


"""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


lowerCAmelCase__				    =       logging.get_logger(__name__)
lowerCAmelCase__				    =       {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__				    =       {
    '''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''',
    }
}

lowerCAmelCase__				    =       {
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}


class 				SCREAMING_SNAKE_CASE__    (		A__    ):
							"""simple docstring"""


							a  :    str       =VOCAB_FILES_NAMES
							a  :    List[str]       =PRETRAINED_VOCAB_FILES_MAP
							a  :    int       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a  :    str       =["input_ids", "attention_mask"]

							def __init__(						self		,					snake_case__		,					snake_case__=False		,					snake_case__=False		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__ = None		,					**snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[Any]								=							{} if sp_model_kwargs is None else sp_model_kwargs

									lowerCAmelCase					:					Any								=							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					:					str								=							"""None"""

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

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

									lowerCAmelCase					:					List[Any]								=							do_lower_case
									lowerCAmelCase					:					Dict								=							remove_space
									lowerCAmelCase					:					Optional[int]								=							keep_accents
									lowerCAmelCase					:					int								=							vocab_file

									lowerCAmelCase					:					int								=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(__A						)

									# Used for whitespace normalization in input texts
									# fmt : off
									lowerCAmelCase					:					List[str]								=							{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
									# fmt : on

									# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
									lowerCAmelCase					:					int								=							re.compile(
									    f"""[{"".join(map(__A		,					list(range(0		,					9						)						) + list(range(11		,					32						)						) + list(range(127		,					160						)						) + [160, 173, 8_203]						)						)}]"""						)

							def __getstate__(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Dict								=							self.__dict__.copy()
									lowerCAmelCase					:					str								=							None
									return state

							def __setstate__(						self		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							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						)

							@property
							# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
							def 	lowercase__							(						self						):






									"""simple docstring"""





									return len(self.sp_model						)

							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							self.non_printing_characters_re.sub(""		,					__A						)

									# Normalize whitespaces
									lowerCAmelCase					:					str								=							"""""".join([char if char not in self.whitespaces else " " for char in text]						)

									# NFC Unicode normalization
									lowerCAmelCase					:					Union[str, Any]								=							unicodedata.normalize("NFC"		,					__A						)
									return text

							def 	lowercase__							(						self		,					snake_case__		,					**snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Dict								=							self.preprocess_text(__A						)
									return self.sp_model.encode(__A		,					out_type=__A						)

							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									return self.sp_model.PieceToId(__A						)

							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									return self.sp_model.IdToPiece(__A						)

							@staticmethod
							def 	lowercase__							(						snake_case__						):






									"""simple docstring"""





									return out_string

							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							[]
									lowerCAmelCase					:					int								=							""""""
									lowerCAmelCase					:					List[Any]								=							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(__A						) + token
													lowerCAmelCase					:					Tuple								=							True
													lowerCAmelCase					:					Optional[int]								=							[]
											else:
													current_sub_tokens.append(__A						)
													lowerCAmelCase					:					str								=							False
									out_string += self.sp_model.decode(__A						)

									return out_string

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							{self.convert_ids_to_tokens(__A						): i for i in range(self.vocab_size						)}
									vocab.update(self.added_tokens_encoder						)
									return vocab

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






									"""simple docstring"""





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

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file		,					__A						)
									elif not os.path.isfile(self.vocab_file						):
											with open(__A		,					"wb"						) as fi:
													lowerCAmelCase					:					str								=							self.sp_model.serialized_model_proto()
													fi.write(__A						)

									return (out_vocab_file,)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = False						):






									"""simple docstring"""





									if isinstance(__A		,					__A						):
											lowerCAmelCase					:					Dict								=							self.preprocess_text(__A						)
											lowerCAmelCase					:					int								=							self.sp_model.encode(__A						)
									else:
											lowerCAmelCase					:					List[str]								=							[self.preprocess_text(__A						) for t in text]
											lowerCAmelCase					:					List[Any]								=							self.sp_model.encode(__A						)

									if return_tensors is True or return_tensors == "pt":
											lowerCAmelCase					:					Dict								=							torch.tensor(__A						)

									return token_ids

							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									return self.sp_model.decode(__A						)







							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					str								=							[f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
									lowerCAmelCase					:					Optional[Any]								=							(
									    f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__A						) + f"""{self.bos_token}Bot:"""
									)
									return self.encode(text=__A						)









"""simple docstring"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							len(SCREAMING_SNAKE_CASE    )
  for i in range(SCREAMING_SNAKE_CASE    ):
    for j in range(i + 1    ,				SCREAMING_SNAKE_CASE    ):
      if numbers[j] < numbers[i]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


if __name__ == "__main__":
     lowerCAmelCase__				    =       input('''Enter numbers separated by a comma:\n''').strip()
     lowerCAmelCase__				    =       [int(item) for item in user_input.split(''',''')]
     print(exchange_sort(unsorted))





