
"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

lowerCamelCase__           =	logging.get_logger(__name__)


lowerCamelCase__           =	{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase__           =	{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

lowerCamelCase__           =	{"""facebook/blenderbot-3B""": 128}




class 				A__ (			_lowerCamelCase):
		A_            =			VOCAB_FILES_NAMES
		A_            =			PRETRAINED_VOCAB_FILES_MAP
		A_            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_            =			['input_ids', 'attention_mask']
		A_            =			BlenderbotTokenizer

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="replace" ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE="<mask>" ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(
			    _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	tokenizer_file=_SCREAMING_SNAKE_CASE ,	errors=_SCREAMING_SNAKE_CASE ,	bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	sep_token=_SCREAMING_SNAKE_CASE ,	cls_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	mask_token=_SCREAMING_SNAKE_CASE ,	add_prefix_space=_SCREAMING_SNAKE_CASE ,	trim_offsets=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		List[Any]		    =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
			if pre_tok_state.get('add_prefix_space' ,	_SCREAMING_SNAKE_CASE				) != add_prefix_space:
				__lowerCAmelCase    :		List[str]		    =   getattr(_SCREAMING_SNAKE_CASE ,	pre_tok_state.pop('type'				)				)
				__lowerCAmelCase    :		Dict		    =   add_prefix_space
				__lowerCAmelCase    :		Dict		    =   pre_tok_class(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   add_prefix_space

			__lowerCAmelCase    :		Union[str, Any]		    =   'post_processor'
			__lowerCAmelCase    :		str		    =   getattr(self.backend_tokenizer ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			if tokenizer_component_instance:
				__lowerCAmelCase    :		str		    =   json.loads(tokenizer_component_instance.__getstate__()				)

				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
				if "sep" in state:
					__lowerCAmelCase    :		Tuple		    =   tuple(state['sep']				)
				if "cls" in state:
					__lowerCAmelCase    :		Optional[int]		    =   tuple(state['cls']				)

				__lowerCAmelCase    :		List[Any]		    =   False

				if state.get('add_prefix_space' ,	_SCREAMING_SNAKE_CASE				) != add_prefix_space:
					__lowerCAmelCase    :		str		    =   add_prefix_space
					__lowerCAmelCase    :		List[str]		    =   True

				if state.get('trim_offsets' ,	_SCREAMING_SNAKE_CASE				) != trim_offsets:
					__lowerCAmelCase    :		List[Any]		    =   trim_offsets
					__lowerCAmelCase    :		Any		    =   True

				if changes_to_apply:
					__lowerCAmelCase    :		List[str]		    =   getattr(_SCREAMING_SNAKE_CASE ,	state.pop('type'				)				)
					__lowerCAmelCase    :		List[str]		    =   component_class(**_SCREAMING_SNAKE_CASE				)
					setattr(self.backend_tokenizer ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@property
		# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
		def 							__lowerCamelCase							(   self				):
			if self._mask_token is None:
				if self.verbose:
					logger.error('Using mask_token, but it is not set yet.'				)
				return None
			return str(self._mask_token				)

		@mask_token.setter
		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   AddedToken(_SCREAMING_SNAKE_CASE ,	lstrip=_SCREAMING_SNAKE_CASE ,	rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) else value
			__lowerCAmelCase    :		Optional[int]		    =   value

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   kwargs.get('is_split_into_words' ,	_SCREAMING_SNAKE_CASE				)
			assert self.add_prefix_space or not is_split_into_words, (
			    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
			    "to use it with pretokenized inputs."
			)

			return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   kwargs.get('is_split_into_words' ,	_SCREAMING_SNAKE_CASE				)

			assert self.add_prefix_space or not is_split_into_words, (
			    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
			    "to use it with pretokenized inputs."
			)

			return super()._encode_plus(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		List[str]		    =   self._tokenizer.model.save(_SCREAMING_SNAKE_CASE ,	name=_SCREAMING_SNAKE_CASE				)
			return tuple(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		int		    =   [self.sep_token_id]
			__lowerCAmelCase    :		Dict		    =   [self.cls_token_id]

			if token_ids_a is None:
				return len(cls + token_ids_a + sep				) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			return token_ids_a + [self.eos_token_id]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   []
			for is_user, text in conversation.iter_texts():
				if is_user:
					# We need to space prefix as it's being done within blenderbot
					inputs.append(' ' + text				)
				else:
					# Generated responses should contain them already.
					inputs.append(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   '  '.join(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   self.encode(_SCREAMING_SNAKE_CASE				)
			if len(_SCREAMING_SNAKE_CASE				) > self.model_max_length:
				__lowerCAmelCase    :		Tuple		    =   input_ids[-self.model_max_length :]
				logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."				)
			return input_ids

"""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))