






'''simple docstring'''

import os
import unicodedata
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


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

UpperCamelCase							={
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

UpperCamelCase							={
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

UpperCamelCase							="▁"




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Dict      =						VOCAB_FILES_NAMES
 __a			:						int      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						List[Any]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=True			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase="[CLS]"			,     __lowerCAmelCase="[SEP]"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase="[SEP]"			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase="[CLS]"			,     __lowerCAmelCase="[MASK]"			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       UpperCamelCase_				:   Union[str, Any]					    =							(
           AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase			,     normalized=__lowerCAmelCase )
           if isinstance(__lowerCAmelCase			,     __lowerCAmelCase )
           else mask_token
       )

       UpperCamelCase_				:   str					    =							{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=__lowerCAmelCase			,     remove_space=__lowerCAmelCase			,     keep_accents=__lowerCAmelCase			,     bos_token=__lowerCAmelCase			,     eos_token=__lowerCAmelCase			,     unk_token=__lowerCAmelCase			,     sep_token=__lowerCAmelCase			,     pad_token=__lowerCAmelCase			,     cls_token=__lowerCAmelCase			,     mask_token=__lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCAmelCase			,     )

       UpperCamelCase_				:   Optional[Any]					    =							do_lower_case
       UpperCamelCase_				:   Tuple					    =							remove_space
       UpperCamelCase_				:   Union[str, Any]					    =							keep_accents
       UpperCamelCase_				:   Any					    =							vocab_file

       UpperCamelCase_				:   Union[str, Any]					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(__lowerCAmelCase )
 @property
 def   _UpperCAmelCase       (							self ):
       return len(self.sp_model )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							{self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
 def __getstate__(							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.__dict__.copy()
       UpperCamelCase_				:   Tuple					    =							None
       return state
 def __setstate__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							d

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

       UpperCamelCase_				:   int					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.remove_space:
             UpperCamelCase_				:   List[str]					    =							""" """.join(inputs.strip().split() )
       else:
             UpperCamelCase_				:   Dict					    =							inputs
       UpperCamelCase_				:   int					    =							outputs.replace("""``"""			,     """\"""" ).replace("""''"""			,     """\"""" )

       if not self.keep_accents:
             UpperCamelCase_				:   Union[str, Any]					    =							unicodedata.normalize("""NFKD"""			,     __lowerCAmelCase )
             UpperCamelCase_				:   Optional[int]					    =							"""""".join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase )] )
       if self.do_lower_case:
             UpperCamelCase_				:   Any					    =							outputs.lower()

       return outputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							self.preprocess_text(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							self.sp_model.encode(__lowerCAmelCase			,     out_type=__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							[]
       for piece in pieces:
             if len(__lowerCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
                   UpperCamelCase_				:   str					    =							self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase			,     """""" ) )
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0] ) == 1:
                               UpperCamelCase_				:   List[str]					    =							cur_pieces[1:]
                         else:
                               UpperCamelCase_				:   Any					    =							cur_pieces[0][1:]
                   cur_pieces.append(piece[-1] )
                   new_pieces.extend(__lowerCAmelCase )
             else:
                   new_pieces.append(__lowerCAmelCase )

       return new_pieces
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.sp_model.PieceToId(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.sp_model.IdToPiece(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							[]
       UpperCamelCase_				:   Optional[Any]					    =							""""""
       UpperCamelCase_				:   Tuple					    =							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(__lowerCAmelCase ) + token
                   UpperCamelCase_				:   Tuple					    =							True
                   UpperCamelCase_				:   List[str]					    =							[]
             else:
                   current_sub_tokens.append(__lowerCAmelCase )
                   UpperCamelCase_				:   Optional[Any]					    =							False
       out_string += self.sp_model.decode(__lowerCAmelCase )
       return out_string.strip()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   List[str]					    =							[self.sep_token_id]
       UpperCamelCase_				:   int					    =							[self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False ):

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

       if token_ids_a is not None:
             return [1] + ([0] * len(__lowerCAmelCase )) + [1] + ([0] * len(__lowerCAmelCase )) + [1]
       return [1] + ([0] * len(__lowerCAmelCase )) + [1]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   List[str]					    =							[self.sep_token_id]
       UpperCamelCase_				:   Dict					    =							[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]





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       if not os.path.isdir(__lowerCAmelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Optional[Any]					    =							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:
                   UpperCamelCase_				:   Optional[Any]					    =							self.sp_model.serialized_model_proto()
                   fi.write(__lowerCAmelCase )

       return (out_vocab_file,)














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCamelCase							="A photo of sks dog in a bucket"
UpperCamelCase							=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")






