

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

_lowerCAmelCase  :      Tuple			 ="""▁"""

_lowerCAmelCase  :      Union[str, Any]			 ={"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCAmelCase  :      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"""
        ),
    }
}

_lowerCAmelCase  :      Any			 ={
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
_lowerCAmelCase  :      Optional[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     (				_a		):






					'''simple docstring'''



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

					__magic_name__        =   []
					__magic_name__        =   []





					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="<unk>"				,      lowerCamelCase__="<pad>"				,      lowerCamelCase__="<mask>"				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						# Mask token behave like a normal word, i.e. include the space before it
						UpperCAmelCase__:	int									= AddedToken(lowerCamelCase__				,      lstrip=lowerCamelCase__				,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else mask_token

						UpperCAmelCase__:	Optional[int]									= {} if sp_model_kwargs is None else sp_model_kwargs

						UpperCAmelCase__:	Tuple									= 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=lowerCamelCase__				,      tgt_lang=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__				,      )

						UpperCAmelCase__:	Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
						self.sp_model.Load(str(lowerCamelCase__						)						)
						UpperCAmelCase__:	Tuple									= 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__:	Union[str, Any]									= {"<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__:	Tuple									= 1

						UpperCAmelCase__:	List[str]									= len(self.sp_model						)
						UpperCAmelCase__:	Optional[int]									= {
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase__						)
						}
						UpperCAmelCase__:	str									= {v: k for k, v in self.lang_code_to_id.items()}
						UpperCAmelCase__:	Optional[Any]									= 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__:	int									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

						UpperCAmelCase__:	str									= src_lang if src_lang is not None else "en_XX"
						UpperCAmelCase__:	Optional[int]									= self.lang_code_to_id[self._src_lang]
						UpperCAmelCase__:	Union[str, Any]									= tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					@property
					def    _UpperCAmelCase    (				self						):
						return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token





					@property
					def    _UpperCAmelCase    (				self						):
						return self._src_lang





					@src_lang.setter
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					def __getstate__(				self						):
						UpperCAmelCase__:	Any									= self.__dict__.copy()
						UpperCAmelCase__:	Optional[int]									= None
						return state





					def __setstate__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= d

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

						UpperCAmelCase__:	int									= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
						self.sp_model.Load(self.vocab_file						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= {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				,      lowerCamelCase__						):
						return self.sp_model.encode(lowerCamelCase__				,      out_type=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						UpperCAmelCase__:	Optional[int]									= self.sp_model.PieceToId(lowerCamelCase__						)

						# 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    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						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    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= []
						UpperCAmelCase__:	Any									= ""
						UpperCAmelCase__:	Tuple									= 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(lowerCamelCase__						) + token
								UpperCAmelCase__:	str									= True
								UpperCAmelCase__:	Dict									= []
							else:
								current_sub_tokens.append(lowerCamelCase__						)
								UpperCAmelCase__:	Optional[Any]									= False
						out_string += self.sp_model.decode(lowerCamelCase__						)
						return out_string.strip()





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						if not os.path.isdir(lowerCamelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
							return
						UpperCAmelCase__:	List[Any]									= os.path.join(
						    lowerCamelCase__				,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
								UpperCAmelCase__:	Optional[Any]									= self.sp_model.serialized_model_proto()
								fi.write(lowerCamelCase__						)

						return (out_vocab_file,)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False						):

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

						UpperCAmelCase__:	Optional[int]									= [1] * len(self.prefix_tokens						)
						UpperCAmelCase__:	Any									= [1] * len(self.suffix_tokens						)
						if token_ids_a is None:
							return prefix_ones + ([0] * len(lowerCamelCase__						)) + suffix_ones
						return prefix_ones + ([0] * len(lowerCamelCase__						)) + ([0] * len(lowerCamelCase__						)) + suffix_ones





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						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__:	str									= src_lang
						UpperCAmelCase__:	List[Any]									= self(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors=lowerCamelCase__				,      **lowerCamelCase__						)
						UpperCAmelCase__:	Any									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= tgt_lang_id
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = "en_XX"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "ro_RO"				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	List[str]									= src_lang
						UpperCAmelCase__:	Any									= tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						return self.set_src_lang_special_tokens(self.src_lang						)





					def    _UpperCAmelCase    (				self						):
						return self.set_tgt_lang_special_tokens(self.tgt_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= self.lang_code_to_id[src_lang]
						UpperCAmelCase__:	str									= [self.cur_lang_code_id]
						UpperCAmelCase__:	str									= [self.eos_token_id]





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Union[str, Any]									= self.lang_code_to_id[tgt_lang]
						UpperCAmelCase__:	Optional[Any]									= [self.cur_lang_code_id]
						UpperCAmelCase__:	Union[str, Any]									= [self.eos_token_id]


def 			_A	(      SCREAMING_SNAKE_CASE      ):  # noqa: E741
	UpperCAmelCase__:	int									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[int]									= [0] * n
	UpperCAmelCase__:	List[str]									= [False] * n
	UpperCAmelCase__:	List[str]									= [False] * n

	def dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		if parent == root:
			out_edge_count += 1
		UpperCAmelCase__:	List[str]									= True
		UpperCAmelCase__:	List[Any]									= at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				UpperCAmelCase__:	str									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				UpperCAmelCase__:	Optional[Any]									= min(low[at]	,low[to]      )

				# AP found via bridge
				if at < low[to]:
					UpperCAmelCase__:	List[Any]									= True
				# AP found via cycle
				if at == low[to]:
					UpperCAmelCase__:	str									= True
			else:
				UpperCAmelCase__:	Union[str, Any]									= min(low[at]	,SCREAMING_SNAKE_CASE      )
		return out_edge_count

	for i in range(SCREAMING_SNAKE_CASE      ):
		if not visited[i]:
			UpperCAmelCase__:	Optional[Any]									= 0
			UpperCAmelCase__:	Union[str, Any]									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,-1	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Tuple									= out_edge_count > 1

	for x in range(len(SCREAMING_SNAKE_CASE      )      ):
		if is_art[x] is True:
			print(SCREAMING_SNAKE_CASE      )


# Adjacency list of graph
_lowerCAmelCase  :      Any			 ={
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(data)