



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


__A				    =						logging.get_logger(__name__)

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

__A				    =						{
    '''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'''
        )
    },
}

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






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					List[Any]		   =							VOCAB_FILES_NAMES
	a__      :					Optional[int]		   =							PRETRAINED_VOCAB_FILES_MAP
	a__      :					List[str]		   =							PRETRAINED_INIT_CONFIGURATION
	a__      :					int		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__      :					Any		   =							["input_ids", "attention_mask"]
	a__      :					Dict		   =							re.compile(">>.+<<")  # type: re.Pattern




	def __init__(	self	:						int						,  __UpperCAmelCase	:						Dict						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						int						,  __UpperCAmelCase	:						List[str]=None						,  __UpperCAmelCase	:						str=None						,  __UpperCAmelCase	:						List[Any]=None						,  __UpperCAmelCase	:						str="<unk>"						,  __UpperCAmelCase	:						Optional[int]="</s>"						,  __UpperCAmelCase	:						Optional[int]="<pad>"						,  __UpperCAmelCase	:						Optional[Any]=512						,  __UpperCAmelCase	:						Optional[Dict[str, Any]] = None						,  __UpperCAmelCase	:						Optional[Any]=False						,  **__UpperCAmelCase	:						str						,  )			-> None:
							UpperCAmelCase_=   {} 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}"""

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

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

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

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

							self._setup_normalizer()




	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> Any:
							try:
													from sacremoses import MosesPunctNormalizer

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						int						,  __UpperCAmelCase	:						str     )			-> str:
							return self.punc_normalizer(__UpperCAmelCase     ) if x else ""




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict						,  __UpperCAmelCase	:						Tuple     )			-> Optional[Any]:
							return self.current_encoder.get(__UpperCAmelCase						,  self.current_encoder[self.unk_token]     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict						,  __UpperCAmelCase	:						str     )			-> List[Any]:
							UpperCAmelCase_=   self.language_code_re.match(__UpperCAmelCase     )
							UpperCAmelCase_=   [match.group(0     )] if match else []
							return code, self.language_code_re.sub(""""""						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple						,  __UpperCAmelCase	:						str     )			-> List[str]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.remove_language_code(__UpperCAmelCase     )
							UpperCAmelCase_=   self.current_spm.encode(__UpperCAmelCase						,  out_type=__UpperCAmelCase     )
							return code + pieces




	def   _SCREAMING_SNAKE_CASE		(	self	:						int						,  __UpperCAmelCase	:						int     )			-> str:
							return self.decoder.get(__UpperCAmelCase						,  self.unk_token     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  __UpperCAmelCase	:						str						,  **__UpperCAmelCase	:						Optional[Any]     )			-> Optional[Any]:
							return super().batch_decode(__UpperCAmelCase						,  **__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						List[str]						,  **__UpperCAmelCase	:						List[Any]     )			-> Optional[Any]:
							return super().decode(__UpperCAmelCase						,  **__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						List[str]     )			-> str:
							UpperCAmelCase_=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							UpperCAmelCase_=   []
							UpperCAmelCase_=   """"""
							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 + " "
																			UpperCAmelCase_=   []
													else:
																			current_sub_tokens.append(__UpperCAmelCase     )
							out_string += sp_model.decode_pieces(__UpperCAmelCase     )
							return out_string.strip()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						Any=None     )			-> List[int]:
							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	:						Union[str, Any]     )			-> Optional[Any]:
							UpperCAmelCase_=   self.spm_source
							UpperCAmelCase_=   self.encoder




	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> str:
							UpperCAmelCase_=   self.spm_target
							if self.separate_vocabs:
													UpperCAmelCase_=   self.target_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> int:
							return len(self.encoder     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						Optional[str] = None     )			-> Tuple[str]:
							if not os.path.isdir(__UpperCAmelCase     ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
													return
							UpperCAmelCase_=   []

							if self.separate_vocabs:
													UpperCAmelCase_=   os.path.join(
													    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]						,  )
													UpperCAmelCase_=   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:
													UpperCAmelCase_=   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]						,  ):
													UpperCAmelCase_=   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:
																									UpperCAmelCase_=   spm_model.serialized_model_proto()
																									fi.write(__UpperCAmelCase     )
																			saved_files.append(__UpperCAmelCase     )

							return tuple(__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Dict:
							return self.get_src_vocab()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Union[str, Any]:
							return dict(self.encoder						,  **self.added_tokens_encoder     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Any:
							return dict(self.target_encoder						,  **self.added_tokens_decoder     )




	def __getstate__(	self	:						str     )			-> Dict:
							UpperCAmelCase_=   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	:						Tuple						,  __UpperCAmelCase	:						Dict     )			-> None:
							UpperCAmelCase_=   d

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

							UpperCAmelCase_,   UpperCAmelCase_=   (load_spm(__UpperCAmelCase						,  self.sp_model_kwargs     ) for f in self.spm_files)
							UpperCAmelCase_=   self.spm_source
							self._setup_normalizer()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple						,  *__UpperCAmelCase	:						Any						,  **__UpperCAmelCase	:						List[str]     )			-> Dict:
							return 1




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple						,  __UpperCAmelCase	:						List[Any]     )			-> List[Any]:
							UpperCAmelCase_=   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	:						Any						,  __UpperCAmelCase	:						List						,  __UpperCAmelCase	:						Optional[List] = None						,  __UpperCAmelCase	:						bool = False     )			-> List[int]:
							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        __a     (    lowerCAmelCase_   :					str			,lowerCAmelCase_   :					Dict[str, Any]				)      -> sentencepiece.SentencePieceProcessor:






						'''simple docstring'''



						UpperCAmelCase_=   sentencepiece.SentencePieceProcessor(**lowerCAmelCase_				)
						spm.Load(lowerCAmelCase_				)
						return spm



def        __a     (    lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> None:






						'''simple docstring'''



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



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






						'''simple docstring'''



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









def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if number != int(lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if number < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						UpperCAmelCase_=   1
						for i in range(1			,number + 1				):
												value *= i
						return value



def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__A				    =						int(input('''Enter a positive integer: ''').strip() or 0)
	print(f'factorial{n} is {factorial(n)}')




