






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


lowerCamelCase	:							str				=logging.get_logger(__name__)

lowerCamelCase	:							List[str]				={'''vocab_file''': '''spiece.model'''}

lowerCamelCase	:							int				={
    '''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
lowerCamelCase	:							Union[str, Any]				={
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowerCamelCase	:							List[Any]				='''▁'''





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = VOCAB_FILES_NAMES
 _lowerCAmelCase       :       str        = PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase       :       Any        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase       :       Optional[Any]        = ['''input_ids''', '''attention_mask''']







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Any="</s>"							,   SCREAMING_SNAKE_CASE					:   Any="<unk>"							,   SCREAMING_SNAKE_CASE					:   Optional[Any]="<pad>"							,   SCREAMING_SNAKE_CASE					:   List[Any]=1_00							,   SCREAMING_SNAKE_CASE					:   str=None							,   SCREAMING_SNAKE_CASE					:   Optional[Dict[str, Any]] = None							,   SCREAMING_SNAKE_CASE					:   List[Any]=True							,   **SCREAMING_SNAKE_CASE					:   List[str]							,   ):


      '''simple docstring'''






      if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__			:   List[Any]       				=  [F'<extra_id_{i}>' for i in range(SCREAMING_SNAKE_CASE						)]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__			:   Dict       				=  len(set(filter(lambda SCREAMING_SNAKE_CASE						: bool("extra_id" in str(SCREAMING_SNAKE_CASE						)						)							,   SCREAMING_SNAKE_CASE						)						)						)
           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"						)

      UpperCamelCase__			:   List[Any]       				=  legacy
      UpperCamelCase__			:   int       				=  {} if sp_model_kwargs is None else sp_model_kwargs

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

      UpperCamelCase__			:   List[str]       				=  vocab_file
      UpperCamelCase__			:   Optional[Any]       				=  extra_ids

      UpperCamelCase__			:   str       				=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(SCREAMING_SNAKE_CASE						)







 @staticmethod
 def 					__lowercase					(		SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__			:   Tuple       				=  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."							,   SCREAMING_SNAKE_CASE							,   )

      return max_model_length







 @property
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
      vocab.update(self.added_tokens_encoder						)
      return vocab







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None							,   SCREAMING_SNAKE_CASE					:   bool = False						):


      '''simple docstring'''






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

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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   List[int]						):


      '''simple docstring'''






      if len(SCREAMING_SNAKE_CASE						) > 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					:   Any							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  [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					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self._add_eos_if_not_present(SCREAMING_SNAKE_CASE						)
      if token_ids_a is None:
           return token_ids_a
      else:
           UpperCamelCase__			:   Optional[Any]       				=  self._add_eos_if_not_present(SCREAMING_SNAKE_CASE						)
           return token_ids_a + token_ids_a







 def __getstate__(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.__dict__.copy()
      UpperCamelCase__			:   List[Any]       				=  None
      return state







 def __setstate__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  d

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

      UpperCamelCase__			:   int       				=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(self.vocab_file						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   "TextInput"							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      if not self.legacy:
           UpperCamelCase__			:   Tuple       				=  SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE							,   " "						)
      return super().tokenize(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      if not self.legacy:
           UpperCamelCase__			:   List[str]       				=  text.startswith(SCREAMING_SNAKE_CASE						)
           if is_first:
                UpperCamelCase__			:   List[str]       				=  text[1:]

      UpperCamelCase__			:   Tuple       				=  self.sp_model.encode(SCREAMING_SNAKE_CASE							,   out_type=SCREAMING_SNAKE_CASE						)

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







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if token.startswith("<extra_id_"						):
           UpperCamelCase__			:   str       				=  re.match(R"<extra_id_(\d+)>"							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   List[str]       				=  int(match.group(1						)						)
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if index < self.sp_model.get_piece_size():
           UpperCamelCase__			:   Any       				=  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   int       				=  F'<extra_id_{self.vocab_size - 1 - index}>'
      return token







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  []
      UpperCamelCase__			:   List[str]       				=  ""
      UpperCamelCase__			:   List[Any]       				=  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(SCREAMING_SNAKE_CASE						) + token
                UpperCamelCase__			:   List[Any]       				=  True
                UpperCamelCase__			:   Optional[Any]       				=  []
           else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Optional[int]       				=  False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE						)
      return out_string.strip()







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[str] = None						):


      '''simple docstring'''






      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
           return
      UpperCamelCase__			:   Union[str, Any]       				=  os.path.join(
          SCREAMING_SNAKE_CASE							,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

      return (out_vocab_file,)







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2