


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 SPIECE_UNDERLINE, logging


__UpperCAmelCase : Tuple 			=				logging.get_logger(__name__)

__UpperCAmelCase : Tuple 			=				{"vocab_file": "spiece.model"}

__UpperCAmelCase : List[Any] 			=				{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<sep>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<cls>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=["<eop>", "<eod>"] ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else mask_token

              UpperCamelCase    :	Any   	=		{} if sp_model_kwargs is None else sp_model_kwargs

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

              UpperCamelCase    :	Dict   	=		3

              UpperCamelCase    :	str   	=		do_lower_case
              UpperCamelCase    :	Optional[Any]   	=		remove_space
              UpperCamelCase    :	List[Any]   	=		keep_accents
              UpperCamelCase    :	Optional[int]   	=		vocab_file

              UpperCamelCase    :	int   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(__SCREAMING_SNAKE_CASE						)

              try:
                     import jieba
              except ModuleNotFoundError as error:
                     raise error.__class__(
                         '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                         '''See https://pypi.org/project/jieba/ for installation.'''						)
              UpperCamelCase    :	str   	=		jieba
              UpperCamelCase    :	Tuple   	=		str.maketrans(''' \n''' ,					'''\u2582\u2583'''						)






       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def  _lowercase   (     self						):




              """simple docstring"""




              return len(self.sp_model						)






       def  _lowercase   (     self						):




              """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 __getstate__(     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.__dict__.copy()
              UpperCamelCase    :	Tuple   	=		None
              return state






       def __setstate__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		d

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

              UpperCamelCase    :	str   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(self.vocab_file						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if self.remove_space:
                     UpperCamelCase    :	Tuple   	=		''' '''.join(inputs.strip().split()						)
              else:
                     UpperCamelCase    :	Any   	=		inputs
              UpperCamelCase    :	List[Any]   	=		outputs.replace('''``''' ,					'''"'''						).replace('''\'\'''' ,					'''"'''						)

              if not self.keep_accents:
                     UpperCamelCase    :	List[Any]   	=		unicodedata.normalize('''NFKD''' ,					__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Any   	=		''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE						)]						)
              if self.do_lower_case:
                     UpperCamelCase    :	Union[str, Any]   	=		outputs.lower()

              return outputs






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.preprocess_text(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		self.sp_model.encode(__SCREAMING_SNAKE_CASE ,					out_type=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		[]
              for piece in pieces:
                     if len(__SCREAMING_SNAKE_CASE						) > 1 and piece[-1] == str(''','''						) and piece[-2].isdigit():
                            UpperCamelCase    :	Union[str, Any]   	=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE ,					''''''						)						)
                            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                   if len(cur_pieces[0]						) == 1:
                                          UpperCamelCase    :	str   	=		cur_pieces[1:]
                                   else:
                                          UpperCamelCase    :	Tuple   	=		cur_pieces[0][1:]
                            cur_pieces.append(piece[-1]						)
                            new_pieces.extend(__SCREAMING_SNAKE_CASE						)
                     else:
                            new_pieces.append(__SCREAMING_SNAKE_CASE						)

              return new_pieces






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		''''''.join(__SCREAMING_SNAKE_CASE						).replace(__SCREAMING_SNAKE_CASE ,					''' '''						).strip()
              return out_string






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		[self.sep_token_id]
              UpperCamelCase    :	Optional[Any]   	=		[self.cls_token_id]
              if token_ids_a is None:
                     return token_ids_a + sep + cls
              return token_ids_a + sep + token_ids_a + sep + cls






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = 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						)

              if token_ids_a is not None:
                     return ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1, 1]
              return ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1, 1]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		[self.sep_token_id]
              UpperCamelCase    :	Any   	=		[2]

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if not os.path.isdir(__SCREAMING_SNAKE_CASE						):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
                     return
              UpperCamelCase    :	Optional[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,)






       def  _lowercase   (     self ,					*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		super()._decode(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		text.replace(''' ''' ,					''''''						).replace('''\u2582''' ,					''' '''						).replace('''\u2583''' ,					'''\n'''						)
              return text





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


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		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    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		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

