'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A			:      Optional[Any]					      =				logging.get_logger(__name__)


_A			:      Optional[Any]					      =				'''▁'''

_A			:      Tuple					      =				{'''vocab_file''': '''spiece.model'''}

_A			:      Tuple					      =				{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

_A			:      Union[str, Any]					      =				{
    '''google/reformer-crime-and-punishment''': 524288,
}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE     :       Any      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["input_ids", "attention_mask"]
 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Tuple="</s>"      ,       SCREAMING_SNAKE_CASE__   :	Any="<unk>"      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=[]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Dict[str, Any]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       )  ->    None:
  __lowerCAmelCase											=						{} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      eos_token=UpperCAmelCase__      ,       unk_token=UpperCAmelCase__      ,       additional_special_tokens=UpperCAmelCase__      ,       sp_model_kwargs=self.sp_model_kwargs      ,       **UpperCAmelCase__      ,       )

  __lowerCAmelCase											=						vocab_file
  __lowerCAmelCase											=						spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  self.sp_model.Load(UpperCAmelCase__							)
 @property
 def a					(      self   :	Dict							)  ->    str:
  return self.sp_model.get_piece_size()
 def a					(      self   :	List[Any]							)  ->    Dict[str, int]:
  __lowerCAmelCase											=						{self.convert_ids_to_tokens(UpperCAmelCase__							): i for i in range(self.vocab_size							)}
  vocab.update(self.added_tokens_encoder							)
  return vocab
 def __getstate__(      self   :	Optional[int]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.__dict__.copy()
  __lowerCAmelCase											=						None
  return state
 def __setstate__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						d

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

  __lowerCAmelCase											=						spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  self.sp_model.Load(self.vocab_file							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    List[str]:
  return self.sp_model.encode(UpperCAmelCase__      ,       out_type=UpperCAmelCase__							)
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    Optional[int]:
  return self.sp_model.piece_to_id(UpperCAmelCase__							)
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Any							)  ->    List[str]:
  if index < self.sp_model.get_piece_size():
   __lowerCAmelCase											=						self.sp_model.IdToPiece(UpperCAmelCase__							)
  return token
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						""""""
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    out_string += self.sp_model.decode(UpperCAmelCase__							) + token
    __lowerCAmelCase											=						[]
   else:
    current_sub_tokens.append(UpperCAmelCase__							)
  out_string += self.sp_model.decode(UpperCAmelCase__							)
  return out_string.strip()





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

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

  return (out_vocab_file,)










"""simple docstring"""







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


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

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