

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "spiece.model"}

lowercase_			=  {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

lowercase_			=  {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							['input_ids', 'attention_mask']
    lowerCamelCase		     =							[]


    def __init__(				self			:	Optional[Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]="<unk>",lowercase_			:	Union[str, Any]="<s>",lowercase_			:	Optional[int]="</s>",lowercase_			:	Union[str, Any]="<pad>",lowercase_			:	int="[SEP]",lowercase_			:	int="[MASK]",lowercase_			:	List[Any]="[CLS]",lowercase_			:	Optional[Dict[str, Any]] = None,**lowercase_			:	Tuple,)->   None:





        '''simple docstring'''







        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else bos_token
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else eos_token
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else unk_token
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else pad_token
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else cls_token
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token

        A__   				=       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowercase_,eos_token=lowercase_,unk_token=lowercase_,pad_token=lowercase_,sep_token=lowercase_,mask_token=lowercase_,cls_token=lowercase_,sp_model_kwargs=self.sp_model_kwargs,**lowercase_,)

        A__   				=       vocab_file

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


    @property
    def 						snake_case__					(				self			:	Union[str, Any]		)->   List[Any]:





        '''simple docstring'''







        return self.sp_model.get_piece_size()


    def 						snake_case__					(				self			:	Union[str, Any]		)->   Union[str, Any]:





        '''simple docstring'''







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


    def __getstate__(				self			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







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


    def __setstate__(				self			:	Tuple,lowercase_			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       d

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

        A__   				=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	str		)->   List[str]:





        '''simple docstring'''







        return self.sp_model.encode(lowercase_,out_type=lowercase_		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict		)->   Dict:





        '''simple docstring'''







        return self.sp_model.piece_to_id(lowercase_		)


    def 						snake_case__					(				self			:	Any,lowercase_			:	int		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.sp_model.IdToPiece(lowercase_		)
        return token


    def 						snake_case__					(				self			:	int,lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        A__   				=       []
        A__   				=       ''
        A__   				=       False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(lowercase_		) + token
                A__   				=       True
                A__   				=       []
            else:
                current_sub_tokens.append(lowercase_		)
                A__   				=       False
        out_string += self.sp_model.decode(lowercase_		)
        return out_string.strip()


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	List[int],lowercase_			:	bool = False,lowercase_			:	bool = None,lowercase_			:	bool = True,**lowercase_			:	str,)->   str:





        '''simple docstring'''







        A__   				=       kwargs.pop('use_source_tokenizer',lowercase_		)

        A__   				=       self.convert_ids_to_tokens(lowercase_,skip_special_tokens=lowercase_		)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__   				=       []
        A__   				=       []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            if token in self.added_tokens_encoder:
                if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(lowercase_		)		)
                    A__   				=       []
                sub_texts.append(lowercase_		)
            else:
                current_sub_text.append(lowercase_		)
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(lowercase_		)		)

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
            A__   				=       re.sub(r' (\[(MASK|SEP)\])',r'\1',' '.join(lowercase_		)		)
        else:
            A__   				=       ''.join(lowercase_		)

        A__   				=       (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
            A__   				=       self.clean_up_tokenization(lowercase_		)
            return clean_text
        else:
            return text


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







        if not os.path.isdir(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
            return
        A__   				=       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:
                A__   				=       self.sp_model.serialized_model_proto()
                fi.write(lowercase_		)

        return (out_vocab_file,)


    def 						snake_case__					(				self			:	Any,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A__   				=       [self.cls_token_id]
        A__   				=       [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None,lowercase_			:	bool = False		)->   List[int]:





        '''simple docstring'''







        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowercase_,token_ids_a=lowercase_,already_has_special_tokens=lowercase_		)

        if token_ids_a is None:
            return [1] + ([0] * len(lowercase_		)) + [1]
        return [1] + ([0] * len(lowercase_		)) + [1] + ([0] * len(lowercase_		)) + [1]


    def 						snake_case__					(				self			:	int,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [self.cls_token_id]
        if token_ids_a is None:
            return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

