






'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{'''vocab_file''': '''spiece.model'''}

lowercase							       =			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase							       =			{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowercase							       =			'''▁'''







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      VOCAB_FILES_NAMES
 __magic_name__					: Any  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: str  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: Dict  =      ["input_ids", "attention_mask"]




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_="</s>"							,  lowerCAmelCase_="<unk>"							,  lowerCAmelCase_="<pad>"							,  lowerCAmelCase_=1_0_0							,  lowerCAmelCase_=None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_=True							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      if extra_ids > 0 and additional_special_tokens is None:
           a_       		=[f"""<extra_id_{i}>""" for i in range(lowerCAmelCase_)]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           a_       		=len(set(filter(lambda lowerCAmelCase_: bool("extra_id" in str(lowerCAmelCase_))							,  lowerCAmelCase_)))
           if extra_tokens != extra_ids:
                raise ValueError(
                    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                    " tokens")
      if legacy:
           logger.warning_once(
               f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
               " read the related pull request available at https://github.com/huggingface/transformers/pull/24565")

      a_       		=legacy
      a_       		={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowerCAmelCase_							,  unk_token=lowerCAmelCase_							,  pad_token=lowerCAmelCase_							,  extra_ids=lowerCAmelCase_							,  additional_special_tokens=lowerCAmelCase_							,  sp_model_kwargs=self.sp_model_kwargs							,  legacy=lowerCAmelCase_							,  **lowerCAmelCase_							,  )

      a_       		=vocab_file
      a_       		=extra_ids

      a_       		=spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)




 @staticmethod
 def       lowercase_  (   lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           a_       		=TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    "This tokenizer was incorrectly instantiated with a model max length of"
                    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                    " instantiate this tokenizer with `model_max_length` set to your preferred value."							,  lowerCAmelCase_							,  )

      return max_model_length




 @property
 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      return self.sp_model.get_piece_size() + self._extra_ids




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



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = False) ->      List[int]:



      """simple docstring"""







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

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(lowerCAmelCase_)) + [1]
      return ([0] * len(lowerCAmelCase_)) + [1] + ([0] * len(lowerCAmelCase_)) + [1]




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



      """simple docstring"""







      return list(
          set(filter(lambda lowerCAmelCase_: bool(re.search(r"<extra_id_\d+>"							,  lowerCAmelCase_)) is not None							,  self.additional_special_tokens)))




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



      """simple docstring"""







      return [self._convert_token_to_id(lowerCAmelCase_) for token in self.get_sentinel_tokens()]




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



      """simple docstring"""







      if len(lowerCAmelCase_) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
               " eos tokens being added.")
           return token_ids
      else:
           return token_ids + [self.eos_token_id]




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



      """simple docstring"""







      a_       		=[self.eos_token_id]

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




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



      """simple docstring"""







      a_       		=self._add_eos_if_not_present(lowerCAmelCase_)
      if token_ids_a is None:
           return token_ids_a
      else:
           a_       		=self._add_eos_if_not_present(lowerCAmelCase_)
           return token_ids_a + token_ids_a




 def __getstate__(   self) ->      Any:



      """simple docstring"""







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




 def __setstate__(   self							,  lowerCAmelCase_) ->      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       lowercase_  (   self							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      if not self.legacy:
           a_       		=SPIECE_UNDERLINE + text.replace(lowerCAmelCase_							,  " ")
      return super().tokenize(lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      int:



      """simple docstring"""







      if not self.legacy:
           a_       		=text.startswith(lowerCAmelCase_)
           if is_first:
                a_       		=text[1:]

      a_       		=self.sp_model.encode(lowerCAmelCase_							,  out_type=lowerCAmelCase_)

      if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(lowerCAmelCase_):
           a_       		=([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
      return tokens




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      if token.startswith("<extra_id_"):
           a_       		=re.match(r"<extra_id_(\d+)>"							,  lowerCAmelCase_)
           a_       		=int(match.group(1))
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      if index < self.sp_model.get_piece_size():
           a_       		=self.sp_model.IdToPiece(lowerCAmelCase_)
      else:
           a_       		=f"""<extra_id_{self.vocab_size - 1 - index}>"""
      return token




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """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(lowerCAmelCase_) + token
                a_       		=True
                a_       		=[]
           else:
                current_sub_tokens.append(lowerCAmelCase_)
                a_       		=False
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Tuple[str]:



      """simple docstring"""







      if not os.path.isdir(lowerCAmelCase_):
           logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
           return
      a_       		=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:
                a_       		=self.sp_model.serialized_model_proto()
                fi.write(lowerCAmelCase_)

      return (out_vocab_file,)







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


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

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

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


