
"""simple docstring"""


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
						import torch


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


A_    							=					logging.get_logger(__name__)
A_    							=					{"vocab_file": "spiece.model"}

A_    							=					{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

A_    							=					{
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}




class  __lowerCamelCase  ( UpperCamelCase__			):
				a__:      Union[str, Any]          =    VOCAB_FILES_NAMES
				a__:      Tuple          =    PRETRAINED_VOCAB_FILES_MAP
				a__:      int          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__:      Optional[int]          =    ['input_ids', 'attention_mask']




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=False							,						UpperCAmelCase=False							,						UpperCAmelCase=False							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  {} if sp_model_kwargs is None else sp_model_kwargs

							lowerCamelCase_					=  kwargs.get('''name_or_path'''   )
							if name_or_path is None:
										logger.warning(
										    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
										    ''' you are testing the model, this can safely be ignored'''   )
										lowerCamelCase_					=  """None"""

							# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
							lowerCamelCase_					=  """<|endoftext|>""" if eos_token is None else eos_token
							lowerCamelCase_					=  """<unk>""" if unk_token is None else unk_token
							if "gpt-sw3-7b" in name_or_path:
										lowerCamelCase_					=  unk_token if pad_token is None else pad_token
										lowerCamelCase_					=  eos_token if bos_token is None else bos_token
							else:
										lowerCamelCase_					=  """<pad>""" if pad_token is None else pad_token
										lowerCamelCase_					=  """<s>""" if bos_token is None else bos_token

							super().__init__(
							    do_lower_case=__snake_case							,						remove_space=__snake_case							,						keep_accents=__snake_case							,						bos_token=__snake_case							,						eos_token=__snake_case							,						unk_token=__snake_case							,						pad_token=__snake_case							,						sp_model_kwargs=self.sp_model_kwargs							,						**__snake_case							,						)

							lowerCamelCase_					=  do_lower_case
							lowerCamelCase_					=  remove_space
							lowerCamelCase_					=  keep_accents
							lowerCamelCase_					=  vocab_file

							lowerCamelCase_					=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(__snake_case   )

							# Used for whitespace normalization in input texts
							# fmt : off
							lowerCamelCase_					=  {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							lowerCamelCase_					=  re.compile(
							    f"[{''.join(map(__snake_case							,						list(range(0							,						9   )   ) + list(range(11							,						32   )   ) + list(range(127							,						160   )   ) + [160, 173, 8203]   )   )}]"   )




				def __getstate__(				self   ):
							lowerCamelCase_					=  self.__dict__.copy()
							lowerCamelCase_					=  None
							return state




				def __setstate__(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  d

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

							lowerCamelCase_					=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )




				@property
				# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
				def        UpperCAmelCase__						(				self   ):
							return len(self.sp_model   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.non_printing_characters_re.sub(''''''							,						__snake_case   )

							# Normalize whitespaces
							lowerCamelCase_					=  """""".join([char if char not in self.whitespaces else ''' ''' for char in text]   )

							# NFC Unicode normalization
							lowerCamelCase_					=  unicodedata.normalize('''NFC'''							,						__snake_case   )
							return text




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  self.preprocess_text(__snake_case   )
							return self.sp_model.encode(__snake_case							,						out_type=__snake_case   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.sp_model.PieceToId(__snake_case   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.sp_model.IdToPiece(__snake_case   )




				@staticmethod
				def        UpperCAmelCase__						(				UpperCAmelCase   ):
							return out_string




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  []
							lowerCamelCase_					=  """"""
							lowerCamelCase_					=  False
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													if not prev_is_special:
																out_string += " "

													out_string += self.sp_model.decode(__snake_case   ) + token
													lowerCamelCase_					=  True
													lowerCamelCase_					=  []
										else:
													current_sub_tokens.append(__snake_case   )
													lowerCamelCase_					=  False
							out_string += self.sp_model.decode(__snake_case   )

							return out_string




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):

							if not os.path.isdir(__snake_case   ):
										logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
										return
							lowerCamelCase_					=  os.path.join(
							    __snake_case							,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(__snake_case   ) and os.path.isfile(self.vocab_file   ):
										copyfile(self.vocab_file							,						__snake_case   )
							elif not os.path.isfile(self.vocab_file   ):
										with open(__snake_case							,						'''wb'''   ) as fi:
													lowerCamelCase_					=  self.sp_model.serialized_model_proto()
													fi.write(__snake_case   )

							return (out_vocab_file,)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = False   ):

							if isinstance(__snake_case							,						__snake_case   ):
										lowerCamelCase_					=  self.preprocess_text(__snake_case   )
										lowerCamelCase_					=  self.sp_model.encode(__snake_case   )
							else:
										lowerCamelCase_					=  [self.preprocess_text(__snake_case   ) for t in text]
										lowerCamelCase_					=  self.sp_model.encode(__snake_case   )

							if return_tensors is True or return_tensors == "pt":
										lowerCamelCase_					=  torch.tensor(__snake_case   )

							return token_ids




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.sp_model.decode(__snake_case   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  [f"User: {text}" if is_user else f"Bot: {text}" for is_user, text in conversation.iter_texts()]
							lowerCamelCase_					=  (
							    f"{self.eos_token}{self.bos_token}" + f"{self.bos_token}".join(__snake_case   ) + f"{self.bos_token}Bot:"
							)
							return self.encode(text=__snake_case   )









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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=sum(a_i[j] for j in range(lowerCamelCase						,  len(lowerCamelCase ) ) )
				__magic_name__						:						Any										=sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )						,  lowerCamelCase ) ) )

				__magic_name__					,			__magic_name__						:						Tuple										=0, 0
				__magic_name__						:						Optional[Any]										=n - i

				__magic_name__						:						Union[str, Any]										=memo.get(lowerCamelCase )

				if sub_memo is not None:
								__magic_name__						:						int										=sub_memo.get(lowerCamelCase )

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

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

								else:
												__magic_name__						:						str										=[]
				else:
								__magic_name__						:						List[str]										={c: []}
								__magic_name__						:						List[str]										=sub_memo

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

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												__magic_name__					,			__magic_name__						:						Union[str, Any]										=next_term(lowerCamelCase						,  k - 1						,  i + dn						,  lowerCamelCase )
												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
								__magic_name__					,			__magic_name__						:						Optional[int]										=compute(lowerCamelCase						,  lowerCamelCase						,  i + dn						,  lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				__magic_name__						:						Tuple										=sub_memo[c]

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

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




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

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

				while i < n:
								i += 1
								__magic_name__						:						Optional[Any]										=ds_c + ds_b
								diff += addend
								__magic_name__						:						str										=0
								for j in range(lowerCamelCase ):
												__magic_name__						:						int										=a_i[j] + addend
												__magic_name__					,			__magic_name__						:						Any										=divmod(lowerCamelCase						,  10 )

												ds_c += a_i[j]

								if addend > 0:
												break

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				for j in range(lowerCamelCase						,  len(lowerCamelCase ) ):
								__magic_name__						:						Tuple										=digits[j] + addend
								if s >= 10:
												__magic_name__					,			__magic_name__						:						int										=divmod(lowerCamelCase						,  10 )
												__magic_name__						:						int										=addend // 10 + quotient
								else:
												__magic_name__						:						Dict										=s
												__magic_name__						:						Any										=addend // 10

								if addend == 0:
												break

				while addend > 0:
								__magic_name__					,			__magic_name__						:						Union[str, Any]										=divmod(lowerCamelCase						,  10 )
								digits.append(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase = 10**15 ):
				__magic_name__						:						List[str]										=[1]
				__magic_name__						:						str										=1
				__magic_name__						:						str										=0
				while True:
								__magic_name__					,			__magic_name__						:						List[str]										=next_term(lowerCamelCase						,  20						,  i + dn						,  lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				__magic_name__						:						int										=0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 10**j
				return a_n


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




