
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_mbart import MBartTokenizer
else:
							UpperCamelCase_						:     Union[str, Any]	       =							None


UpperCamelCase_						:     Optional[Any]	       =							logging.get_logger(__name__)


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

UpperCamelCase_						:     Optional[int]	       =							{
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase_						:     Union[str, Any]	       =							{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
UpperCamelCase_						:     List[str]	       =							["""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						(						__snake_case       ):
	_A       =   VOCAB_FILES_NAMES
	_A       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       =   PRETRAINED_VOCAB_FILES_MAP
	_A       =   ["input_ids", "attention_mask"]
	_A       =   MBartTokenizer

	_A       =   []
	_A       =   []






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

							super().__init__(
							    vocab_file=snake_case      ,	tokenizer_file=snake_case      ,	bos_token=snake_case      ,	eos_token=snake_case      ,	sep_token=snake_case      ,	cls_token=snake_case      ,	unk_token=snake_case      ,	pad_token=snake_case      ,	mask_token=snake_case      ,	src_lang=snake_case      ,	tgt_lang=snake_case      ,	additional_special_tokens=snake_case      ,	**snake_case      ,	)

							_lowercase       :	Dict           =   vocab_file
							_lowercase       :	List[str]           =   False if not self.vocab_file else True

							_lowercase       :	Any           =   FAIRSEQ_LANGUAGE_CODES.copy()

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]    )

							self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}    )
							_lowercase       :	Dict           =   {
							    lang_code: self.convert_tokens_to_ids(snake_case    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							_lowercase       :	Any           =   src_lang if src_lang is not None else "en_XX"
							_lowercase       :	Tuple           =   self.convert_tokens_to_ids(self._src_lang    )
							_lowercase       :	Optional[int]           =   tgt_lang
							self.set_src_lang_special_tokens(self._src_lang    )






	@property
	def       _a(self				: Tuple    ) ->			str:
							return self._src_lang






	@src_lang.setter
	def       _a(self				: Optional[Any]      ,	snake_case				: str    ) ->			None:
							_lowercase       :	Dict           =   new_src_lang
							self.set_src_lang_special_tokens(self._src_lang    )






	def       _a(self				: Any      ,	snake_case				: List[int]      ,	snake_case				: 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				: List[str]      ,	snake_case				: List[int]      ,	snake_case				: Optional[List[int]] = None    ) ->			List[int]:
							_lowercase       :	Optional[Any]           =   [self.sep_token_id]
							_lowercase       :	List[str]           =   [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]      ,	snake_case				: Optional[Any]      ,	snake_case				: str      ,	snake_case				: Optional[str]      ,	snake_case				: Optional[str]      ,	**snake_case				: Optional[int]    ) ->			Tuple:
							if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"    )
							_lowercase       :	str           =   src_lang
							_lowercase       :	List[str]           =   self(snake_case      ,	add_special_tokens=snake_case      ,	return_tensors=snake_case      ,	**snake_case    )
							_lowercase       :	List[str]           =   self.convert_tokens_to_ids(snake_case    )
							_lowercase       :	Any           =   tgt_lang_id
							return inputs






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






	def       _a(self				: Optional[int]    ) ->			int:
							return self.set_src_lang_special_tokens(self.src_lang    )






	def       _a(self				: str    ) ->			List[Any]:
							return self.set_tgt_lang_special_tokens(self.tgt_lang    )






	def       _a(self				: Any      ,	snake_case				: str    ) ->			None:
							_lowercase       :	List[Any]           =   self.convert_tokens_to_ids(snake_case    )
							_lowercase       :	Optional[int]           =   []
							_lowercase       :	int           =   [self.eos_token_id, self.cur_lang_code]

							_lowercase       :	Union[str, Any]           =   self.convert_ids_to_tokens(self.prefix_tokens    )
							_lowercase       :	str           =   self.convert_ids_to_tokens(self.suffix_tokens    )

							_lowercase       :	Dict           =   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       _a(self				: Any      ,	snake_case				: str    ) ->			None:
							_lowercase       :	int           =   self.convert_tokens_to_ids(snake_case    )
							_lowercase       :	Optional[Any]           =   []
							_lowercase       :	Dict           =   [self.eos_token_id, self.cur_lang_code]

							_lowercase       :	Tuple           =   self.convert_ids_to_tokens(self.prefix_tokens    )
							_lowercase       :	int           =   self.convert_ids_to_tokens(self.suffix_tokens    )

							_lowercase       :	Optional[Any]           =   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       _a(self				: List[Any]      ,	snake_case				: str      ,	snake_case				: Optional[str] = None    ) ->			Tuple[str]:
							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(snake_case    ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""    )
													return
							_lowercase       :	List[Any]           =   os.path.join(
							    snake_case      ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

							return (out_vocab_file,)






import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




