


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


_SCREAMING_SNAKE_CASE			: str			      =    logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE			: Optional[int]			      =    {'vocab_file': 'spiece.model'}

_SCREAMING_SNAKE_CASE			: List[str]			      =    {
    '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',
    }
}

_SCREAMING_SNAKE_CASE			: Any			      =    {
    'AI-Sweden/gpt-sw3-126m': 20_48,
    'AI-Sweden/gpt-sw3-350m': 20_48,
    'AI-Sweden/gpt-sw3-1.6b': 20_48,
    'AI-Sweden/gpt-sw3-6.7b': 20_48,
    'AI-Sweden/gpt-sw3-20b': 20_48,
}

class   A__					(    lowercase_	):







    """simple docstring"""

    __magic_name__			  =		VOCAB_FILES_NAMES
    __magic_name__			  =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__			  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__			  =		['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			,					):
         snake_case		 =				{} if sp_model_kwargs is None else sp_model_kwargs

         snake_case		 =				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'''    )
              snake_case		 =				'''None'''

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

         snake_case		 =				do_lower_case
         snake_case		 =				remove_space
         snake_case		 =				keep_accents
         snake_case		 =				vocab_file

         snake_case		 =				spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(__UpperCamelCase    )

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

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         snake_case		 =				re.compile(
             F'''[{"".join(map(__UpperCamelCase			,					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    ):
         snake_case		 =				self.__dict__.copy()
         snake_case		 =				None
         return state
    def __setstate__(       self			,					__snake_case    ):
         snake_case		 =				d

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

         snake_case		 =				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    ):
         return len(self.sp_model    )
    def 		a_			(       self			,					__snake_case    ):
         snake_case		 =				self.non_printing_characters_re.sub(''''''			,					__UpperCamelCase    )

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

         # NFC Unicode normalization
         snake_case		 =				unicodedata.normalize('''NFC'''			,					__UpperCamelCase    )
         return text
    def 		a_			(       self			,					__snake_case			,					**__snake_case    ):
         snake_case		 =				self.preprocess_text(__UpperCamelCase    )
         return self.sp_model.encode(__UpperCamelCase			,					out_type=__UpperCamelCase    )
    def 		a_			(       self			,					__snake_case    ):
         return self.sp_model.PieceToId(__UpperCamelCase    )
    def 		a_			(       self			,					__snake_case    ):
         return self.sp_model.IdToPiece(__UpperCamelCase    )
    @staticmethod
    def 		a_			(       __snake_case    ):
         return out_string
    def 		a_			(       self			,					__snake_case    ):
         snake_case		 =				[]
         snake_case		 =				''''''
         snake_case		 =				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
                   snake_case		 =				True
                   snake_case		 =				[]
              else:
                   current_sub_tokens.append(__UpperCamelCase    )
                   snake_case		 =				False
         out_string += self.sp_model.decode(__UpperCamelCase    )

         return out_string
    def 		a_			(       self    ):
         snake_case		 =				{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			,					__snake_case			,					__snake_case = None    ):
         if not os.path.isdir(__UpperCamelCase    ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
              return
         snake_case		 =				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:
                   snake_case		 =				self.sp_model.serialized_model_proto()
                   fi.write(__UpperCamelCase    )

         return (out_vocab_file,)
    def 		a_			(       self			,					__snake_case			,					__snake_case = False    ):

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

         if return_tensors is True or return_tensors == "pt":
              snake_case		 =				torch.tensor(__UpperCamelCase    )

         return token_ids
    def 		a_			(       self			,					__snake_case    ):
         return self.sp_model.decode(__UpperCamelCase    )




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











"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format






class 					_UpperCAmelCase   :
					UpperCamelCase      =							None


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)
												A		=  json.loads(feat_extract.to_json_string()		)
												for key, value in self.feat_extract_dict.items():
																			self.assertEqual(obj[key]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  os.path.join(__UpperCamelCase     ,       "feat_extract.json"		)
																			feat_extract_first.to_json_file(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_json_file(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  feat_extract_first.save_pretrained(__UpperCamelCase		)[0]
																			check_json_file_has_correct_format(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_pretrained(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  self.feature_extraction_class()
												self.assertIsNotNone(__UpperCamelCase		)


