



'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     """▁"""

snake_case_			=     {"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case_			=     {
    """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"""
        ),
    }
}

snake_case_			=     {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
snake_case_			=     ["""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 	a__     (				_lowercase	):
	__magic_name__		:    Optional[Any]	    =     VOCAB_FILES_NAMES
	__magic_name__		:    List[str]	    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__		:    List[str]	    =     PRETRAINED_VOCAB_FILES_MAP
	__magic_name__		:    List[Any]	    =     ["input_ids", "attention_mask"]

	__magic_name__		:    List[int]	    =     []
	__magic_name__		:    List[int]	    =     []


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= AddedToken(__UpperCAmelCase,					lstrip=__UpperCAmelCase,					rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase,					__UpperCAmelCase      ) else mask_token

					SCREAMING_SNAKE_CASE  :						Dict      					= {} if sp_model_kwargs is None else sp_model_kwargs

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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=__UpperCAmelCase,					tgt_lang=__UpperCAmelCase,					eos_token=__UpperCAmelCase,					unk_token=__UpperCAmelCase,					sep_token=__UpperCAmelCase,					cls_token=__UpperCAmelCase,					pad_token=__UpperCAmelCase,					mask_token=__UpperCAmelCase,					sp_model_kwargs=self.sp_model_kwargs,					**__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						Dict      					= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(str(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						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
					SCREAMING_SNAKE_CASE  :						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
					SCREAMING_SNAKE_CASE  :						Any      					= 1

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

					SCREAMING_SNAKE_CASE  :						List[Any]      					= src_lang if src_lang is not None else '''en_XX'''
					SCREAMING_SNAKE_CASE  :						Dict      					= self.lang_code_to_id[self._src_lang]
					SCREAMING_SNAKE_CASE  :						Dict      					= tgt_lang
					self.set_src_lang_special_tokens(self._src_lang      )


	@property
	def 				lowercase__  (self : int      )     ->			int:

					"""simple docstring"""

					return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token


	@property
	def 				lowercase__  (self : List[Any]      )     ->			str:

					"""simple docstring"""

					return self._src_lang


	@src_lang.setter
	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : str      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= new_src_lang
					self.set_src_lang_special_tokens(self._src_lang      )


	def __getstate__(self : Optional[int]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.__dict__.copy()
					SCREAMING_SNAKE_CASE  :						Any      					= None
					return state


	def __setstate__(self : Union[str, Any],					__UpperCAmelCase : Dict      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= d

					# for backward compatibility
					if not hasattr(self,					'''sp_model_kwargs'''      ):
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {}

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(self.vocab_file      )


	def 				lowercase__  (self : List[str]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= {self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
					vocab.update(self.added_tokens_encoder      )
					return vocab


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str      )     ->			List[str]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str      )     ->			int:

					"""simple docstring"""

					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					SCREAMING_SNAKE_CASE  :						Dict      					= self.sp_model.PieceToId(__UpperCAmelCase      )

					# 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 				lowercase__  (self : str,					__UpperCAmelCase : int      )     ->			str:

					"""simple docstring"""

					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 				lowercase__  (self : Tuple,					__UpperCAmelCase : str      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Tuple      					= ''''''
					SCREAMING_SNAKE_CASE  :						List[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(__UpperCAmelCase      ) + token
													SCREAMING_SNAKE_CASE  :						str      					= True
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									else:
													current_sub_tokens.append(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= False
					out_string += self.sp_model.decode(__UpperCAmelCase      )
					return out_string.strip()


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[str] = None      )     ->			Tuple[str]:

					"""simple docstring"""

					if not os.path.isdir(__UpperCAmelCase      ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
									return
					SCREAMING_SNAKE_CASE  :						str      					= 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:
													SCREAMING_SNAKE_CASE  :						Dict      					= self.sp_model.serialized_model_proto()
													fi.write(__UpperCAmelCase      )

					return (out_vocab_file,)


	def 				lowercase__  (self : str,					__UpperCAmelCase : List[int],					__UpperCAmelCase : Optional[List[int]] = None,					__UpperCAmelCase : bool = False      )     ->			List[int]:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						List[str]      					= [1] * len(self.prefix_tokens      )
					SCREAMING_SNAKE_CASE  :						Any      					= [1] * len(self.suffix_tokens      )
					if token_ids_a is None:
									return prefix_ones + ([0] * len(__UpperCAmelCase      )) + suffix_ones
					return prefix_ones + ([0] * len(__UpperCAmelCase      )) + ([0] * len(__UpperCAmelCase      )) + suffix_ones


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[int],					__UpperCAmelCase : Optional[List[int]] = None      )     ->			List[int]:

					"""simple docstring"""

					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 				lowercase__  (self : Tuple,					__UpperCAmelCase : Dict,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[str],					__UpperCAmelCase : Optional[str],					**__UpperCAmelCase : Any      )     ->			List[Any]:

					"""simple docstring"""

					if src_lang is None or tgt_lang is None:
									raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= src_lang
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self(__UpperCAmelCase,					add_special_tokens=__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.convert_tokens_to_ids(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tgt_lang_id
					return inputs


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str],					__UpperCAmelCase : str = "en_XX",					__UpperCAmelCase : Optional[List[str]] = None,					__UpperCAmelCase : str = "ro_RO",					**__UpperCAmelCase : Any,					)     ->			BatchEncoding:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= src_lang
					SCREAMING_SNAKE_CASE  :						str      					= tgt_lang
					return super().prepare_seqaseq_batch(__UpperCAmelCase,					__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			Any:

					"""simple docstring"""

					return self.set_src_lang_special_tokens(self.src_lang      )


	def 				lowercase__  (self : Optional[int]      )     ->			Optional[int]:

					"""simple docstring"""

					return self.set_tgt_lang_special_tokens(self.tgt_lang      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.lang_code_to_id[src_lang]
					SCREAMING_SNAKE_CASE  :						str      					= [self.cur_lang_code_id]
					SCREAMING_SNAKE_CASE  :						Any      					= [self.eos_token_id]






	def 				lowercase__  (self : str,					__UpperCAmelCase : str      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.lang_code_to_id[tgt_lang]
					SCREAMING_SNAKE_CASE  :						str      					= [self.cur_lang_code_id]
					SCREAMING_SNAKE_CASE  :						str      					= [self.eos_token_id]









'''simple docstring'''

from collections import deque






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= deque()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [False for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Any      					= [-1 for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= index_of[:]

				def strong_connect(_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
								SCREAMING_SNAKE_CASE  :						Any      					= index  # the number when this node is seen
								SCREAMING_SNAKE_CASE  :						str      					= index  # lowest rank node reachable from here
								index += 1
								stack.append(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= True

								for w in g[v]:
												if index_of[w] == -1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= strong_connect(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												SCREAMING_SNAKE_CASE  :						str      					= []
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= stack.pop()
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
												component.append(_SCREAMING_SNAKE_CASE       )
												while w != v:
																SCREAMING_SNAKE_CASE  :						int      					= stack.pop()
																SCREAMING_SNAKE_CASE  :						Tuple      					= False
																component.append(_SCREAMING_SNAKE_CASE       )
												components.append(_SCREAMING_SNAKE_CASE       )
								return index

				SCREAMING_SNAKE_CASE  :						int      					= []
				for v in range(_SCREAMING_SNAKE_CASE       ):
								if index_of[v] == -1:
												strong_connect(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE       )

				return components






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= [[] for _ in range(_SCREAMING_SNAKE_CASE       )]
				for u, v in edges:
								g[u].append(_SCREAMING_SNAKE_CASE       )
				return g


if __name__ == "__main__":
	# Test
	snake_case_			=     7
	snake_case_			=     [0, 0, 1, 2, 3, 3, 4, 4, 6]
	snake_case_			=     [1, 3, 2, 0, 1, 4, 5, 6, 5]
	snake_case_			=     [(u, v) for u, v in zip(source, target)]
	snake_case_			=     create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)




