



'''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


snake_case_ :	Any         =  logging.get_logger(__name__)
snake_case_ :	Tuple         =  {'vocab_file': 'spiece.model'}

snake_case_ :	Tuple         =  {
    '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',
    }
}

snake_case_ :	Union[str, Any]         =  {
    '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 							lowercase__	(							lowercase   ):
	lowercase__  				=			VOCAB_FILES_NAMES
	lowercase__  				=			PRETRAINED_VOCAB_FILES_MAP
	lowercase__  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__  				=			["""input_ids""", """attention_mask"""]





	def __init__( self					:				List[Any] ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Tuple=False ,lowerCamelCase__					:				List[Any]=False ,lowerCamelCase__					:				Any=False ,lowerCamelCase__					:				Union[str, Any]=None ,lowerCamelCase__					:				Optional[int]=None ,lowerCamelCase__					:				Optional[Any]=None ,lowerCamelCase__					:				Tuple=None ,lowerCamelCase__					:				Optional[Dict[str, Any]] = None ,**lowerCamelCase__					:				List[Any] ,):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   {} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCamelCase					:		int 				=   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' )
													_UpperCamelCase					:		str 				=   'None'

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

							_UpperCamelCase					:		int 				=   do_lower_case
							_UpperCamelCase					:		Tuple 				=   remove_space
							_UpperCamelCase					:		int 				=   keep_accents
							_UpperCamelCase					:		Union[str, Any] 				=   vocab_file

							_UpperCamelCase					:		List[str] 				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(lowerCamelCase__ )

							# Used for whitespace normalization in input texts
							# fmt : off
							_UpperCamelCase					:		List[Any] 				=   {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
							# fmt : on

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





	def __getstate__( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.__dict__.copy()
							_UpperCamelCase					:		List[str] 				=   None
							return state





	def __setstate__( self					:				Dict ,lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   d

							# for backward compatibility
							if not hasattr(self ,'sp_model_kwargs' ):
													_UpperCamelCase					:		List[str] 				=   {}

							_UpperCamelCase					:		Tuple 				=   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					:				Tuple ):







							'''simple docstring'''
							return len(self.sp_model )





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   self.non_printing_characters_re.sub('' ,lowerCamelCase__ )

							# Normalize whitespaces
							_UpperCamelCase					:		List[Any] 				=   ''.join([char if char not in self.whitespaces else ' ' for char in text] )

							# NFC Unicode normalization
							_UpperCamelCase					:		Union[str, Any] 				=   unicodedata.normalize('NFC' ,lowerCamelCase__ )
							return text





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				str ,**lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   self.preprocess_text(lowerCamelCase__ )
							return self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							return self.sp_model.PieceToId(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							return self.sp_model.IdToPiece(lowerCamelCase__ )





	@staticmethod
	def 	UpperCamelCase_  ( lowerCamelCase__					:				str ):







							'''simple docstring'''
							return out_string





	def 	UpperCamelCase_  ( self					:				str ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   []
							_UpperCamelCase					:		Optional[int] 				=   ''
							_UpperCamelCase					:		Optional[int] 				=   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
																			_UpperCamelCase					:		Optional[Any] 				=   True
																			_UpperCamelCase					:		Union[str, Any] 				=   []
													else:
																			current_sub_tokens.append(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   False
							out_string += self.sp_model.decode(lowerCamelCase__ )

							return out_string





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   {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					:				str ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Optional[str] = None ):







							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__ ):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
													return
							_UpperCamelCase					:		Dict 				=   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:
																			_UpperCamelCase					:		str 				=   self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__ )

							return (out_vocab_file,)





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				Union[str, List[str]] ,lowerCamelCase__					:				Union[str, bool] = False ):







							'''simple docstring'''

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

							if return_tensors is True or return_tensors == "pt":
													_UpperCamelCase					:		Dict 				=   torch.tensor(lowerCamelCase__ )

							return token_ids





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Union[int, List[int]] ):







							'''simple docstring'''
							return self.sp_model.decode(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				"Conversation" ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
							_UpperCamelCase					:		List[Any] 				=   (
							    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(lowerCamelCase__ ) + F'{self.bos_token}Bot:'
							)
							return self.encode(text=lowerCamelCase__ )






"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list )	->						int:


    """simple docstring"""





    if not grid or not grid[0]:
        raise TypeError("The grid does not contain the appropriate information" )

    for cell_n in range(1						,							len(grid[0] ) ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    snake_case    :      int      					=    grid[0]

    for row_n in range(1						,							len(lowercase ) ):
        snake_case    :      Optional[Any]      					=    grid[row_n]
        snake_case    :      List[str]      					=    fill_row(lowercase						,							lowercase )
        snake_case    :      Optional[int]      					=    grid[row_n]

    return grid[-1][-1]







def   __lowerCAmelCase						(						lowercase  :  list						,							lowercase  :  list )	->						list:


    """simple docstring"""





    current_row[0] += row_above[0]
    for cell_n in range(1						,							len(lowercase ) ):
        current_row[cell_n] += min(current_row[cell_n - 1]						,							row_above[cell_n] )

    return current_row


if __name__ == "__main__":
  import doctest

  doctest.testmod()





