






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


__A      =				logging.get_logger(__name__)

__A      =				'▁'

__A      =				{'vocab_file': 'sentencepiece.bpe.model'}

__A      =				{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__A      =				{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
__A      =				['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', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

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

							A_						  =					[]
							A_						  =					[]

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

												_A								= {} if sp_model_kwargs is None else sp_model_kwargs

												_A								= kwargs.get('''additional_special_tokens'''		,     []   )
												kwargs["additional_special_tokens"] += [
												    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
												]

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

												_A								= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(str(__A   )   )
												_A								= 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
												_A								= {'''<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
												_A								= 1

												_A								= len(self.sp_model   )
												_A								= {
												    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A   )
												}
												_A								= {v: k for k, v in self.lang_code_to_id.items()}
												_A								= len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

												self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
												_A								= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

												_A								= src_lang if src_lang is not None else '''en_XX'''
												_A								= self.lang_code_to_id[self._src_lang]
												_A								= tgt_lang
												self.set_src_lang_special_tokens(self._src_lang   )

							@property
							def       __A (  self:   str   )    ->				int:
												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:   Any   )    ->				str:
												return self._src_lang

							@src_lang.setter
							def       __A (  self:   Dict		,     __A:   str   )    ->				None:
												_A								= new_src_lang
												self.set_src_lang_special_tokens(self._src_lang   )

							def __getstate__(  self:   str   )    ->				Dict:
												_A								= self.__dict__.copy()
												_A								= None
												return state

							def __setstate__(  self:   int		,     __A:   Dict   )    ->				None:
												_A								= d

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

												_A								= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(self.vocab_file   )

							def       __A (  self:   List[Any]   )    ->				Dict:
												_A								= {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:   Tuple		,     __A:   str   )    ->				List[str]:
												return self.sp_model.encode(__A		,     out_type=__A   )

							def       __A (  self:   List[str]		,     __A:   str   )    ->				int:
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												_A								= 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:   Tuple		,     __A:   int   )    ->				str:
												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:   List[Any]		,     __A:   str   )    ->				Tuple:
												_A								= []
												_A								= ''''''
												_A								= False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(__A   ) + token
																						_A								= True
																						_A								= []
																	else:
																						current_sub_tokens.append(__A   )
																						_A								= False
												out_string += self.sp_model.decode(__A   )
												return out_string.strip()

							def       __A (  self:   Dict		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												if not os.path.isdir(__A   ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
																	return
												_A								= 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:
																						_A								= self.sp_model.serialized_model_proto()
																						fi.write(__A   )

												return (out_vocab_file,)

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

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

												_A								= [1] * len(self.prefix_tokens   )
												_A								= [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:   Tuple		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												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		,     __A:   List[str]		,     __A:   str		,     __A:   Optional[str]		,     __A:   Optional[str]		,     **__A:   Any   )    ->				Optional[int]:
												if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
												_A								= src_lang
												_A								= self(__A		,     add_special_tokens=__A		,     return_tensors=__A		,     **__A   )
												_A								= self.convert_tokens_to_ids(__A   )
												_A								= tgt_lang_id
												return inputs

							def       __A (  self:   Optional[int]		,     __A:   List[str]		,     __A:   str = "en_XX"		,     __A:   Optional[List[str]] = None		,     __A:   str = "ro_RO"		,     **__A:   Optional[Any]		,     )    ->				BatchEncoding:
												_A								= src_lang
												_A								= tgt_lang
												return super().prepare_seqaseq_batch(__A		,     __A		,     **__A   )

							def       __A (  self:   List[str]   )    ->				int:
												return self.set_src_lang_special_tokens(self.src_lang   )

							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												return self.set_tgt_lang_special_tokens(self.tgt_lang   )

							def       __A (  self:   Dict		,     __A:   str   )    ->				None:
												_A								= self.lang_code_to_id[src_lang]
												_A								= [self.cur_lang_code_id]
												_A								= [self.eos_token_id]



							def       __A (  self:   List[str]		,     __A:   str   )    ->				None:
												_A								= self.lang_code_to_id[tgt_lang]
												_A								= [self.cur_lang_code_id]
												_A								= [self.eos_token_id]











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

				__A      =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



