


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 : Tuple		=				logging.get_logger(__name__)
a : Optional[int]		=				{"vocab_file": "spiece.model"}

a : 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",
    }
}

a : str		=				{
    "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_				):



		'''simple docstring'''







		__UpperCAmelCase            =				VOCAB_FILES_NAMES
		__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase            =				["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_ ,       )   ->			str:







					'''simple docstring'''

					__lowercase 					=   {} if sp_model_kwargs is None else sp_model_kwargs

					__lowercase 					=   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'''						)
								__lowercase 					=   '''None'''

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

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

					__lowercase 					=   do_lower_case
					__lowercase 					=   remove_space
					__lowercase 					=   keep_accents
					__lowercase 					=   vocab_file

					__lowercase 					=   spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(_lowercase						)

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

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__lowercase 					=   re.compile(
					    F'[{"".join(map(_lowercase ,       list(range(0 ,       9						)						) + list(range(1_1 ,       3_2						)						) + list(range(1_2_7 ,       1_6_0						)						) + [1_6_0, 1_7_3, 8_2_0_3]						)						)}]'						)






		def __getstate__(				self						)   ->			Optional[int]:







					'''simple docstring'''

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






		def __setstate__(				self ,       snake_case_						)   ->			Dict:







					'''simple docstring'''

					__lowercase 					=   d

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

					__lowercase 					=   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						)   ->			Tuple:







					'''simple docstring'''

					return len(self.sp_model						)






		def A      (				self ,       snake_case_						)   ->			Optional[Any]:







					'''simple docstring'''

					__lowercase 					=   self.non_printing_characters_re.sub('''''' ,       _lowercase						)

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

					# NFC Unicode normalization
					__lowercase 					=   unicodedata.normalize('''NFC''' ,       _lowercase						)
					return text






		def A      (				self ,       snake_case_ ,       **snake_case_						)   ->			Any:







					'''simple docstring'''

					__lowercase 					=   self.preprocess_text(_lowercase						)
					return self.sp_model.encode(_lowercase ,       out_type=_lowercase						)






		def A      (				self ,       snake_case_						)   ->			Dict:







					'''simple docstring'''

					return self.sp_model.PieceToId(_lowercase						)






		def A      (				self ,       snake_case_						)   ->			List[str]:







					'''simple docstring'''

					return self.sp_model.IdToPiece(_lowercase						)






		@staticmethod
		def A      (				snake_case_						)   ->			Any:







					'''simple docstring'''

					return out_string






		def A      (				self ,       snake_case_						)   ->			Optional[Any]:







					'''simple docstring'''

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

					return out_string






		def A      (				self						)   ->			Tuple:







					'''simple docstring'''

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






		def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple:







					'''simple docstring'''

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

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

					return (out_vocab_file,)






		def A      (				self ,       snake_case_ ,       snake_case_ = False						)   ->			Any:







					'''simple docstring'''

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

					if return_tensors is True or return_tensors == "pt":
								__lowercase 					=   torch.tensor(_lowercase						)

					return token_ids






		def A      (				self ,       snake_case_						)   ->			str:







					'''simple docstring'''

					return self.sp_model.decode(_lowercase						)






		def A      (				self ,       snake_case_						)   ->			Optional[int]:







					'''simple docstring'''

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






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   len(_UpperCamelCase  )
			__lowercase 					=   len(matrix[0]  )
			__lowercase 					=   min(_UpperCamelCase       ,			_UpperCamelCase  )

			for row in range(_UpperCamelCase  ):
						# Check if diagonal element is not zero
						if matrix[row][row] != 0:
									# Eliminate all the elements below the diagonal
									for col in range(row + 1       ,			_UpperCamelCase  ):
												__lowercase 					=   matrix[col][row] / matrix[row][row]
												for i in range(_UpperCamelCase       ,			_UpperCamelCase  ):
															matrix[col][i] -= multiplier * matrix[row][i]
						else:
									# Find a non-zero diagonal element to swap rows
									__lowercase 					=   True
									for i in range(row + 1       ,			_UpperCamelCase  ):
												if matrix[i][row] != 0:
															__lowercase					, __lowercase 					=   matrix[i], matrix[row]
															__lowercase 					=   False
															break
									if reduce:
												rank -= 1
												for i in range(_UpperCamelCase  ):
															__lowercase 					=   matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
									row -= 1

			return rank


if __name__ == "__main__":
	import doctest

	doctest.testmod()


