





"""simple docstring"""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

_snake_case	:						Any					=					logging.get_logger(__name__)

_snake_case	:						List[str]					=					{'vocab_file': 'tokenizer.model'}

_snake_case	:						Union[str, Any]					=					{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
_snake_case	:						Tuple					=					{
    'hf-internal-testing/llama-tokenizer': 2048,
}
_snake_case	:						Dict					=					'▁'

_snake_case		,		_snake_case	:						int					=					'[INST]', '[/INST]'
_snake_case		,		_snake_case	:						Any					=					'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_snake_case	:						Optional[Any]					=					'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :int	,			__UpperCamelCase     :Dict="<unk>"	,			__UpperCamelCase     :List[Any]="<s>"	,			__UpperCamelCase     :Any="</s>"	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Dict[str, Any]] = None	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :Tuple=False	,			__UpperCamelCase     :Any=True	,			**__UpperCamelCase     :str	,			):
												A						=    {} if sp_model_kwargs is None else sp_model_kwargs
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else bos_token
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else eos_token
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else unk_token
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else pad_token
												super().__init__(
												    bos_token=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			add_bos_token=__UpperCamelCase	,			add_eos_token=__UpperCamelCase	,			sp_model_kwargs=self.sp_model_kwargs	,			clean_up_tokenization_spaces=__UpperCamelCase	,			legacy=__UpperCamelCase	,			**__UpperCamelCase	,			)
												if legacy:
																			logger.warning_once(
																			    f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
																			    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"						)
												A						=    legacy
												A						=    vocab_file
												A						=    add_bos_token
												A						=    add_eos_token
												A						=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)


					def __getstate__(   self     :Optional[int]						):
												A						=    self.__dict__.copy()
												A						=    None
												A						=    self.sp_model.serialized_model_proto()
												return state


					def __setstate__(   self     :str	,			__UpperCamelCase     :Tuple						):
												A						=    d
												A						=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)


					@property
					def       lowerCamelCase			(   self     :Dict						):
												return self.sp_model.get_piece_size()


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    {self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab


					def       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :str	,			**__UpperCamelCase     :Dict						):
												# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
												# the beginning of the text
												if not self.legacy:
																			A						=    SPIECE_UNDERLINE + text.replace(__UpperCamelCase	,			" "						)
												return super().tokenize(__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :Dict						):
												if not self.legacy:
																			A						=    text.startswith(__UpperCamelCase						)
																			if is_first:
																										A						=    text[1:]

												A						=    self.sp_model.encode(__UpperCamelCase	,			out_type=__UpperCamelCase						)

												if not self.legacy and not is_first and not text.startswith(" "						) and tokens[0].startswith(__UpperCamelCase						):
																			A						=    ([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
												return tokens


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :List[Any]						):
												return self.sp_model.piece_to_id(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :Dict						):
												A						=    self.sp_model.IdToPiece(__UpperCamelCase						)
												return token


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :Dict						):
												A						=    []
												A						=    ""
												A						=    False
												for i, token in enumerate(__UpperCamelCase						):
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										if not prev_is_special and i != 0:
																																	out_string += " "
																										out_string += self.sp_model.decode(__UpperCamelCase						) + token
																										A						=    True
																										A						=    []
																			else:
																										current_sub_tokens.append(__UpperCamelCase						)
																										A						=    False
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :Union[str, Any]	,			__UpperCamelCase     :Optional[str] = None						):
												if not os.path.isdir(__UpperCamelCase						):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
																			return
												A						=    os.path.join(
												    __UpperCamelCase	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						) and os.path.isfile(self.vocab_file						):
																			copyfile(self.vocab_file	,			__UpperCamelCase						)
												elif not os.path.isfile(self.vocab_file						):
																			with open(__UpperCamelCase	,			"wb"						) as fi:
																										A						=    self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase						)

												return (out_vocab_file,)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :Tuple	,			__UpperCamelCase     :Optional[int]=None						):
												A						=    [self.bos_token_id] if self.add_bos_token else []
												A						=    [self.eos_token_id] if self.add_eos_token else []

												A						=    bos_token_id + token_ids_a + eos_token_id

												if token_ids_a is not None:
																			A						=    output + bos_token_id + token_ids_a + eos_token_id

												return output


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None	,			__UpperCamelCase     :bool = False						):
												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase	,			token_ids_a=__UpperCamelCase	,			already_has_special_tokens=__UpperCamelCase						)

												A						=    [1] if self.add_bos_token else []
												A						=    [1] if self.add_eos_token else []

												if token_ids_a is None:
																			return bos_token_id + ([0] * len(__UpperCamelCase						)) + eos_token_id
												return (
												    bos_token_id
												    + ([0] * len(__UpperCamelCase						))
												    + eos_token_id
												    + bos_token_id
												    + ([0] * len(__UpperCamelCase						))
												    + eos_token_id
												)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None						):
												A						=    [self.bos_token_id] if self.add_bos_token else []
												A						=    [self.eos_token_id] if self.add_eos_token else []

												A						=    [0] * len(bos_token_id + token_ids_a + eos_token_id						)

												if token_ids_a is not None:
																			output += [1] * len(bos_token_id + token_ids_a + eos_token_id						)

												return output


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :"Conversation"						):
												A						=    list(conversation.iter_texts()						)
												if not all([is_user for is_user, msg in dialogue[::2]]						) or not all(
												    [not is_user for is_user, msg in dialogue[1::2]]						):
																			raise ValueError(
																			    "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"						)

												A						=    []
												if len(conversation.past_user_inputs						) > 0:
																			if not conversation.past_user_inputs[0].startswith(__UpperCamelCase						) or E_SYS not in conversation.past_user_inputs[0]:
																										A						=    (
																										    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																										)
												elif not dialogue[0][1].startswith(__UpperCamelCase						) or E_SYS not in dialogue[0][1]:
																			A						=    (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

												dialog_tokens += sum(
												    [
												        [self.bos_token_id]
												        + self.encode(
												            f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "	,			add_special_tokens=__UpperCamelCase						)
												        + [self.eos_token_id]
												        for prompt, answer in zip(dialogue[::2]	,			dialogue[1::2]						)
												    ]	,			[]	,			)
												if not (dialogue[-1][0]):
																			raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"						)
												dialog_tokens += [self.bos_token_id] + self.encode(
												    f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"	,			add_special_tokens=__UpperCamelCase						)
												return dialog_tokens









"""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_mbartaa import MBartaaTokenizer
else:
		_snake_case	:						Dict					=					None


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[Any]					=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case	:						str					=					{
    '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'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case	:						Union[str, Any]					=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
_snake_case	:						int					=					['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    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']
					UpperCamelCase						=				MBartaaTokenizer

					UpperCamelCase						=				[]
					UpperCamelCase						=				[]


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

												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__(
												    __UpperCamelCase	,			src_lang=__UpperCamelCase	,			tgt_lang=__UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			**__UpperCamelCase	,			)

												A						=    vocab_file
												A						=    False if not self.vocab_file else True

												A						=    {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

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


					@property
					def       lowerCamelCase			(   self     :int						):
												return self._src_lang


					@src_lang.setter
					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :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       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :str = "en_XX"	,			__UpperCamelCase     :Optional[List[str]] = None	,			__UpperCamelCase     :str = "ro_RO"	,			**__UpperCamelCase     :Tuple	,			):
												A						=    src_lang
												A						=    tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												return self.set_src_lang_special_tokens(self.src_lang						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    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       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    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       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str]	,			__UpperCamelCase     :Optional[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												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(__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    tgt_lang_id
												return inputs


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :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(__UpperCamelCase						):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
																			return
												A						=    os.path.join(
												    __UpperCamelCase	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

												return (out_vocab_file,)


