
"""simple docstring"""

import numpy as np
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Tuple		=    n_draws * class_counts / class_counts.sum()
		# floored means we don't overshoot n_samples, but probably undershoot
		lowerCamelCase     :			Dict		=    np.floor(a_			)
		# we add samples according to how much "left over" probability
		# they had, until we arrive at n_samples
		lowerCamelCase     :			Optional[int]		=    int(n_draws - floored.sum()			)
		if need_to_add > 0:
				lowerCamelCase     :			List[str]		=    continuous - floored
				lowerCamelCase     :			Union[str, Any]		=    np.sort(np.unique(a_			)			)[::-1]
				# add according to remainder, but break ties
				# randomly to avoid biases
				for value in values:
						((lowerCamelCase)	,    )     :			str		=    np.where(remainder == value			)
						# if we need_to_add less than what's in inds
						# we draw randomly from them.
						# if we need to add more, we add them all and
						# go to the next value
						lowerCamelCase     :			Tuple		=    min(len(a_			),						a_			)
						lowerCamelCase     :			Any		=    rng.choice(a_,						size=a_,						replace=a_			)
						floored[inds] += 1
						need_to_add -= add_now
						if need_to_add == 0:
								break
		return floored.astype(np.intaa			)





def 		UpperCAmelCase					(		a_,						a_,						a_,						a_,						a_=10			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase     :			Any		=    np.unique(a_,						return_inverse=a_			)
		lowerCamelCase     :			Dict		=    classes.shape[0]
		lowerCamelCase     :			Tuple		=    np.bincount(a_			)
		if np.min(a_			) < 2:
				raise ValueError('Minimum class count error'			)
		if n_train < n_classes:
				raise ValueError(
				    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
		if n_test < n_classes:
				raise ValueError(
				    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
		lowerCamelCase     :			List[str]		=    np.split(np.argsort(a_,						kind='mergesort'			),						np.cumsum(a_			)[:-1]			)
		for _ in range(a_			):
				lowerCamelCase     :			str		=    approximate_mode(a_,						a_,						a_			)
				lowerCamelCase     :			Optional[Any]		=    class_counts - n_i
				lowerCamelCase     :			Dict		=    approximate_mode(a_,						a_,						a_			)

				lowerCamelCase     :			Tuple		=    []
				lowerCamelCase     :			Optional[int]		=    []

				for i in range(a_			):
						lowerCamelCase     :			int		=    rng.permutation(class_counts[i]			)
						lowerCamelCase     :			Optional[Any]		=    class_indices[i].take(a_,						mode='clip'			)
						train.extend(perm_indices_class_i[: n_i[i]]			)
						test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
				lowerCamelCase     :			int		=    rng.permutation(a_			)
				lowerCamelCase     :			List[Any]		=    rng.permutation(a_			)

				yield train, test












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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowercase_		=							{
    '''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'''
        )
    },
}

lowercase_		=							{'''Helsinki-NLP/opus-mt-en-de''': 512}
lowercase_		=							{}





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: int				    =VOCAB_FILES_NAMES
	lowerCamelCase__		: Any				    =PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__		: Optional[Any]				    =PRETRAINED_INIT_CONFIGURATION
	lowerCamelCase__		: Any				    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__		: Optional[int]				    =["input_ids", "attention_mask"]
	lowerCamelCase__		: Tuple				    =re.compile(">>.+<<" )  # type: re.Pattern






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase="<unk>"				,     lowerCamelCase="</s>"				,     lowerCamelCase="<pad>"				,     lowerCamelCase=512				,     lowerCamelCase = None				,     lowerCamelCase=False				,     **lowerCamelCase				,     )      ->   None:




						"""simple docstring"""






						__magic_name__					:							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}'''

						__magic_name__					:							Optional[Any]		   =							separate_vocabs
						__magic_name__					:							int		   =							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:
											__magic_name__					:							int		   =							load_json(lowerCamelCase						)
											__magic_name__					:							Tuple		   =							{v: k for k, v in self.target_encoder.items()}
											__magic_name__					:							Optional[Any]		   =							[]
						else:
											__magic_name__					:							Dict		   =							{v: k for k, v in self.encoder.items()}
											__magic_name__					:							list		   =							[k for k in self.encoder if k.startswith('''>>'''						) and k.endswith('''<<'''						)]

						__magic_name__					:							Optional[int]		   =							source_lang
						__magic_name__					:							List[Any]		   =							target_lang
						__magic_name__					:							Tuple		   =							[source_spm, target_spm]

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

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

						self._setup_normalizer()






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




						"""simple docstring"""






						try:
											from sacremoses import MosesPunctNormalizer

											__magic_name__					:							List[str]		   =							MosesPunctNormalizer(self.source_lang						).normalize
						except (ImportError, FileNotFoundError):
											warnings.warn('''Recommended: pip install sacremoses.'''						)
											__magic_name__					:							Dict		   =							lambda lowerCamelCase						: x






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.language_code_re.match(lowerCamelCase						)
						__magic_name__					:							list		   =							[match.group(0						)] if match else []
						return code, self.language_code_re.sub(''''''				,     lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							Tuple		   =							self.remove_language_code(lowerCamelCase						)
						__magic_name__					:							Any		   =							self.current_spm.encode(lowerCamelCase				,     out_type=lowerCamelCase						)
						return code + pieces






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase				,     **lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase				,     **lowerCamelCase						)      ->   Any:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.spm_source if self._decode_use_source_tokenizer else self.spm_target
						__magic_name__					:							List[str]		   =							[]
						__magic_name__					:							str		   =							''''''
						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 + " "
																__magic_name__					:							Dict		   =							[]
											else:
																current_sub_tokens.append(lowerCamelCase						)
						out_string += sp_model.decode_pieces(lowerCamelCase						)
						return out_string.strip()






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   List[int]:




						"""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  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.spm_source
						__magic_name__					:							Tuple		   =							self.encoder






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




						"""simple docstring"""






						__magic_name__					:							int		   =							self.spm_target
						if self.separate_vocabs:
											__magic_name__					:							List[str]		   =							self.target_encoder






	@property
	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return len(self.encoder						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None						)      ->   Tuple[str]:




						"""simple docstring"""






						if not os.path.isdir(lowerCamelCase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
						__magic_name__					:							Any		   =							[]

						if self.separate_vocabs:
											__magic_name__					:							Optional[Any]		   =							os.path.join(
											    lowerCamelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']				,     )
											__magic_name__					:							Dict		   =							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:
											__magic_name__					:							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]				,     ):
											__magic_name__					:							List[Any]		   =							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:
																					__magic_name__					:							int		   =							spm_model.serialized_model_proto()
																					fi.write(lowerCamelCase						)
																saved_files.append(lowerCamelCase						)

						return tuple(lowerCamelCase						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						return self.get_src_vocab()






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






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






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




						"""simple docstring"""






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






	def __getstate__(	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							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				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							d

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

						__magic_name__   , __magic_name__					:							Optional[int]		   =							(load_spm(lowerCamelCase				,     self.sp_model_kwargs						) for f in self.spm_files)
						__magic_name__					:							str		   =							self.spm_source
						self._setup_normalizer()






	def  lowercase      (	self				,     *lowerCamelCase				,     **lowerCamelCase						)      ->   Optional[Any]:




						"""simple docstring"""






						return 1






	def  lowercase      (	self				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							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  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = False						)      ->   List[int]:




						"""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  (	UpperCAmelCase,	UpperCAmelCase       )  ->sentencepiece.SentencePieceProcessor:






					"""simple docstring"""






					__magic_name__					:							int		   =							sentencepiece.SentencePieceProcessor(**UpperCAmelCase       )
					spm.Load(UpperCAmelCase       )
					return spm







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->None:






					"""simple docstring"""






					with open(UpperCAmelCase,	'''w'''       ) as f:
										json.dump(UpperCAmelCase,	UpperCAmelCase,	indent=2       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->Union[Dict, List]:






					"""simple docstring"""






					with open(UpperCAmelCase,	'''r'''       ) as f:
										return json.load(UpperCAmelCase       )



