




'''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


_lowerCamelCase							:						List[Any]								=  logging.get_logger(__name__)

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

_lowerCamelCase							:						Any								=  {
    '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'
        )
    },
}

_lowerCamelCase							:						List[Any]								=  {'Helsinki-NLP/opus-mt-en-de': 512}
_lowerCamelCase							:						Any								=  {}





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = VOCAB_FILES_NAMES
					lowercase__    = PRETRAINED_VOCAB_FILES_MAP
					lowercase__    = PRETRAINED_INIT_CONFIGURATION
					lowercase__    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__    = ["""input_ids""", """attention_mask"""]
					lowercase__    = re.compile(""">>.+<<"""							)  # type: re.Pattern






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase="<unk>",  __UpperCamelCase="</s>",  __UpperCamelCase="<pad>",  __UpperCamelCase=512,  __UpperCamelCase = None,  __UpperCamelCase=False,  **__UpperCamelCase,  ):
							_snake_case	       =       {} 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=__UpperCamelCase,  target_lang=__UpperCamelCase,  unk_token=__UpperCamelCase,  eos_token=__UpperCamelCase,  pad_token=__UpperCamelCase,  model_max_length=__UpperCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  target_vocab_file=__UpperCamelCase,  separate_vocabs=__UpperCamelCase,  **__UpperCamelCase,  )
							assert Path(__UpperCamelCase ).exists(), f'''cannot find spm source {source_spm}'''

							_snake_case	       =       separate_vocabs
							_snake_case	       =       load_json(__UpperCamelCase )
							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:
									_snake_case	       =       load_json(__UpperCamelCase )
									_snake_case	       =       {v: k for k, v in self.target_encoder.items()}
									_snake_case	       =       []
							else:
									_snake_case	       =       {v: k for k, v in self.encoder.items()}
									_snake_case	       =       [k for k in self.encoder if k.startswith(">>" ) and k.endswith("<<" )]

							_snake_case	       =       source_lang
							_snake_case	       =       target_lang
							_snake_case	       =       [source_spm, target_spm]

							# load SentencePiece model for pre-processing
							_snake_case	       =       load_spm(__UpperCamelCase,  self.sp_model_kwargs )
							_snake_case	       =       load_spm(__UpperCamelCase,  self.sp_model_kwargs )
							_snake_case	       =       self.spm_source
							_snake_case	       =       self.encoder

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

							self._setup_normalizer()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							try:
									from sacremoses import MosesPunctNormalizer

									_snake_case	       =       MosesPunctNormalizer(self.source_lang ).normalize
							except (ImportError, FileNotFoundError):
									warnings.warn("Recommended: pip install sacremoses." )
									_snake_case	       =       lambda __UpperCamelCase : x






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.punc_normalizer(__UpperCamelCase ) if x else ""






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.current_encoder.get(__UpperCamelCase,  self.current_encoder[self.unk_token] )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       self.language_code_re.match(__UpperCamelCase )
							_snake_case	       =       [match.group(0 )] if match else []
							return code, self.language_code_re.sub("",  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case       ,    _snake_case	       =       self.remove_language_code(__UpperCamelCase )
							_snake_case	       =       self.current_spm.encode(__UpperCamelCase,  out_type=__UpperCamelCase )
							return code + pieces






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.decoder.get(__UpperCamelCase,  self.unk_token )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  **__UpperCamelCase ):
							return super().batch_decode(__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  **__UpperCamelCase ):
							return super().decode(__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							_snake_case	       =       []
							_snake_case	       =       ""
							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(__UpperCamelCase ) + token + " "
											_snake_case	       =       []
									else:
											current_sub_tokens.append(__UpperCamelCase )
							out_string += sp_model.decode_pieces(__UpperCamelCase )
							return out_string.strip()






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase=None ):
							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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.spm_source
							_snake_case	       =       self.encoder






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.spm_target
							if self.separate_vocabs:
									_snake_case	       =       self.target_encoder






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return len(self.encoder )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							if not os.path.isdir(__UpperCamelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
							_snake_case	       =       []

							if self.separate_vocabs:
									_snake_case	       =       os.path.join(
									    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],  )
									_snake_case	       =       os.path.join(
									    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],  )
									save_json(self.encoder,  __UpperCamelCase )
									save_json(self.target_encoder,  __UpperCamelCase )
									saved_files.append(__UpperCamelCase )
									saved_files.append(__UpperCamelCase )
							else:
									_snake_case	       =       os.path.join(
									    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] )
									save_json(self.encoder,  __UpperCamelCase )
									saved_files.append(__UpperCamelCase )

							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],  ):
									_snake_case	       =       os.path.join(
									    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + spm_save_filename )
									if os.path.abspath(__UpperCamelCase ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(__UpperCamelCase ):
											copyfile(__UpperCamelCase,  __UpperCamelCase )
											saved_files.append(__UpperCamelCase )
									elif not os.path.isfile(__UpperCamelCase ):
											with open(__UpperCamelCase,  "wb" ) as fi:
													_snake_case	       =       spm_model.serialized_model_proto()
													fi.write(__UpperCamelCase )
											saved_files.append(__UpperCamelCase )

							return tuple(__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return self.get_src_vocab()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return dict(self.encoder,  **self.added_tokens_encoder )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return dict(self.target_encoder,  **self.added_tokens_decoder )






					def __getstate__(    self ):
							_snake_case	       =       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,  __UpperCamelCase ):
							_snake_case	       =       d

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

							_snake_case       ,    _snake_case	       =       (load_spm(__UpperCamelCase,  self.sp_model_kwargs ) for f in self.spm_files)
							_snake_case	       =       self.spm_source
							self._setup_normalizer()






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return 1






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = False ):
							if already_has_special_tokens:
									return self._special_token_mask(__UpperCamelCase )
							elif token_ids_a is None:
									return self._special_token_mask(__UpperCamelCase ) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       sentencepiece.SentencePieceProcessor(**lowercase_  )
		spm.Load(lowercase_  )
		return spm






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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











'''simple docstring'''







from functools import lru_cache






@lru_cache
def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if not isinstance(lowercase_						,							lowercase_  ):
				raise TypeError("k must be an integer."  )
		if k < 0:
				raise ValueError("k integer must be greater or equal to zero."  )
		if k == 0:
				return 0
		elif k == 1:
				return 1
		else:
				return fibonacci(k - 1  ) + fibonacci(k - 2  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       len(lowercase_  )
		# Find m such that F_m >= n where F_i is the i_th fibonacci number.
		_snake_case	       =       0
		while True:
				if fibonacci(lowercase_  ) >= len_list:
						_snake_case	       =       i
						break
				i += 1
		_snake_case	       =       0
		while fibb_k > 0:
				_snake_case	       =       min(
				    offset + fibonacci(fibb_k - 1  )						,							len_list - 1  )  # Prevent out of range
				_snake_case	       =       arr[index_k]
				if item_k_1 == val:
						return index_k
				elif val < item_k_1:
						fibb_k -= 1
				elif val > item_k_1:
						offset += fibonacci(fibb_k - 1  )
						fibb_k -= 2
		else:
				return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()





