
"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{"""vocab_file""": """spm_char.model"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

lowerCamelCase__           =	{
    """microsoft/speecht5_asr""": 1_024,
    """microsoft/speecht5_tts""": 1_024,
    """microsoft/speecht5_vc""": 1_024,
}




class 				A__ (			_lowerCamelCase):
		A_		: Optional[Any]            =			VOCAB_FILES_NAMES
		A_		: Dict            =			PRETRAINED_VOCAB_FILES_MAP
		A_		: Dict            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_		: int            =			['input_ids', 'attention_mask']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Optional[int]		    =   {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	sp_model_kwargs=self.sp_model_kwargs ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		List[str]		    =   vocab_file

			__lowerCAmelCase    :		List[str]		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			return self.sp_model.get_piece_size()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
			vocab.update(self.added_tokens_encoder				)
			return vocab

		def __getstate__(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.__dict__.copy()
			__lowerCAmelCase    :		Optional[int]		    =   None
			return state

		def __setstate__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[Any]		    =   d

			# for backward compatibility
			if not hasattr(self ,	'sp_model_kwargs'				):
				__lowerCAmelCase    :		Optional[Any]		    =   {}

			__lowerCAmelCase    :		int		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.Load(self.vocab_file				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,	out_type=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
			return token

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   []
			__lowerCAmelCase    :		Tuple		    =   ''
			for token in tokens:
				# make sure that special tokens are not decoded using sentencepiece model
				if token in self.all_special_tokens:
					out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
					__lowerCAmelCase    :		Optional[Any]		    =   []
				else:
					current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
			out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
			return out_string.strip()

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False				):
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_SCREAMING_SNAKE_CASE ,	token_ids_a=_SCREAMING_SNAKE_CASE ,	already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   [1]
			if token_ids_a is None:
				return ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones
			return ([0] * len(_SCREAMING_SNAKE_CASE				)) + ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones







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

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

			return (out_vocab_file,)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase	):
	if n_term == "":
		return []
	__lowerCAmelCase    :		list		    =   []
	for temp in range(int(_UpperCamelCase	)	):
		series.append(F"1/{temp + 1}" if series else '1'	)
	return series


if __name__ == "__main__":
							lowerCamelCase__           =	input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))