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


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

__UpperCamelCase		:       List[str]									=     '''▁'''

__UpperCamelCase		:       Optional[Any]									=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCamelCase		:       Any									=     {
    '''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'''
        ),
    }
}

__UpperCamelCase		:       Optional[int]									=     {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__UpperCamelCase		:       Optional[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__						(   __lowerCamelCase):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_     =		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		["input_ids", "attention_mask"]

						UpperCamelCase_     =		[]
						UpperCamelCase_     =		[]


						def __init__(       self	:				Optional[int]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Any="<s>"    ,		UpperCamelCase__	:				List[str]="</s>"    ,		UpperCamelCase__	:				Dict="</s>"    ,		UpperCamelCase__	:				str="<s>"    ,		UpperCamelCase__	:				Optional[int]="<unk>"    ,		UpperCamelCase__	:				str="<pad>"    ,		UpperCamelCase__	:				Optional[int]="<mask>"    ,		UpperCamelCase__	:				List[str]=None    ,		UpperCamelCase__	:				Optional[int]=None    ,		UpperCamelCase__	:				Optional[Any]=None    ,		UpperCamelCase__	:				Optional[Dict[str, Any]] = None    ,		UpperCamelCase__	:				Dict=None    ,		**UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AddedToken(__A    ,		lstrip=__A    ,		rstrip=__A						) if isinstance(__A    ,		__A						) else mask_token

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

												super().__init__(
												    bos_token=__A    ,		eos_token=__A    ,		unk_token=__A    ,		sep_token=__A    ,		cls_token=__A    ,		pad_token=__A    ,		mask_token=__A    ,		tokenizer_file=__A    ,		src_lang=__A    ,		tgt_lang=__A    ,		additional_special_tokens=__A    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**__A    ,		)

												SCREAMING_SNAKE_CASE							:    List[str]							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(str(__A						)						)
												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							:    List[str]							=							{'''<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							:    Tuple							=							1

												SCREAMING_SNAKE_CASE							:    str							=							len(self.sp_model						)
												SCREAMING_SNAKE_CASE							:    str							=							{
												    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A						)
												}
												SCREAMING_SNAKE_CASE							:    Any							=							{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							:    Optional[Any]							=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												SCREAMING_SNAKE_CASE							:    Tuple							=							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							:    int							=							src_lang if src_lang is not None else '''en_XX'''
												SCREAMING_SNAKE_CASE							:    List[str]							=							self.lang_code_to_id[self._src_lang]
												SCREAMING_SNAKE_CASE							:    str							=							tgt_lang
												self.set_src_lang_special_tokens(self._src_lang						)


						def __getstate__(       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.__dict__.copy()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							None
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.sp_model.serialized_model_proto()
												return state


						def __setstate__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							d

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

												SCREAMING_SNAKE_CASE							:    int							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												return self._src_lang


						@src_lang.setter
						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None    ,		UpperCamelCase__	:				bool = False						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							[1] * len(self.prefix_tokens						)
												SCREAMING_SNAKE_CASE							:    str							=							[1] * len(self.suffix_tokens						)
												if token_ids_a is None:
																		return prefix_ones + ([0] * len(__A						)) + suffix_ones
												return prefix_ones + ([0] * len(__A						)) + ([0] * len(__A						)) + suffix_ones


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = 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        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							[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        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[str]    ,		UpperCamelCase__	:				Optional[str]    ,		**UpperCamelCase__	:				List[str]						):





												'''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							:    List[Any]							=							src_lang
												SCREAMING_SNAKE_CASE							:    str							=							self(__A    ,		add_special_tokens=__A    ,		return_tensors=__A    ,		**__A						)
												SCREAMING_SNAKE_CASE							:    Any							=							self.convert_tokens_to_ids(__A						)
												SCREAMING_SNAKE_CASE							:    Dict							=							tgt_lang_id
												return inputs


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							{self.convert_ids_to_tokens(__A						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				str						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.sp_model.PieceToId(__A						)

												# 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        __A       (       self	:				Dict    ,		UpperCamelCase__	:				List[str]						):





												'''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        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''.join(__A						).replace(__A    ,		''' '''						).strip()
												return out_string


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[str] = None						):





												'''simple docstring'''





												if not os.path.isdir(__A						):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																		return
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							os.path.join(
												    __A    ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file    ,		__A						)
												elif not os.path.isfile(self.vocab_file						):
																		with open(__A    ,		'''wb'''						) as fi:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.sp_model.serialized_model_proto()
																								fi.write(__A						)

												return (out_vocab_file,)


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				List[str]    ,		UpperCamelCase__	:				str = "en_XX"    ,		UpperCamelCase__	:				Optional[List[str]] = None    ,		UpperCamelCase__	:				str = "ro_RO"    ,		**UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							src_lang
												SCREAMING_SNAKE_CASE							:    str							=							tgt_lang
												return super().prepare_seqaseq_batch(__A    ,		__A    ,		**__A						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return self.set_src_lang_special_tokens(self.src_lang						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





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



						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.lang_code_to_id[lang]
												SCREAMING_SNAKE_CASE							:    int							=							[]
												SCREAMING_SNAKE_CASE							:    int							=							[self.eos_token_id, self.cur_lang_code]












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[str] ={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Any =[
        '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''BigBirdPegasusForCausalLM''',
        '''BigBirdPegasusForConditionalGeneration''',
        '''BigBirdPegasusForQuestionAnswering''',
        '''BigBirdPegasusForSequenceClassification''',
        '''BigBirdPegasusModel''',
        '''BigBirdPegasusPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

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