






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

__snake_case    :    int         =     logging.get_logger(__name__)


__snake_case    :    Optional[int]         =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    :    int         =     {
    """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"""
    },
}

__snake_case    :    Any         =     {"""facebook/blenderbot-3B""": 1_28}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       str   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					['''input_ids''', '''attention_mask''']
						_SCREAMING_SNAKE_CASE	:       str   =					BlenderbotTokenizer






						def __init__(	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase="replace"     ,						_UpperCamelCase="<s>"     ,						_UpperCamelCase="</s>"     ,						_UpperCamelCase="</s>"     ,						_UpperCamelCase="<s>"     ,						_UpperCamelCase="<unk>"     ,						_UpperCamelCase="<pad>"     ,						_UpperCamelCase="<mask>"     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						tokenizer_file=_UpperCamelCase     ,						errors=_UpperCamelCase     ,						bos_token=_UpperCamelCase     ,						eos_token=_UpperCamelCase     ,						sep_token=_UpperCamelCase     ,						cls_token=_UpperCamelCase     ,						unk_token=_UpperCamelCase     ,						pad_token=_UpperCamelCase     ,						mask_token=_UpperCamelCase     ,						add_prefix_space=_UpperCamelCase     ,						trim_offsets=_UpperCamelCase     ,						**_UpperCamelCase     ,						)

												lowerCAmelCase__    						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'     ,						_UpperCamelCase   ) != add_prefix_space:
																		lowerCAmelCase__    						=						getattr(_UpperCamelCase     ,						pre_tok_state.pop('type'   )   )
																		lowerCAmelCase__    						=						add_prefix_space
																		lowerCAmelCase__    						=						pre_tok_class(**_UpperCamelCase   )

												lowerCAmelCase__    						=						add_prefix_space

												lowerCAmelCase__    						=						'post_processor'
												lowerCAmelCase__    						=						getattr(self.backend_tokenizer     ,						_UpperCamelCase     ,						_UpperCamelCase   )
												if tokenizer_component_instance:
																		lowerCAmelCase__    						=						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(state['sep']   )
																		if "cls" in state:
																								lowerCAmelCase__    						=						tuple(state['cls']   )

																		lowerCAmelCase__    						=						False

																		if state.get('add_prefix_space'     ,						_UpperCamelCase   ) != add_prefix_space:
																								lowerCAmelCase__    						=						add_prefix_space
																								lowerCAmelCase__    						=						True

																		if state.get('trim_offsets'     ,						_UpperCamelCase   ) != trim_offsets:
																								lowerCAmelCase__    						=						trim_offsets
																								lowerCAmelCase__    						=						True

																		if changes_to_apply:
																								lowerCAmelCase__    						=						getattr(_UpperCamelCase     ,						state.pop('type'   )   )
																								lowerCAmelCase__    						=						component_class(**_UpperCamelCase   )
																								setattr(self.backend_tokenizer     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						@property
						# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						AddedToken(_UpperCamelCase     ,						lstrip=_UpperCamelCase     ,						rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ) else value
												lowerCAmelCase__    						=						value






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )
												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(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )

												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(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._tokenizer.model.save(_UpperCamelCase     ,						name=_UpperCamelCase   )
												return tuple(_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[self.sep_token_id]
												lowerCAmelCase__    						=						[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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												return token_ids_a + [self.eos_token_id]







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[]
												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(_UpperCamelCase   )

												lowerCAmelCase__    						=						'  '.join(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.encode(_UpperCamelCase   )
												if len(_UpperCamelCase   ) > self.model_max_length:
																		lowerCAmelCase__    						=						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













__snake_case    :    Tuple         =     range(2, 20 + 1)
__snake_case    :    str         =     [10**k for k in range(ks[-1] + 1)]
__snake_case    :    dict[int, dict[int, list[list[int]]]]         =     {}







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Tuple		)  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						sum(a_i[j] for j in range(UpperCamelCase_    ,       len(UpperCamelCase_		)		)		)
						lowerCAmelCase__    						=						sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase_		)    ,       UpperCamelCase_		)		)		)

						lowerCAmelCase__						,       lowerCAmelCase__    						=						0, 0
						lowerCAmelCase__    						=						n - i

						lowerCAmelCase__    						=						memo.get(UpperCamelCase_		)

						if sub_memo is not None:
												lowerCAmelCase__    						=						sub_memo.get(UpperCamelCase_		)

												if jumps is not None and len(UpperCamelCase_		) > 0:
																		# find and make the largest jump without going over
																		lowerCAmelCase__    						=						-1
																		for _k in range(len(UpperCamelCase_		) - 1    ,       -1    ,       -1		):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														lowerCAmelCase__    						=						_k
																														break

																		if max_jump >= 0:
																								lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								lowerCAmelCase__    						=						diff + c
																								for j in range(min(UpperCamelCase_    ,       len(UpperCamelCase_		)		)		):
																														lowerCAmelCase__						,       lowerCAmelCase__    						=						divmod(UpperCamelCase_    ,       10		)
																								if new_c > 0:
																														add(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

												else:
																		lowerCAmelCase__    						=						[]
						else:
												lowerCAmelCase__    						=						{c: []}
												lowerCAmelCase__    						=						sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						next_term(UpperCamelCase_    ,       k - 1    ,       i + dn    ,       UpperCamelCase_		)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												lowerCAmelCase__						,       lowerCAmelCase__    						=						compute(UpperCamelCase_    ,       UpperCamelCase_    ,       i + dn    ,       UpperCamelCase_		)
												diff += _diff
												dn += terms_jumped

						lowerCAmelCase__    						=						sub_memo[c]

						# keep jumps sorted by # of terms skipped
						lowerCAmelCase__    						=						0
						while j < len(UpperCamelCase_		):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(UpperCamelCase_    ,       (diff, dn, k)		)
						return (diff, dn)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Tuple		)  ->   List[str]:

						"""simple docstring"""



						if i >= n:
												return 0, i
						if k > len(UpperCamelCase_		):
												a_i.extend([0 for _ in range(k - len(UpperCamelCase_		)		)]		)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						lowerCAmelCase__    						=						i
						lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						0, 0, 0
						for j in range(len(UpperCamelCase_		)		):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												lowerCAmelCase__    						=						ds_c + ds_b
												diff += addend
												lowerCAmelCase__    						=						0
												for j in range(UpperCamelCase_		):
																		lowerCAmelCase__    						=						a_i[j] + addend
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						divmod(UpperCamelCase_    ,       10		)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						return diff, i - start_i







def 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					List[str]		)  ->   List[str]:

						"""simple docstring"""



						for j in range(UpperCamelCase_    ,       len(UpperCamelCase_		)		):
												lowerCAmelCase__    						=						digits[j] + addend
												if s >= 10:
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						divmod(UpperCamelCase_    ,       10		)
																		lowerCAmelCase__    						=						addend // 10 + quotient
												else:
																		lowerCAmelCase__    						=						s
																		lowerCAmelCase__    						=						addend // 10

												if addend == 0:
																		break

						while addend > 0:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						divmod(UpperCamelCase_    ,       10		)
												digits.append(UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 10**15		)  ->   int:

						"""simple docstring"""



						lowerCAmelCase__    						=						[1]
						lowerCAmelCase__    						=						1
						lowerCAmelCase__    						=						0
						while True:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						next_term(UpperCamelCase_    ,       20    ,       i + dn    ,       UpperCamelCase_		)
												dn += terms_jumped
												if dn == n - i:
																		break

						lowerCAmelCase__    						=						0
						for j in range(len(UpperCamelCase_		)		):
												a_n += digits[j] * 10**j
						return a_n


if __name__ == "__main__":
					print(f'{solution() = }')





