'''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


_lowercase		:				List[str]	       =		logging.get_logger(__name__)
_lowercase		:				List[str]	       =		{"vocab_file": "spiece.model"}

_lowercase		:				List[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",
    }
}

_lowercase		:				Dict	       =		{
    "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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = VOCAB_FILES_NAMES
			UpperCamelCase__       = PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__       = ['''input_ids''', '''attention_mask''']







			def __init__(			self       :						str       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=False       ,    lowercase_       :						Union[str, Any]=False       ,    lowercase_       :						Dict=False       ,    lowercase_       :						str=None       ,    lowercase_       :						Optional[int]=None       ,    lowercase_       :						str=None       ,    lowercase_       :						Any=None       ,    lowercase_       :						Optional[Dict[str, Any]] = None       ,    **lowercase_       :						Tuple       ,    ):
										lowercase_      :    Optional[int]       		= {} if sp_model_kwargs is None else sp_model_kwargs

										lowercase_      :    Union[str, 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"""					)
																	lowercase_      :    List[Any]       		= """None"""

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

										lowercase_      :    Dict       		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(lowercase_					)

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

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







			def __getstate__(			self       :						Tuple					):
										lowercase_      :    int       		= self.__dict__.copy()
										lowercase_      :    Union[str, Any]       		= None
										return state







			def __setstate__(			self       :						Union[str, Any]       ,    lowercase_       :						str					):
										lowercase_      :    Any       		= d

										# for backward compatibility
										if not hasattr(self       ,    """sp_model_kwargs"""					):
																	lowercase_      :    Optional[Any]       		= {}

										lowercase_      :    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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										return len(self.sp_model					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str					):
										lowercase_      :    Union[str, Any]       		= self.non_printing_characters_re.sub(""""""       ,    lowercase_					)

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

										# NFC Unicode normalization
										lowercase_      :    Tuple       		= unicodedata.normalize("""NFC"""       ,    lowercase_					)
										return text







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						str       ,    **lowercase_       :						List[Any]					):
										lowercase_      :    Tuple       		= self.preprocess_text(lowercase_					)
										return self.sp_model.encode(lowercase_       ,    out_type=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						str					):
										return self.sp_model.PieceToId(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						int					):
										return self.sp_model.IdToPiece(lowercase_					)







			@staticmethod
			def 			SCREAMING_SNAKE_CASE_  (			lowercase_       :						str					):
										return out_string







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Optional[int]       		= []
										lowercase_      :    Optional[Any]       		= """"""
										lowercase_      :    Dict       		= 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_      :    Optional[Any]       		= True
																								lowercase_      :    Optional[Any]       		= []
																	else:
																								current_sub_tokens.append(lowercase_					)
																								lowercase_      :    List[str]       		= False
										out_string += self.sp_model.decode(lowercase_					)

										return out_string







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    List[Any]       		= {self.convert_ids_to_tokens(lowercase_					): i for i in range(self.vocab_size					)}
										vocab.update(self.added_tokens_encoder					)
										return vocab







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						Optional[str] = None					):
										if not os.path.isdir(lowercase_					):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
																	return
										lowercase_      :    str       		= 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_      :    Any       		= self.sp_model.serialized_model_proto()
																								fi.write(lowercase_					)

										return (out_vocab_file,)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						Union[str, List[str]]       ,    lowercase_       :						Union[str, bool] = False					):

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

										if return_tensors is True or return_tensors == "pt":
																	lowercase_      :    List[str]       		= torch.tensor(lowercase_					)

										return token_ids







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Union[int, List[int]]					):
										return self.sp_model.decode(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						"Conversation"					):
										lowercase_      :    Optional[Any]       		= [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
										lowercase_      :    Optional[Any]       		= (
										    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(lowercase_					) + f'''{self.bos_token}Bot:'''
										)
										return self.encode(text=lowercase_					)






'''simple docstring'''




_lowercase		:				int	       =		"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"




def 							lowerCamelCase			(   UpperCAmelCase__ :      bytes				)    ->   bytes:
							# Make sure the supplied data is a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Union[str, Any]       		= F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
														raise TypeError(UpperCAmelCase__				)

							lowercase_      :    Dict       		= """""".join(bin(UpperCAmelCase__				)[2:].zfill(8				) for byte in data				)

							lowercase_      :    Union[str, Any]       		= len(UpperCAmelCase__				) % 6 != 0

							if padding_needed:
														# The padding that will be added later
														lowercase_      :    List[Any]       		= b"""=""" * ((6 - len(UpperCAmelCase__				) % 6) // 2)

														# Append binary_stream with arbitrary binary digits (0's by default) to make its
														# length a multiple of 6.
														binary_stream += "0" * (6 - len(UpperCAmelCase__				) % 6)
							else:
														lowercase_      :    Union[str, Any]       		= b""""""

							# Encode every 6 binary digits to their corresponding Base64 character
							return (
							    "".join(
							        B64_CHARSET[int(binary_stream[index : index + 6]   ,   2				)]
							        for index in range(0   ,   len(UpperCAmelCase__				)   ,   6				)				).encode()
							    + padding
							)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   bytes:
							# Make sure encoded_data is either a string or a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				) and not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    List[str]       		= (
														    """argument should be a bytes-like object or ASCII string, """
														    F'''not \'{encoded_data.__class__.__name__}\''''
														)
														raise TypeError(UpperCAmelCase__				)

							# In case encoded_data is a bytes-like object, make sure it contains only
							# ASCII characters so we convert it to a string object
							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														try:
																					lowercase_      :    Optional[int]       		= encoded_data.decode("""utf-8"""				)
														except UnicodeDecodeError:
																					raise ValueError("""base64 encoded data should only contain ASCII characters"""				)

							lowercase_      :    Any       		= encoded_data.count("""="""				)

							# Check if the encoded string contains non base64 characters
							if padding:
														assert all(
														    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
							else:
														assert all(
														    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

							# Check the padding
							assert len(UpperCAmelCase__				) % 4 == 0 and padding < 3, "Incorrect padding"

							if padding:
														# Remove padding if there is one
														lowercase_      :    Optional[int]       		= encoded_data[:-padding]

														lowercase_      :    Any       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
							else:
														lowercase_      :    int       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)

							lowercase_      :    Optional[int]       		= [
							    int(binary_stream[index : index + 8]   ,   2				)
							    for index in range(0   ,   len(UpperCAmelCase__				)   ,   8				)
							]

							return bytes(UpperCAmelCase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





