



"""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


a_			   =					logging.get_logger(__name__)

a_			   =					'''▁'''

a_			   =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

a_			   =					{
    '''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'''
        ),
    }
}

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

# fmt: off
a_			   =					['''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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Tuple							       =		VOCAB_FILES_NAMES
						_A		:							Optional[int]							       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A		:							Union[str, Any]							       =		PRETRAINED_VOCAB_FILES_MAP
						_A		:							Any							       =		["""input_ids""", """attention_mask"""]

						_A		:							List[int]							       =		[]
						_A		:							List[int]							       =		[]







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

											snake_case_					:		Tuple 					=					{} if sp_model_kwargs is None else sp_model_kwargs

											snake_case_					:		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=lowercase__   ,		tgt_lang=lowercase__   ,		eos_token=lowercase__   ,		unk_token=lowercase__   ,		sep_token=lowercase__   ,		cls_token=lowercase__   ,		pad_token=lowercase__   ,		mask_token=lowercase__   ,		sp_model_kwargs=self.sp_model_kwargs   ,		**lowercase__   ,		)

											snake_case_					:		Any 					=					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(str(lowercase__       )       )
											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
											snake_case_					:		Optional[int] 					=					{"""<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
											snake_case_					:		Dict 					=					1

											snake_case_					:		Any 					=					len(self.sp_model       )
											snake_case_					:		str 					=					{
											    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__       )
											}
											snake_case_					:		Any 					=					{v: k for k, v in self.lang_code_to_id.items()}
											snake_case_					:		Union[str, 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       )
											snake_case_					:		Any 					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}

											snake_case_					:		str 					=					src_lang if src_lang is not None else """en_XX"""
											snake_case_					:		Optional[int] 					=					self.lang_code_to_id[self._src_lang]
											snake_case_					:		Optional[int] 					=					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   ,		lowercase__       ):
											snake_case_					:		str 					=					new_src_lang
											self.set_src_lang_special_tokens(self._src_lang       )







						def __getstate__(self       ):
											snake_case_					:		Tuple 					=					self.__dict__.copy()
											snake_case_					:		Any 					=					None
											return state







						def __setstate__(self   ,		lowercase__       ):
											snake_case_					:		Optional[int] 					=					d

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

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					{self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return self.sp_model.encode(lowercase__   ,		out_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
											snake_case_					:		List[Any] 					=					self.sp_model.PieceToId(lowercase__       )

											# 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   ,		lowercase__       ):
											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   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					[]
											snake_case_					:		int 					=					""""""
											snake_case_					:		str 					=					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(lowercase__       ) + token
																					snake_case_					:		Optional[Any] 					=					True
																					snake_case_					:		List[str] 					=					[]
																else:
																					current_sub_tokens.append(lowercase__       )
																					snake_case_					:		Optional[int] 					=					False
											out_string += self.sp_model.decode(lowercase__       )
											return out_string.strip()







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											if not os.path.isdir(lowercase__       ):
																logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
																return
											snake_case_					:		str 					=					os.path.join(
											    lowercase__   ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ) and os.path.isfile(self.vocab_file       ):
																copyfile(self.vocab_file   ,		lowercase__       )
											elif not os.path.isfile(self.vocab_file       ):
																with open(lowercase__   ,		"""wb"""       ) as fi:
																					snake_case_					:		Union[str, Any] 					=					self.sp_model.serialized_model_proto()
																					fi.write(lowercase__       )

											return (out_vocab_file,)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = False       ):

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

											snake_case_					:		Dict 					=					[1] * len(self.prefix_tokens       )
											snake_case_					:		int 					=					[1] * len(self.suffix_tokens       )
											if token_ids_a is None:
																return prefix_ones + ([0] * len(lowercase__       )) + suffix_ones
											return prefix_ones + ([0] * len(lowercase__       )) + ([0] * len(lowercase__       )) + suffix_ones







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = 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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											if src_lang is None or tgt_lang is None:
																raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
											snake_case_					:		Tuple 					=					src_lang
											snake_case_					:		int 					=					self(lowercase__   ,		add_special_tokens=lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )
											snake_case_					:		str 					=					self.convert_tokens_to_ids(lowercase__       )
											snake_case_					:		str 					=					tgt_lang_id
											return inputs







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = "en_XX"   ,		lowercase__ = None   ,		lowercase__ = "ro_RO"   ,		**lowercase__   ,		):
											snake_case_					:		Union[str, Any] 					=					src_lang
											snake_case_					:		Union[str, Any] 					=					tgt_lang
											return super().prepare_seqaseq_batch(lowercase__   ,		lowercase__   ,		**lowercase__       )







						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   ,		lowercase__       ):
											snake_case_					:		str 					=					self.lang_code_to_id[src_lang]
											snake_case_					:		str 					=					[self.cur_lang_code_id]
											snake_case_					:		str 					=					[self.eos_token_id]







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					self.lang_code_to_id[tgt_lang]
											snake_case_					:		str 					=					[self.cur_lang_code_id]
											snake_case_					:		str 					=					[self.eos_token_id]









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




