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


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

__UpperCamelCase	: Optional[Any]    		=  {
    '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',
    }
}

__UpperCamelCase	: Tuple    		=  {
    '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__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							{} if sp_model_kwargs is None else sp_model_kwargs

												SCREAMING_SNAKE_CASE							:					Optional[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'''		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							'''None'''

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

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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							do_lower_case
												SCREAMING_SNAKE_CASE							:					List[str]           =							remove_space
												SCREAMING_SNAKE_CASE							:					Dict           =							keep_accents
												SCREAMING_SNAKE_CASE							:					List[str]           =							vocab_file

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(UpperCamelCase__		)

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

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


						def __getstate__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.__dict__.copy()
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							None
												return state


						def __setstate__( self			:       Optional[int]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							d

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

												SCREAMING_SNAKE_CASE							:					Any           =							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  __A ( self			:       Dict		):





												'''simple docstring'''





												return len(self.sp_model		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.non_printing_characters_re.sub(''''''					,	UpperCamelCase__		)

												# Normalize whitespaces
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]		)

												# NFC Unicode normalization
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							unicodedata.normalize('''NFC'''					,	UpperCamelCase__		)
												return text


						def  __A ( self			:       str					,	UpperCamelCase__			:       str					,	**UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.preprocess_text(UpperCamelCase__		)
												return self.sp_model.encode(UpperCamelCase__					,	out_type=UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												return self.sp_model.PieceToId(UpperCamelCase__		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												return self.sp_model.IdToPiece(UpperCamelCase__		)


						@staticmethod
						def  __A ( UpperCamelCase__			:       str		):





												'''simple docstring'''





												return out_string


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							''''''
												SCREAMING_SNAKE_CASE							:					Union[str, 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(UpperCamelCase__		) + token
																								SCREAMING_SNAKE_CASE							:					int           =							True
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
																		else:
																								current_sub_tokens.append(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Any           =							False
												out_string += self.sp_model.decode(UpperCamelCase__		)

												return out_string


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{self.convert_ids_to_tokens(UpperCamelCase__		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





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

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		) and os.path.isfile(self.vocab_file		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
												elif not os.path.isfile(self.vocab_file		):
																		with open(UpperCamelCase__					,	'''wb'''		) as fi:
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							self.sp_model.serialized_model_proto()
																								fi.write(UpperCamelCase__		)

												return (out_vocab_file,)


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, List[str]]					,	UpperCamelCase__			:       Union[str, bool] = False		):





												'''simple docstring'''





												if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.preprocess_text(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.sp_model.encode(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[self.preprocess_text(UpperCamelCase__		) for t in text]
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.sp_model.encode(UpperCamelCase__		)

												if return_tensors is True or return_tensors == "pt":
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(UpperCamelCase__		)

												return token_ids


						def  __A ( self			:       Any					,	UpperCamelCase__			:       Union[int, List[int]]		):





												'''simple docstring'''





												return self.sp_model.decode(UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       "Conversation"		):





												'''simple docstring'''





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






import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





