






'''simple docstring'''


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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


lowerCamelCase_					:			int     						=				logging.get_logger(__name__)

lowerCamelCase_					:			Tuple     						=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					:			Tuple     						=				{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			def __init__( self				:    Dict			,   lowercase				:    Optional[int]=None			,   lowercase				:    int=None			,   lowercase				:    Any=False			,   lowercase				:    Tuple=True			,   lowercase				:    List[str]=False			,   lowercase				:    Tuple="<s>"			,   lowercase				:    List[str]="</s>"			,   lowercase				:    Dict="<unk>"			,   lowercase				:    Any="<sep>"			,   lowercase				:    Union[str, Any]="<pad>"			,   lowercase				:    str="<cls>"			,   lowercase				:    List[str]="<mask>"			,   lowercase				:    Any=["<eop>", "<eod>"]			,   **lowercase				:    str			,   )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			AddedToken(lowercase			,   lstrip=lowercase			,   rstrip=lowercase      ) if isinstance(lowercase			,   lowercase      ) else mask_token

									super().__init__(
									    vocab_file=lowercase			,   tokenizer_file=lowercase			,   do_lower_case=lowercase			,   remove_space=lowercase			,   keep_accents=lowercase			,   bos_token=lowercase			,   eos_token=lowercase			,   unk_token=lowercase			,   sep_token=lowercase			,   pad_token=lowercase			,   cls_token=lowercase			,   mask_token=lowercase			,   additional_special_tokens=lowercase			,   **lowercase			,   )

									UpperCamelCase__       =			3
									UpperCamelCase__       =			do_lower_case
									UpperCamelCase__       =			remove_space
									UpperCamelCase__       =			keep_accents
									UpperCamelCase__       =			vocab_file
									UpperCamelCase__       =			False if not self.vocab_file else True

									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__       =			jieba
									UpperCamelCase__       =			str.maketrans(""" \n"""			,   """\u2582\u2583"""      )






			def 					A   ( self				:    List[str]			,   lowercase				:    List[int]			,   lowercase				:    Optional[List[int]] = None      )				->     List[int]:

									'''simple docstring'''







									UpperCamelCase__       =			[self.sep_token_id]
									UpperCamelCase__       =			[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 					A   ( self				:    Dict			,   lowercase				:    List[int]			,   lowercase				:    Optional[List[int]] = None      )				->     List[int]:

									'''simple docstring'''







									UpperCamelCase__       =			[self.sep_token_id]
									UpperCamelCase__       =			[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 					A   ( self				:    Dict			,   lowercase				:    str			,   lowercase				:    Optional[str] = None      )				->     Tuple[str]:

									'''simple docstring'''







									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""      )

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

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase      ):
															copyfile(self.vocab_file			,   lowercase      )

									return (out_vocab_file,)






			def 					A   ( self				:    str			,   lowercase				:    Optional[int]			,   *lowercase				:    Tuple			,   **lowercase				:    Union[str, Any]      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			[
									    """ """.join([x.translate(self.translator      ) for x in self.jieba.cut(lowercase			,   cut_all=lowercase      )]      )
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase			,   *lowercase			,   **lowercase      )







			def 					A   ( self				:    int			,   *lowercase				:    List[Any]			,   **lowercase				:    str      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			super()._decode(*lowercase			,   **lowercase      )
									UpperCamelCase__       =			text.replace(""" """			,   """"""      ).replace("""\u2582"""			,   """ """      ).replace("""\u2583"""			,   """\n"""      )
									return text








'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
