )
										SCREAMING_SNAKE_CASE_		:     Any					=					{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__       )
										}
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{v: k for k, v in self.lang_code_to_id.items()}
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
										SCREAMING_SNAKE_CASE_		:     Any					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
										SCREAMING_SNAKE_CASE_		:     List[str]					=					list(self.lang_code_to_id.keys()       )

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

										SCREAMING_SNAKE_CASE_		:     Dict					=					src_lang if src_lang is not None else 'en_XX'
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.lang_code_to_id[self._src_lang]
										SCREAMING_SNAKE_CASE_		:     Dict					=					tgt_lang
										self.set_src_lang_special_tokens(self._src_lang       )






							def __getstate__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.__dict__.copy()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
										SCREAMING_SNAKE_CASE_		:     Any					=					self.sp_model.serialized_model_proto()
										return state






							def __setstate__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					d

										# for backward compatibility
										if not hasattr(self			,						'sp_model_kwargs'       ):
													SCREAMING_SNAKE_CASE_		:     int					=					{}

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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self._src_lang






							@src_lang.setter
							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[1] * len(self.prefix_tokens       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[1] * len(self.suffix_tokens       )
										if token_ids_a is None:
													return prefix_ones + ([0] * len(lowerCAmelCase__       )) + suffix_ones
										return prefix_ones + ([0] * len(lowerCAmelCase__       )) + ([0] * len(lowerCAmelCase__       )) + suffix_ones






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""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			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					[self.sep_token_id]
										SCREAMING_SNAKE_CASE_		:     Dict					=					[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			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""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'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					src_lang
										SCREAMING_SNAKE_CASE_		:     Any					=					self(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.convert_tokens_to_ids(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tgt_lang_id
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					{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__       ):





										"""simple docstring"""




										return self.sp_model.encode(lowerCAmelCase__			,						out_type=lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.sp_model.PieceToId(lowerCAmelCase__       )

										# 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 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										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__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					''.join(lowerCAmelCase__       ).replace(lowerCAmelCase__			,						' '       ).strip()
										return out_string






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										if not os.path.isdir(lowerCAmelCase__       ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										SCREAMING_SNAKE_CASE_		:     Union[str, 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:
																SCREAMING_SNAKE_CASE_		:     Dict					=					self.sp_model.serialized_model_proto()
																fi.write(lowerCAmelCase__       )

										return (out_vocab_file,)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = "en_XX"			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = "ro_RO"			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					src_lang
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tgt_lang
										return super().prepare_seqaseq_batch(lowerCAmelCase__			,						lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.set_src_lang_special_tokens(self.src_lang       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.set_tgt_lang_special_tokens(self.tgt_lang       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.lang_code_to_id[src_lang]
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.eos_token_id, self.cur_lang_code]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.lang_code_to_id[lang]
										SCREAMING_SNAKE_CASE_		:     Any					=					[]
										SCREAMING_SNAKE_CASE_		:     int					=					[self.eos_token_id, self.cur_lang_code]









from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
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    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
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    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
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    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
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    mega,
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    vision_encoder_decoder,
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    vit,
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    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
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    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)





