
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											=    logging.get_logger(__name__)
_lowercase											=    {"""vocab_file""": """spiece.model"""}

_lowercase											=    {
    """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											=    {
    """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_		(      A       ):
       __lowerCamelCase				       =						VOCAB_FILES_NAMES
       __lowerCamelCase				       =						PRETRAINED_VOCAB_FILES_MAP
       __lowerCamelCase				       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCamelCase				       =						["input_ids", "attention_mask"]


       def __init__(    self						,       __A						,       __A=False						,       __A=False						,       __A=False						,       __A=None						,       __A=None						,       __A=None						,       __A=None						,       __A = None						,       **__A						,       )						->    None:
              SCREAMING_SNAKE_CASE_							:				str		={} if sp_model_kwargs is None else sp_model_kwargs

              SCREAMING_SNAKE_CASE_							:				List[str]		=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_							:				Tuple		='''None'''

              # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
              SCREAMING_SNAKE_CASE_							:				List[Any]		='''<|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_							:				List[Any]		=unk_token if pad_token is None else pad_token
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=eos_token if bos_token is None else bos_token
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''<pad>''' if pad_token is None else pad_token
                     SCREAMING_SNAKE_CASE_							:				List[Any]		='''<s>''' if bos_token is None else bos_token

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

              SCREAMING_SNAKE_CASE_							:				str		=do_lower_case
              SCREAMING_SNAKE_CASE_							:				str		=remove_space
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=keep_accents
              SCREAMING_SNAKE_CASE_							:				Any		=vocab_file

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=spm.SentencePieceProcessor(**self.sp_model_kwargs				)
              self.sp_model.Load(__A				)

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

              # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
              SCREAMING_SNAKE_CASE_							:				List[str]		=re.compile(
                  F'[{"".join(map(__A						,       list(range(0						,       9				)				) + list(range(11						,       32				)				) + list(range(127						,       160				)				) + [160, 173, 8_203]				)				)}]'				)


       def __getstate__(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.__dict__.copy()
              SCREAMING_SNAKE_CASE_							:				Tuple		=None
              return state


       def __setstate__(    self						,       __A				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=d

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

              SCREAMING_SNAKE_CASE_							:				int		=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 	_snake_case				(    self				)						->    int:
              return len(self.sp_model				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.non_printing_characters_re.sub(''''''						,       __A				)

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

              # NFC Unicode normalization
              SCREAMING_SNAKE_CASE_							:				Dict		=unicodedata.normalize('''NFC'''						,       __A				)
              return text


       def 	_snake_case				(    self						,       __A						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.preprocess_text(__A				)
              return self.sp_model.encode(__A						,       out_type=__A				)


       def 	_snake_case				(    self						,       __A				)						->    int:
              return self.sp_model.PieceToId(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              return self.sp_model.IdToPiece(__A				)


       @staticmethod
       def 	_snake_case				(    __A				)						->    str:
              return out_string


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]
              SCREAMING_SNAKE_CASE_							:				Any		=''''''
              SCREAMING_SNAKE_CASE_							:				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(__A				) + token
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=True
                            SCREAMING_SNAKE_CASE_							:				Dict		=[]
                     else:
                            current_sub_tokens.append(__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=False
              out_string += self.sp_model.decode(__A				)

              return out_string


       def 	_snake_case				(    self				)						->    Dict[str, int]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={self.convert_ids_to_tokens(__A				): i for i in range(self.vocab_size				)}
              vocab.update(self.added_tokens_encoder				)
              return vocab


       def 	_snake_case				(    self						,       __A						,       __A = None				)						->    Tuple[str]:
              if not os.path.isdir(__A				):
                     logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
                     return
              SCREAMING_SNAKE_CASE_							:				Tuple		=os.path.join(
                  __A						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

              return (out_vocab_file,)


       def 	_snake_case				(    self						,       __A						,       __A = False				)						->    Union[List[int], List[List[int]], "torch.Tensor"]:

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

              if return_tensors is True or return_tensors == "pt":
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor(__A				)

              return token_ids


       def 	_snake_case				(    self						,       __A				)						->    str:
              return self.sp_model.decode(__A				)





       def 	_snake_case				(    self						,       __A				)						->    List[int]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
              SCREAMING_SNAKE_CASE_							:				List[str]		=(
                  F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(__A				) + F'{self.bos_token}Bot:'
              )
              return self.encode(text=__A				)


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
