

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_0_4_8,
    """AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}

class     a__  (    __UpperCamelCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				VOCAB_FILES_NAMES
			__lowerCamelCase       				=				PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase       				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase       				=				['input_ids', 'attention_mask']


			def __init__(			self					,					lowercase					,					lowercase=False					,					lowercase=False					,					lowercase=False					,					lowercase=None					,					lowercase=None					,					lowercase=None					,					lowercase=None					,					lowercase = None					,					**lowercase					,					)     ->  None:
							'''simple docstring'''






							A__						=	{} if sp_model_kwargs is None else sp_model_kwargs

							A__						=	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"				)
											A__						=	'None'

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

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

							A__						=	do_lower_case
							A__						=	remove_space
							A__						=	keep_accents
							A__						=	vocab_file

							A__						=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(lowerCAmelCase__				)

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

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


			def __getstate__(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.__dict__.copy()
							A__						=	None
							return state


			def __setstate__(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






							A__						=	d

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

							A__						=	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				)     ->  int:
							'''simple docstring'''






							return len(self.sp_model				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	self.non_printing_characters_re.sub(""					,					lowerCAmelCase__				)

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

							# NFC Unicode normalization
							A__						=	unicodedata.normalize("NFC"					,					lowerCAmelCase__				)
							return text


			def    UpperCamelCase						(			self					,					lowercase					,					**lowercase				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.preprocess_text(lowerCAmelCase__				)
							return self.sp_model.encode(lowerCAmelCase__					,					out_type=lowerCAmelCase__				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






							return self.sp_model.PieceToId(lowerCAmelCase__				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							return self.sp_model.IdToPiece(lowerCAmelCase__				)


			@staticmethod
			def    UpperCamelCase						(			lowercase				)     ->  str:
							'''simple docstring'''






							return out_string


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	[]
							A__						=	''
							A__						=	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(lowerCAmelCase__				) + token
															A__						=	True
															A__						=	[]
											else:
															current_sub_tokens.append(lowerCAmelCase__				)
															A__						=	False
							out_string += self.sp_model.decode(lowerCAmelCase__				)

							return out_string


			def    UpperCamelCase						(			self				)     ->  Dict[str, int]:
							'''simple docstring'''






							A__						=	{self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None				)     ->  Tuple[str]:
							'''simple docstring'''






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

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

							return (out_vocab_file,)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = False				)     ->  Union[List[int], List[List[int]], "torch.Tensor"]:
							'''simple docstring'''






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

							if return_tensors is True or return_tensors == "pt":
											A__						=	torch.tensor(lowerCAmelCase__				)

							return token_ids


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							return self.sp_model.decode(lowerCAmelCase__				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  List[int]:
							'''simple docstring'''






							A__						=	[F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
							A__						=	(
							    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(lowerCAmelCase__				) + F'{self.bos_token}Bot:'
							)
							return self.encode(text=lowerCAmelCase__				)





from __future__ import annotations




def    snake_case_     (  snake_case			,							snake_case     )  ->				list[int]:
       lowercase__:   List[str]  			=   0
       lowercase__:   Dict  			=   len(snake_case     ) - 1

       while i < j:
              if nums[i] + nums[j] == target:
                     return [i, j]
              elif nums[i] + nums[j] < target:
                     lowercase__:   Dict  			=   i + 1
              else:
                     lowercase__:   List[Any]  			=   j - 1

       return []


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')


