




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


_lowerCamelCase      			=						logging.get_logger(__name__)

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

_lowerCamelCase      			=						{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}



class       a					(       __SCREAMING_SNAKE_CASE      ):

     '''simple docstring'''





     def __init__(     self       :      Any ,   __snake_case       :      List[str] ,   __snake_case       :      List[Any]=False ,   __snake_case       :      Tuple=True ,   __snake_case       :      List[str]=False ,   __snake_case       :      Optional[Any]="<s>" ,   __snake_case       :      int="</s>" ,   __snake_case       :      str="<unk>" ,   __snake_case       :      Union[str, Any]="<sep>" ,   __snake_case       :      Dict="<pad>" ,   __snake_case       :      Optional[int]="<cls>" ,   __snake_case       :      Optional[Any]="<mask>" ,   __snake_case       :      Optional[Any]=["<eop>", "<eod>"] ,   __snake_case       :      Optional[Dict[str, Any]] = None ,   **__snake_case       :      Optional[int] ,   ):
           UpperCAmelCase_										=					AddedToken(UpperCamelCase__ ,   lstrip=UpperCamelCase__ ,   rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__ ,   UpperCamelCase__    ) else mask_token

           UpperCAmelCase_										=					{} if sp_model_kwargs is None else sp_model_kwargs

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

           UpperCAmelCase_										=					3

           UpperCAmelCase_										=					do_lower_case
           UpperCAmelCase_										=					remove_space
           UpperCAmelCase_										=					keep_accents
           UpperCAmelCase_										=					vocab_file

           UpperCAmelCase_										=					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(UpperCamelCase__    )

           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'''    )
     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def   lowerCamelCase_       (     self       :      Any    ):
           return len(self.sp_model    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
           UpperCAmelCase_										=					{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       :      Dict    ):
           UpperCAmelCase_										=					self.__dict__.copy()
           UpperCAmelCase_										=					None
           return state
     def __setstate__(     self       :      int ,   __snake_case       :      Optional[int]    ):
           UpperCAmelCase_										=					d

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

           UpperCAmelCase_										=					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      str    ):







           if self.remove_space:
                 UpperCAmelCase_										=					''' '''.join(inputs.strip().split()    )
           else:
                 UpperCAmelCase_										=					inputs
           UpperCAmelCase_										=					outputs.replace('''``''' ,   '''"'''    ).replace('''\'\'''' ,   '''"'''    )

           if not self.keep_accents:
                 UpperCAmelCase_										=					unicodedata.normalize('''NFKD''' ,   UpperCamelCase__    )
                 UpperCAmelCase_										=					''''''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase__    )]    )
           if self.do_lower_case:
                 UpperCAmelCase_										=					outputs.lower()

           return outputs
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      str    ):
           UpperCAmelCase_										=					self.preprocess_text(UpperCamelCase__    )
           UpperCAmelCase_										=					self.sp_model.encode(UpperCamelCase__ ,   out_type=UpperCamelCase__    )
           UpperCAmelCase_										=					[]
           for piece in pieces:
                 if len(UpperCamelCase__    ) > 1 and piece[-1] == str(''','''    ) and piece[-2].isdigit():
                       UpperCAmelCase_										=					self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__ ,   ''''''    )    )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]    ) == 1:
                                   UpperCAmelCase_										=					cur_pieces[1:]
                             else:
                                   UpperCAmelCase_										=					cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]    )
                       new_pieces.extend(UpperCamelCase__    )
                 else:
                       new_pieces.append(UpperCamelCase__    )

           return new_pieces
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Union[str, Any]    ):
           return self.sp_model.PieceToId(UpperCamelCase__    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      List[Any]    ):
           return self.sp_model.IdToPiece(UpperCamelCase__    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Optional[Any]    ):
           UpperCAmelCase_										=					''''''.join(UpperCamelCase__    ).replace(UpperCamelCase__ ,   ''' '''    ).strip()
           return out_string
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
           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   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None ,   __snake_case       :      bool = False    ):







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

           if token_ids_a is not None:
                 return ([0] * len(UpperCamelCase__    )) + [1] + ([0] * len(UpperCamelCase__    )) + [1, 1]
           return ([0] * len(UpperCamelCase__    )) + [1, 1]
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
           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   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):







           if not os.path.isdir(UpperCamelCase__    ):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
                 return
           UpperCAmelCase_										=					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:
                       UpperCAmelCase_										=					self.sp_model.serialized_model_proto()
                       fi.write(UpperCamelCase__    )

           return (out_vocab_file,)




     def   lowerCamelCase_       (     self       :      Dict ,   *__snake_case       :      List[Any] ,   **__snake_case       :      int    ):
           UpperCAmelCase_										=					super()._decode(*UpperCamelCase__ ,   **UpperCamelCase__    )
           UpperCAmelCase_										=					text.replace(''' ''' ,   ''''''    ).replace('''\u2582''' ,   ''' '''    ).replace('''\u2583''' ,   '''\n'''    )
           return text











from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  ['pixel_values']
     def __init__(     self       :      Any ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      bool = True ,   __snake_case       :      Union[int, float] = 1 / 2_55 ,   __snake_case       :      bool = True ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,   **__snake_case       :      Optional[Any] ,   ):
         super().__init__(**__snake_case    )
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					resample
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size
         UpperCAmelCase_										=					do_rescale
         UpperCAmelCase_										=					rescale_factor
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_										=					image_std if image_std is not None else IMAGENET_DEFAULT_STD
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Tuple ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
             UpperCAmelCase_										=					int((2_56 / 2_24) * size['''shortest_edge''']    )
             UpperCAmelCase_										=					get_resize_output_image_size(__snake_case ,   size=__snake_case ,   default_to_square=__snake_case    )
             UpperCAmelCase_										=					{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'    )
         return resize(
             __snake_case ,   size=(size_dict['''height'''], size_dict['''width''']) ,   resample=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         if "height" not in size or "width" not in size:
             raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'    )
         return center_crop(__snake_case ,   size=(size['''height'''], size['''width''']) ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[int, float] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      List[Any] ,   ):
         return rescale(__snake_case ,   scale=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Union[str, Any] ,   ):
         return normalize(__snake_case ,   mean=__snake_case ,   std=__snake_case ,   data_format=__snake_case ,   **__snake_case    )




     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      ImageInput ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      PILImageResampling = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[float] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[TensorType] = None ,   __snake_case       :      ChannelDimension = ChannelDimension.FIRST ,   **__snake_case       :      List[str] ,   ):
         UpperCAmelCase_										=					do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_										=					resample if resample is not None else self.resample
         UpperCAmelCase_										=					do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_										=					do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_										=					rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_										=					do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_										=					image_std if image_std is not None else self.image_std

         UpperCAmelCase_										=					size if size is not None else self.size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					make_list_of_images(__snake_case    )

         if not valid_images(__snake_case    ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

         if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''    )

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

         # All transformations expect numpy arrays.
         UpperCAmelCase_										=					[to_numpy_array(__snake_case    ) for image in images]

         if do_resize:
             UpperCAmelCase_										=					[self.resize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         if do_center_crop:
             UpperCAmelCase_										=					[self.center_crop(__snake_case ,   __snake_case    ) for image in images]

         if do_rescale:
             UpperCAmelCase_										=					[self.rescale(__snake_case ,   __snake_case    ) for image in images]

         if do_normalize:
             UpperCAmelCase_										=					[self.normalize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					[to_channel_dimension_format(__snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					{'''pixel_values''': images}
         return BatchFeature(data=__snake_case ,   tensor_type=__snake_case    )





