



'''simple docstring'''




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

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__	:     int    =			logging.get_logger(__name__)

UpperCamelCase__	:     List[Any]    =			{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCamelCase__	:     List[str]    =			{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCamelCase__	:     Dict    =			{'''Helsinki-NLP/opus-mt-en-de''': 5_12}
UpperCamelCase__	:     List[Any]    =			{}






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Tuple         =		VOCAB_FILES_NAMES
			_A				:		Union[str, Any]         =		PRETRAINED_VOCAB_FILES_MAP
			_A				:		Optional[int]         =		PRETRAINED_INIT_CONFIGURATION
			_A				:		Union[str, Any]         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A				:		Optional[Any]         =		['''input_ids''', '''attention_mask''']
			_A				:		Tuple         =		re.compile('''>>.+<<'''   )  # type: re.Pattern

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[Any]=None							,					lowerCAmelCase__					:			int=None							,					lowerCAmelCase__					:			str=None							,					lowerCAmelCase__					:			str="<unk>"							,					lowerCAmelCase__					:			List[Any]="</s>"							,					lowerCAmelCase__					:			List[Any]="<pad>"							,					lowerCAmelCase__					:			Dict=5_1_2							,					lowerCAmelCase__					:			Optional[Dict[str, Any]] = None							,					lowerCAmelCase__					:			Optional[Any]=False							,					**lowerCAmelCase__					:			List[Any]							,					):





					"""simple docstring"""






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

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=lowerCAmelCase__							,					target_lang=lowerCAmelCase__							,					unk_token=lowerCAmelCase__							,					eos_token=lowerCAmelCase__							,					pad_token=lowerCAmelCase__							,					model_max_length=lowerCAmelCase__							,					sp_model_kwargs=self.sp_model_kwargs							,					target_vocab_file=lowerCAmelCase__							,					separate_vocabs=lowerCAmelCase__							,					**lowerCAmelCase__							,					)
					assert Path(lowerCAmelCase__						).exists(), F"cannot find spm source {source_spm}"

					__SCREAMING_SNAKE_CASE	:		int   	=		separate_vocabs
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		load_json(lowerCAmelCase__						)
					if self.unk_token not in self.encoder:
							raise KeyError("""<unk> token must be in vocab"""						)
					assert self.pad_token in self.encoder

					if separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		load_json(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		{v: k for k, v in self.target_encoder.items()}
							__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
					else:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		{v: k for k, v in self.encoder.items()}
							__SCREAMING_SNAKE_CASE	:		list   	=		[k for k in self.encoder if k.startswith(""">>"""						) and k.endswith("""<<"""						)]

					__SCREAMING_SNAKE_CASE	:		int   	=		source_lang
					__SCREAMING_SNAKE_CASE	:		int   	=		target_lang
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[source_spm, target_spm]

					# load SentencePiece model for pre-processing
					__SCREAMING_SNAKE_CASE	:		int   	=		load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.spm_source
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					try:
							from sacremoses import MosesPunctNormalizer

							__SCREAMING_SNAKE_CASE	:		int   	=		MosesPunctNormalizer(self.source_lang						).normalize
					except (ImportError, FileNotFoundError):
							warnings.warn("""Recommended: pip install sacremoses."""						)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		lambda lowerCAmelCase__						: x

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					return self.punc_normalizer(lowerCAmelCase__						) if x else ""

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					return self.current_encoder.get(lowerCAmelCase__							,					self.current_encoder[self.unk_token]						)

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.language_code_re.match(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		list   	=		[match.group(0						)] if match else []
					return code, self.language_code_re.sub(""""""							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.remove_language_code(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.current_spm.encode(lowerCAmelCase__							,					out_type=lowerCAmelCase__						)
					return code + pieces

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__							,					self.unk_token						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Optional[int]							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					return super().batch_decode(lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Union[str, Any]							,					**lowerCAmelCase__					:			str						):





					"""simple docstring"""






					return super().decode(lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
					__SCREAMING_SNAKE_CASE	:		int   	=		""""""
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									out_string += sp_model.decode_pieces(lowerCAmelCase__						) + token + " "
									__SCREAMING_SNAKE_CASE	:		Any   	=		[]
							else:
									current_sub_tokens.append(lowerCAmelCase__						)
					out_string += sp_model.decode_pieces(lowerCAmelCase__						)
					return out_string.strip()

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Optional[Any]=None						):





					"""simple docstring"""






					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.spm_source
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.encoder

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.spm_target
					if self.separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.target_encoder

			@property
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					return len(self.encoder						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[str] = None						):





					"""simple docstring"""






					if not os.path.isdir(lowerCAmelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
							return
					__SCREAMING_SNAKE_CASE	:		int   	=		[]

					if self.separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]							,					)
							__SCREAMING_SNAKE_CASE	:		int   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]							,					)
							save_json(self.encoder							,					lowerCAmelCase__						)
							save_json(self.target_encoder							,					lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]						)
							save_json(self.encoder							,					lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]							,					self.spm_files							,					[self.spm_source, self.spm_target]							,					):
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename						)
							if os.path.abspath(lowerCAmelCase__						) != os.path.abspath(lowerCAmelCase__						) and os.path.isfile(lowerCAmelCase__						):
									copyfile(lowerCAmelCase__							,					lowerCAmelCase__						)
									saved_files.append(lowerCAmelCase__						)
							elif not os.path.isfile(lowerCAmelCase__						):
									with open(lowerCAmelCase__							,					"""wb"""						) as fi:
											__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		spm_model.serialized_model_proto()
											fi.write(lowerCAmelCase__						)
									saved_files.append(lowerCAmelCase__						)

					return tuple(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					return self.get_src_vocab()

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					return dict(self.encoder							,					**self.added_tokens_encoder						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					return dict(self.target_encoder							,					**self.added_tokens_decoder						)

			def __getstate__(							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.__dict__.copy()
					state.update(
					    {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}						)
					return state

			def __setstate__(							self					:			Any							,					lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		d

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

					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		(load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						) for f in self.spm_files)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.spm_source
					self._setup_normalizer()

			def UpperCamelCase__ (							self					:			Tuple							,					*lowerCAmelCase__					:			Optional[int]							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					return 1

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		set(self.all_special_ids						)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]



			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List							,					lowerCAmelCase__					:			Optional[List] = None							,					lowerCAmelCase__					:			bool = False						):





					"""simple docstring"""






					if already_has_special_tokens:
							return self._special_token_mask(lowerCAmelCase__						)
					elif token_ids_a is None:
							return self._special_token_mask(lowerCAmelCase__						) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a						) + [1]


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       Dict[str, Any] ):
		__SCREAMING_SNAKE_CASE	:		str   	=		sentencepiece.SentencePieceProcessor(**_lowerCamelCase )
		spm.Load(_lowerCamelCase )
		return spm


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       str ):
		with open(_lowerCamelCase	,				"""w""" ) as f:
				json.dump(_lowerCamelCase	,				_lowerCamelCase	,				indent=2 )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase	,				"""r""" ) as f:
				return json.load(_lowerCamelCase )




'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()