


'''simple docstring'''






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


__A :  List[str]    							=				logging.get_logger(__name__)

__A :  int    							=				{"vocab_file": "spiece.model"}

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






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



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

      lowerCAmelCase_      :				str      =       {} if sp_model_kwargs is None else sp_model_kwargs

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

      lowerCAmelCase_      :				str      =       3

      lowerCAmelCase_      :				Any      =       do_lower_case
      lowerCAmelCase_      :				Dict      =       remove_space
      lowerCAmelCase_      :				List[str]      =       keep_accents
      lowerCAmelCase_      :				Optional[Any]      =       vocab_file

      lowerCAmelCase_      :				List[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.Load(lowerCamelCase		)

      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."""		)
      lowerCAmelCase_      :				Union[str, Any]      =       jieba
      lowerCAmelCase_      :				List[Any]      =       str.maketrans(""" \n"""   ,						"""\u2582\u2583"""		)

    @property
    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
    def __lowercase       (       self   :  List[str]		)       ->  int:
      return len(self.sp_model		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Union[str, Any]      =       {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
      vocab.update(self.added_tokens_encoder		)
      return vocab

    def __getstate__(       self   :  Dict		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       self.__dict__.copy()
      lowerCAmelCase_      :				Optional[int]      =       None
      return state

    def __setstate__(       self   :  Tuple   ,						lowerCamelCase   :  Dict		)       ->  int:
      lowerCAmelCase_      :				Optional[Any]      =       d

      # for backward compatibility
      if not hasattr(self   ,						"""sp_model_kwargs"""		):
        lowerCAmelCase_      :				List[Any]      =       {}

      lowerCAmelCase_      :				List[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.Load(self.vocab_file		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]		)       ->  Optional[Any]:
      if self.remove_space:
        lowerCAmelCase_      :				int      =       """ """.join(inputs.strip().split()		)
      else:
        lowerCAmelCase_      :				Optional[Any]      =       inputs
      lowerCAmelCase_      :				Dict      =       outputs.replace("""``"""   ,						"""\""""		).replace("""''"""   ,						"""\""""		)

      if not self.keep_accents:
        lowerCAmelCase_      :				Tuple      =       unicodedata.normalize("""NFKD"""   ,						lowerCamelCase		)
        lowerCAmelCase_      :				Union[str, Any]      =       """""".join([c for c in outputs if not unicodedata.combining(lowerCamelCase		)]		)
      if self.do_lower_case:
        lowerCAmelCase_      :				Union[str, Any]      =       outputs.lower()

      return outputs

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  str		)       ->  List[str]:
      lowerCAmelCase_      :				List[Any]      =       self.preprocess_text(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       self.sp_model.encode(lowerCamelCase   ,						out_type=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       []
      for piece in pieces:
        if len(lowerCamelCase		) > 1 and piece[-1] == str(""","""		) and piece[-2].isdigit():
          lowerCAmelCase_      :				int      =       self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase   ,						""""""		)		)
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
            if len(cur_pieces[0]		) == 1:
              lowerCAmelCase_      :				Dict      =       cur_pieces[1:]
            else:
              lowerCAmelCase_      :				Optional[int]      =       cur_pieces[0][1:]
          cur_pieces.append(piece[-1]		)
          new_pieces.extend(lowerCamelCase		)
        else:
          new_pieces.append(lowerCamelCase		)

      return new_pieces

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  str		)       ->  Optional[int]:
      return self.sp_model.PieceToId(lowerCamelCase		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Tuple		)       ->  Optional[int]:
      return self.sp_model.IdToPiece(lowerCamelCase		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Tuple		)       ->  int:
      lowerCAmelCase_      :				Optional[int]      =       """""".join(lowerCamelCase		).replace(lowerCamelCase   ,						""" """		).strip()
      return out_string

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None		)       ->  List[int]:
      lowerCAmelCase_      :				List[str]      =       [self.sep_token_id]
      lowerCAmelCase_      :				str      =       [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   :  str   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None   ,						lowerCamelCase   :  bool = False		)       ->  List[int]:

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

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

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None		)       ->  List[int]:
      lowerCAmelCase_      :				Union[str, Any]      =       [self.sep_token_id]
      lowerCAmelCase_      :				Tuple      =       [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   :  Tuple   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[str] = None		)       ->  Tuple[str]:
      if not os.path.isdir(lowerCamelCase		):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
        return
      lowerCAmelCase_      :				Any      =       os.path.join(
          lowerCamelCase   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

      return (out_vocab_file,)





    def __lowercase       (       self   :  Optional[Any]   ,						*lowerCamelCase   :  Optional[int]   ,						**lowerCamelCase   :  Optional[int]		)       ->  List[Any]:
      lowerCAmelCase_      :				str      =       super()._decode(*lowerCamelCase   ,						**lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       text.replace(""" """   ,						""""""		).replace("""\u2582"""   ,						""" """		).replace("""\u2583"""   ,						"""\n"""		)
      return text







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



