





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

import sentencepiece as spm

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


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

UpperCamelCase_		:				List[Any]				=						'''▁'''

UpperCamelCase_		:				List[str]				=						{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

UpperCamelCase_		:				Union[str, Any]				=						{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

UpperCamelCase_		:				Union[str, Any]				=						{'''vinai/bartpho-syllable''': 1024}


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Any								=	VOCAB_FILES_NAMES
						_UpperCamelCase  :	Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase  :	Tuple								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase  :	Optional[Any]								=	["""input_ids""", """attention_mask"""]







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





										'''simple docstring'''







										_snake_case :			List[str]  						=   AddedToken(lowerCamelCase_ ,				lstrip=lowerCamelCase_ ,				rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_ ,				lowerCamelCase_					) else mask_token

										_snake_case :			Optional[int]  						=   {} 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_ ,				sp_model_kwargs=self.sp_model_kwargs ,				**lowerCamelCase_ ,				)

										_snake_case :			Optional[int]  						=   vocab_file
										_snake_case :			Optional[Any]  						=   monolingual_vocab_file
										_snake_case :			str  						=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(str(lowerCamelCase_					)					)

										# Load the reduced vocab

										# Keep order of special tokens for backward compatibility
										_snake_case :			Dict  						=   {}
										_snake_case :			int  						=   0
										for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
														if str(lowerCamelCase_					) not in self.fairseq_tokens_to_ids:
																		_snake_case :			Optional[Any]  						=   cnt
																		cnt += 1
										with open(lowerCamelCase_ ,				'r' ,				encoding='utf-8'					) as f:
														for line in f.readlines():
																		_snake_case :			Union[str, Any]  						=   line.strip().split()[0]
																		_snake_case :			Dict  						=   len(self.fairseq_tokens_to_ids					)
										if str(lowerCamelCase_					) not in self.fairseq_tokens_to_ids:
														_snake_case :			Union[str, Any]  						=   len(self.fairseq_tokens_to_ids					)

										_snake_case :			Any  						=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}







						def __getstate__(    self			:		str					):





										'''simple docstring'''







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







						def __setstate__(    self			:		str ,				lowerCamelCase_			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   d

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

										_snake_case :			Any  						=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Union[str, Any] = None					):





										'''simple docstring'''







										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_snake_case :			Tuple  						=   [self.cls_token_id]
										_snake_case :			Any  						=   [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		int = None ,				lowerCamelCase_			:		int = 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_					)

										if token_ids_a is None:
														return [1] + ([0] * len(lowerCamelCase_					)) + [1]
										return [1] + ([0] * len(lowerCamelCase_					)) + [1, 1] + ([0] * len(lowerCamelCase_					)) + [1]







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		Optional[int] = None					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   [self.sep_token_id]
										_snake_case :			Tuple  						=   [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]







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										return len(self.fairseq_ids_to_tokens					)







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			str  						=   {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			:		str ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
										else:
														return self.unk_token_id







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										return self.fairseq_ids_to_tokens[index]







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Any					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   ''.join(lowerCamelCase_					).replace(lowerCamelCase_ ,				' '					).strip()
										return out_string







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] = None					):





										'''simple docstring'''







										if not os.path.isdir(lowerCamelCase_					):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
										_snake_case :			List[Any]  						=   os.path.join(
										    lowerCamelCase_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case :			str  						=   os.path.join(
										    lowerCamelCase_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
																		_snake_case :			Dict  						=   self.sp_model.serialized_model_proto()
																		fi.write(lowerCamelCase_					)

										if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
										    lowerCamelCase_					) and os.path.isfile(self.monolingual_vocab_file					):
														copyfile(self.monolingual_vocab_file ,				lowerCamelCase_					)
										elif not os.path.isfile(self.monolingual_vocab_file					):
														with open(lowerCamelCase_ ,				'w' ,				encoding='utf-8'					) as fp:
																		for token in self.fairseq_tokens_to_ids:
																						if token not in self.all_special_tokens:
																										fp.write(f'''{str(lowerCamelCase_					)} \n'''					)

										return out_vocab_file, out_monolingual_vocab_file













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






