





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_  :	Tuple 				=					logging.get_logger(__name__)

lowerCamelCase_  :	List[str] 				=					"▁"

lowerCamelCase_  :	Tuple 				=					{"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase_  :	List[Any] 				=					{
    "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"
        ),
    }
}

lowerCamelCase_  :	List[str] 				=					{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
lowerCamelCase_  :	Union[str, 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", "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_					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Optional[Any]    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Optional[Any]    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  str    =	["input_ids", "attention_mask"]

			__UpperCamelCase :  List[int]    =	[]
			__UpperCamelCase :  List[int]    =	[]

			def __init__(    self	:    Optional[Any]  ,		snake_case_	:    Any  ,		snake_case_	:    Optional[Any]=None  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    str="</s>"  ,		snake_case_	:    List[str]="</s>"  ,		snake_case_	:    List[Any]="<s>"  ,		snake_case_	:    str="<unk>"  ,		snake_case_	:    str="<pad>"  ,		snake_case_	:    Any="<mask>"  ,		snake_case_	:    int = None  ,		**snake_case_	:    Optional[Any]  ,		):
							UpperCamelCase_:							List[str]		=  AddedToken(a__  ,		lstrip=a__  ,		rstrip=a__ ) if isinstance(a__  ,		a__ ) else mask_token

							UpperCamelCase_:							int		=  {} if sp_model_kwargs is None else sp_model_kwargs

							UpperCamelCase_:							int		=  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__  ,		)

							UpperCamelCase_:							int		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(str(a__ ) )
							UpperCamelCase_:							Optional[int]		=  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
							UpperCamelCase_:							Dict		=  {"""<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
							UpperCamelCase_:							List[str]		=  1

							UpperCamelCase_:							str		=  len(self.sp_model )
							UpperCamelCase_:							str		=  {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a__ )
							}
							UpperCamelCase_:							Dict		=  {v: k for k, v in self.lang_code_to_id.items()}
							UpperCamelCase_:							Dict		=  len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

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

							UpperCamelCase_:							Any		=  src_lang if src_lang is not None else """en_XX"""
							UpperCamelCase_:							Optional[Any]		=  self.lang_code_to_id[self._src_lang]
							UpperCamelCase_:							List[str]		=  tgt_lang
							self.set_src_lang_special_tokens(self._src_lang )

			@property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token

			@property
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							return self._src_lang

			@src_lang.setter
			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    str ):
							UpperCamelCase_:							Optional[Any]		=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang )

			def __getstate__(    self	:    List[Any] ):
							UpperCamelCase_:							List[Any]		=  self.__dict__.copy()
							UpperCamelCase_:							Union[str, Any]		=  None
							return state

			def __setstate__(    self	:    str  ,		snake_case_	:    Any ):
							UpperCamelCase_:							List[str]		=  d

							# for backward compatibility
							if not hasattr(self  ,		"""sp_model_kwargs""" ):
											UpperCamelCase_:							List[Any]		=  {}

							UpperCamelCase_:							List[Any]		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Dict		=  {self.convert_ids_to_tokens(a__ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    Optional[int] ):
							return self.sp_model.encode(a__  ,		out_type=a__ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any] ):




							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							UpperCamelCase_:							Tuple		=  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  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    Any ):




							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  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Any ):
							UpperCamelCase_:							Any		=  []
							UpperCamelCase_:							List[Any]		=  """"""
							UpperCamelCase_:							Union[str, Any]		=  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
															UpperCamelCase_:							Union[str, Any]		=  True
															UpperCamelCase_:							Tuple		=  []
											else:
															current_sub_tokens.append(a__ )
															UpperCamelCase_:							Optional[int]		=  False
							out_string += self.sp_model.decode(a__ )
							return out_string.strip()

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    Tuple  ,		snake_case_	:    Tuple = None ):




							if not os.path.isdir(a__ ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
											return
							UpperCamelCase_:							str		=  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:
															UpperCamelCase_:							List[str]		=  self.sp_model.serialized_model_proto()
															fi.write(a__ )

							return (out_vocab_file,)

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any = None  ,		snake_case_	:    int = False ):




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

							UpperCamelCase_:							Any		=  [1] * len(self.prefix_tokens )
							UpperCamelCase_:							Optional[int]		=  [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  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    str = 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	:    str  ,		snake_case_	:    Any  ,		snake_case_	:    Any  ,		snake_case_	:    int  ,		snake_case_	:    int  ,		**snake_case_	:    Dict ):




							if src_lang is None or tgt_lang is None:
											raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
							UpperCamelCase_:							Any		=  src_lang
							UpperCamelCase_:							int		=  self(a__  ,		add_special_tokens=a__  ,		return_tensors=a__  ,		**a__ )
							UpperCamelCase_:							List[Any]		=  self.convert_tokens_to_ids(a__ )
							UpperCamelCase_:							List[Any]		=  tgt_lang_id
							return inputs

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    int = "en_XX"  ,		snake_case_	:    Optional[Any] = None  ,		snake_case_	:    Dict = "ro_RO"  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							List[str]		=  src_lang
							UpperCamelCase_:							List[str]		=  tgt_lang
							return super().prepare_seqaseq_batch(a__  ,		a__  ,		**a__ )

			def  lowerCAmelCase__		(    self	:    int ):
							return self.set_src_lang_special_tokens(self.src_lang )

			def  lowerCAmelCase__		(    self	:    Any ):
							return self.set_tgt_lang_special_tokens(self.tgt_lang )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str ):
							UpperCamelCase_:							Any		=  self.lang_code_to_id[src_lang]
							UpperCamelCase_:							Dict		=  [self.cur_lang_code_id]
							UpperCamelCase_:							str		=  [self.eos_token_id]




			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Tuple ):
							UpperCamelCase_:							str		=  self.lang_code_to_id[tgt_lang]
							UpperCamelCase_:							int		=  [self.cur_lang_code_id]
							UpperCamelCase_:							Union[str, Any]		=  [self.eos_token_id]








import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizer
			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizerFast
			__UpperCamelCase :  Optional[Any]    =	True
			__UpperCamelCase :  Tuple    =	True

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().setUp()

			def  lowerCAmelCase__		(    self	:    List[str]  ,		**snake_case_	:    Union[str, Any] ):
							return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Union[str, Any] ):
							return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """永和服装饰品有限公司,今天天气非常好"""
							UpperCamelCase_:							int		=  """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
							return input_text, output_text

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Dict		=  self.get_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							Optional[int]		=  tokenizer.tokenize(snake_case_ )

							self.assertListEqual(snake_case_  ,		output_text.split() )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							int		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Any		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		output_text.split() )
							UpperCamelCase_:							Union[str, Any]		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							Optional[Any]		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Tuple ):
							pass




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							pass






