




'''simple docstring'''

a  :				Tuple									=  {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
a  :				Optional[Any]									=  ['a', 'b', 'c', 'd', 'e']


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				start
					# add current to visited
					visited.append(__UpperCAmelCase	)
					snake_case_									=				edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															snake_case_									=				topological_sort(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
    # if all neighbors visited add current to sort
					sort.append(__UpperCAmelCase	)
					# if all vertices haven't been visited select a new one to visit
					if len(__UpperCAmelCase	) != len(__UpperCAmelCase	):
										for vertice in vertices:
															if vertice not in visited:
																				snake_case_									=				topological_sort(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
    # return sort
					return sort


if __name__ == "__main__":
							a  :				int									=  topological_sort('a', [], [])
							print(sort)








import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


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

A    :					str =      '''▁'''

A    :					Any =      {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A    :					List[Any] =      {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

A    :					Tuple =      {
    '''facebook/m2m100_418M''': 1_0_2_4,
}

# fmt: off
A    :					Optional[int] =      {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       List[str]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       Optional[int]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Dict							=    ['''input_ids''', '''attention_mask''']

			__lowerCamelCase	:       List[int]							=    []
			__lowerCamelCase	:       List[int]							=    []






			def __init__(			self   :    List[Any]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Union[str, Any]=None							,	__lowerCAmelCase   :    str=None							,	__lowerCAmelCase   :    List[Any]="<s>"							,	__lowerCAmelCase   :    List[Any]="</s>"							,	__lowerCAmelCase   :    Optional[int]="</s>"							,	__lowerCAmelCase   :    Optional[Any]="<pad>"							,	__lowerCAmelCase   :    Any="<unk>"							,	__lowerCAmelCase   :    Any="m2m100"							,	__lowerCAmelCase   :    Optional[Dict[str, Any]] = None							,	__lowerCAmelCase   :    Dict=8							,	**__lowerCAmelCase   :    Tuple							,	)	->	None:







					"""simple docstring"""






					A__        =   {} if sp_model_kwargs is None else sp_model_kwargs

					A__        =   language_codes
					A__        =   FAIRSEQ_LANGUAGE_CODES[language_codes]
					A__        =   {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

					A__        =   kwargs.get("""additional_special_tokens"""							,	[]   )
					kwargs["additional_special_tokens"] += [
					    self.get_lang_token(__lowerCAmelCase   )
					    for lang_code in fairseq_language_code
					    if self.get_lang_token(__lowerCAmelCase   ) not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    src_lang=__lowerCAmelCase							,	tgt_lang=__lowerCAmelCase							,	bos_token=__lowerCAmelCase							,	eos_token=__lowerCAmelCase							,	sep_token=__lowerCAmelCase							,	unk_token=__lowerCAmelCase							,	pad_token=__lowerCAmelCase							,	language_codes=__lowerCAmelCase							,	sp_model_kwargs=self.sp_model_kwargs							,	num_madeup_words=__lowerCAmelCase							,	**__lowerCAmelCase							,	)

					A__        =   vocab_file
					A__        =   load_json(__lowerCAmelCase   )
					A__        =   {v: k for k, v in self.encoder.items()}
					A__        =   spm_file
					A__        =   load_spm(__lowerCAmelCase							,	self.sp_model_kwargs   )

					A__        =   len(self.encoder   )

					A__        =   {
					    self.get_lang_token(__lowerCAmelCase   ): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase   )
					}
					A__        =   {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase   )}
					A__        =   {v: k for k, v in self.lang_token_to_id.items()}

					A__        =   src_lang if src_lang is not None else """en"""
					A__        =   tgt_lang
					A__        =   self.get_lang_id(self._src_lang   )
					self.set_src_lang_special_tokens(self._src_lang   )

					A__        =   num_madeup_words






			@property
			def    a_       (			self   :    Optional[int]   )	->	int:







					"""simple docstring"""






					return len(self.encoder   ) + len(self.lang_token_to_id   )






			@property
			def    a_       (			self   :    Optional[Any]   )	->	str:







					"""simple docstring"""






					return self._src_lang






			@src_lang.setter
			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    str   )	->	None:







					"""simple docstring"""






					A__        =   new_src_lang
					self.set_src_lang_special_tokens(self._src_lang   )






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






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






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					if token in self.lang_token_to_id:
							return self.lang_token_to_id[token]
					return self.encoder.get(__lowerCAmelCase							,	self.encoder[self.unk_token]   )






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    int   )	->	str:







					"""simple docstring"""






					if index in self.id_to_lang_token:
							return self.id_to_lang_token[index]
					return self.decoder.get(__lowerCAmelCase							,	self.unk_token   )






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					A__        =   []
					A__        =   """"""
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									out_string += self.sp_model.decode(__lowerCAmelCase   ) + token
									A__        =   []
							else:
									current_sub_tokens.append(__lowerCAmelCase   )
					out_string += self.sp_model.decode(__lowerCAmelCase   )
					return out_string.strip()






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







					"""simple docstring"""






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

					A__        =   [1] * len(self.prefix_tokens   )
					A__        =   [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    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    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    a_       (			self   :    int   )	->	Dict:







					"""simple docstring"""






					A__        =   {self.convert_ids_to_tokens(__lowerCAmelCase   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab






			def __getstate__(			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.__dict__.copy()
					A__        =   None
					return state






			def __setstate__(			self   :    str							,	__lowerCAmelCase   :    Dict   )	->	None:







					"""simple docstring"""






					A__        =   d

					# for backward compatibility
					if not hasattr(self							,	"""sp_model_kwargs"""   ):
							A__        =   {}

					A__        =   load_spm(self.spm_file							,	self.sp_model_kwargs   )






			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






					A__        =   Path(__lowerCAmelCase   )
					if not save_dir.is_dir():
							raise OSError(f'{save_directory} should be a directory'   )
					A__        =   save_dir / (
					    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
					)
					A__        =   save_dir / (
					    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
					)

					save_json(self.encoder							,	__lowerCAmelCase   )

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

					return (str(__lowerCAmelCase   ), str(__lowerCAmelCase   ))






			def    a_       (			self   :    str							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    str = "en"							,	__lowerCAmelCase   :    Optional[List[str]] = None							,	__lowerCAmelCase   :    str = "ro"							,	**__lowerCAmelCase   :    List[Any]							,	)	->	BatchEncoding:







					"""simple docstring"""






					A__        =   src_lang
					A__        =   tgt_lang
					self.set_src_lang_special_tokens(self.src_lang   )
					return super().prepare_seqaseq_batch(__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Optional[str]							,	__lowerCAmelCase   :    Optional[str]							,	**__lowerCAmelCase   :    Tuple   )	->	Tuple:







					"""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"""   )
					A__        =   src_lang
					A__        =   self(__lowerCAmelCase							,	add_special_tokens=__lowerCAmelCase							,	**__lowerCAmelCase   )
					A__        =   self.get_lang_id(__lowerCAmelCase   )
					A__        =   tgt_lang_id
					return inputs






			def    a_       (			self   :    Dict   )	->	int:







					"""simple docstring"""






					self.set_src_lang_special_tokens(self.src_lang   )






			def    a_       (			self   :    Union[str, Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					self.set_tgt_lang_special_tokens(self.tgt_lang   )






			def    a_       (			self   :    str							,	__lowerCAmelCase   :    str   )	->	None:







					"""simple docstring"""






					A__        =   self.get_lang_token(__lowerCAmelCase   )
					A__        =   self.lang_token_to_id[lang_token]
					A__        =   [self.cur_lang_id]
					A__        =   [self.eos_token_id]






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    str   )	->	None:







					"""simple docstring"""






					A__        =   self.get_lang_token(__lowerCAmelCase   )
					A__        =   self.lang_token_to_id[lang_token]
					A__        =   [self.cur_lang_id]
					A__        =   [self.eos_token_id]






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    str   )	->	str:







					"""simple docstring"""






					return self.lang_code_to_token[lang]







			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    str   )	->	int:







					"""simple docstring"""






					A__        =   self.get_lang_token(__lowerCAmelCase   )
					return self.lang_token_to_id[lang_token]







def    __lowerCamelCase					(							__a   :str    ,     __a   :Dict[str, Any]    )		->				sentencepiece.SentencePieceProcessor:






		"""simple docstring"""

		A__        =   sentencepiece.SentencePieceProcessor(**__a    )
		spm.Load(str(__a    )    )
		return spm







def    __lowerCamelCase					(							__a   :str    )		->				Union[Dict, List]:






		"""simple docstring"""

		with open(__a    ,     """r"""    ) as f:
				return json.load(__a    )







def    __lowerCamelCase					(							__a   :List[Any]    ,     __a   :str    )		->				None:






		"""simple docstring"""

		with open(__a    ,     """w"""    ) as f:
				json.dump(__a    ,     __a    ,     indent=2    )



