

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			:					List[str]						=			logging.get_logger(__name__)
__A			:					Union[str, Any]						=			{'vocab_file': 'spiece.model'}

__A			:					int						=			{
    '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			:					Optional[int]						=			{
    'AI-Sweden/gpt-sw3-126m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-350m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-1.6b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-6.7b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-20b': 2_0_4_8,
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





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

       SCREAMING_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"			)
           SCREAMING_SNAKE_CASE										=   "None"

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

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

       SCREAMING_SNAKE_CASE										=   do_lower_case
       SCREAMING_SNAKE_CASE										=   remove_space
       SCREAMING_SNAKE_CASE										=   keep_accents
       SCREAMING_SNAKE_CASE										=   vocab_file

       SCREAMING_SNAKE_CASE										=   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(__lowerCamelCase			)

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

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





   def __getstate__(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   self.__dict__.copy()
       SCREAMING_SNAKE_CASE										=   None
       return state





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

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

       SCREAMING_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      _snake_case		(       self					:			Tuple			):
       return len(self.sp_model			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   self.non_printing_characters_re.sub(""     , __lowerCamelCase			)

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

       # NFC Unicode normalization
       SCREAMING_SNAKE_CASE										=   unicodedata.normalize("NFC"     , __lowerCamelCase			)
       return text





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			str     , **__lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.preprocess_text(__lowerCamelCase			)
       return self.sp_model.encode(__lowerCamelCase     , out_type=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str			):
       return self.sp_model.PieceToId(__lowerCamelCase			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			int			):
       return self.sp_model.IdToPiece(__lowerCamelCase			)





   @staticmethod
   def      _snake_case		(       __lowerCamelCase					:			str			):
       return out_string





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   ""
       SCREAMING_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(__lowerCamelCase			) + token
               SCREAMING_SNAKE_CASE										=   True
               SCREAMING_SNAKE_CASE										=   []
           else:
               current_sub_tokens.append(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   False
       out_string += self.sp_model.decode(__lowerCamelCase			)

       return out_string





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   {self.convert_ids_to_tokens(__lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

       return (out_vocab_file,)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Union[str, List[str]]     , __lowerCamelCase					:			Union[str, bool] = False			):

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

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

       return token_ids





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[int, List[int]]			):
       return self.sp_model.decode(__lowerCamelCase			)





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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					List[Any]						=			{
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)