



"""simple docstring"""







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]						)						->       Optional[Any]:

  """simple docstring"""




  if (
      (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
      or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
      or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
      or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
      or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
      or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
      or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
      or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
  ):  #
    return True

  return False

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       Tuple:

  """simple docstring"""




  for char in word:
    lowerCAmelCase_       :    List[Any]								=			ord(lowerCAmelCase__						)
    if not _is_chinese_char(lowerCAmelCase__						):
      return 0
  return 1

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			set()

  for token in tokens:
    lowerCAmelCase_       :    Dict								=			len(lowerCAmelCase__						) > 1 and is_chinese(lowerCAmelCase__						)
    if chinese_word:
      word_set.add(lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			list(lowerCAmelCase__						)
  return word_list

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    set()						)						->       int:

  """simple docstring"""




  if not chinese_word_set:
    return bert_tokens
  lowerCAmelCase_       :    List[str]								=			max([len(lowerCAmelCase__						) for w in chinese_word_set]						)

  lowerCAmelCase_       :    Dict								=			bert_tokens
  lowerCAmelCase_       :    Dict								=			0, len(lowerCAmelCase__						)
  while start < end:
    lowerCAmelCase_       :    str								=			True
    if is_chinese(bert_word[start]						):
      lowerCAmelCase_       :    Union[str, Any]								=			min(end - start   ,       lowerCAmelCase__						)
      for i in range(lowerCAmelCase__   ,       1   ,       -1						):
        lowerCAmelCase_       :    str								=			''.join(bert_word[start : start + i]						)
        if whole_word in chinese_word_set:
          for j in range(start + 1   ,       start + i						):
            lowerCAmelCase_       :    Dict								=			'##' + bert_word[j]
          lowerCAmelCase_       :    int								=			start + i
          lowerCAmelCase_       :    str								=			False
          break
    if single_word:
      start += 1
  return bert_word

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    LTP   ,       lowerCAmelCase__				:    BertTokenizer						)						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			[]

  for i in range(0   ,       len(lowerCAmelCase__						)   ,       100						):
    lowerCAmelCase_       :    Any								=			ltp_tokenizer.pipeline(lines[i : i + 100]   ,       tasks=['cws']						).cws
    lowerCAmelCase_       :    Optional[int]								=			[get_chinese_word(lowerCAmelCase__						) for r in res]
    ltp_res.extend(lowerCAmelCase__						)
  assert len(lowerCAmelCase__						) == len(lowerCAmelCase__						)

  lowerCAmelCase_       :    Optional[Any]								=			[]
  for i in range(0   ,       len(lowerCAmelCase__						)   ,       100						):
    lowerCAmelCase_       :    str								=			bert_tokenizer(lines[i : i + 100]   ,       add_special_tokens=lowerCAmelCase__   ,       truncation=lowerCAmelCase__   ,       max_length=512						)
    bert_res.extend(res['input_ids']						)
  assert len(lowerCAmelCase__						) == len(lowerCAmelCase__						)

  lowerCAmelCase_       :    int								=			[]
  for input_ids, chinese_word in zip(lowerCAmelCase__   ,       lowerCAmelCase__						):
    lowerCAmelCase_       :    Optional[int]								=			[]
    for id in input_ids:
      lowerCAmelCase_       :    Optional[Any]								=			bert_tokenizer._convert_id_to_token(lowerCAmelCase__						)
      input_tokens.append(lowerCAmelCase__						)
    lowerCAmelCase_       :    Optional[Any]								=			add_sub_symbol(lowerCAmelCase__   ,       lowerCAmelCase__						)
    lowerCAmelCase_       :    int								=			[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(lowerCAmelCase__						):
      if token[:2] == "##":
        lowerCAmelCase_       :    int								=			token[2:]
        # save chinese tokens' pos
        if len(lowerCAmelCase__						) == 1 and _is_chinese_char(ord(lowerCAmelCase__						)						):
          ref_id.append(lowerCAmelCase__						)
    ref_ids.append(lowerCAmelCase__						)

  assert len(lowerCAmelCase__						) == len(lowerCAmelCase__						)

  return ref_ids

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]						)						->       Optional[int]:

  """simple docstring"""




  with open(args.file_name   ,       'r'   ,       encoding='utf-8'						) as f:
    lowerCAmelCase_       :    List[Any]								=			f.readlines()
  lowerCAmelCase_       :    Union[str, Any]								=			[line.strip() for line in data if len(lowerCAmelCase__						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  lowerCAmelCase_       :    Any								=			LTP(args.ltp						)  # faster in GPU device
  lowerCAmelCase_       :    int								=			BertTokenizer.from_pretrained(args.bert						)

  lowerCAmelCase_       :    Tuple								=			prepare_ref(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

  with open(args.save_path   ,       'w'   ,       encoding='utf-8'						) as f:
    lowerCAmelCase_       :    str								=			[json.dumps(lowerCAmelCase__						) + '\n' for ref in ref_ids]
    f.writelines(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:    str				   =     argparse.ArgumentParser(description="""prepare_chinese_ref""")
     parser.add_argument(
         """--file_name""",
         required=False,
         type=str,
         default="""./resources/chinese-demo.txt""",
         help="""file need process, same as training data in lm""",
     )
     parser.add_argument(
         """--ltp""",
         required=False,
         type=str,
         default="""./resources/ltp""",
         help="""resources for LTP tokenizer, usually a path""",
     )
     parser.add_argument(
         """--bert""",
         required=False,
         type=str,
         default="""./resources/robert""",
         help="""resources for Bert tokenizer""",
     )
     parser.add_argument(
         """--save_path""",
         required=False,
         type=str,
         default="""./resources/ref.txt""",
         help="""path to save res""",
     )

     lowercase__		:    Dict				   =     parser.parse_args()
     main(args)





"""simple docstring"""







from typing import Dict, 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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:    Tuple				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""pixel_values"""]



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, int]] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BICUBIC   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Union[int, float] = 1 / 2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[str]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      default_to_square=SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'	)

        lowerCAmelCase_       :    List[Any]								=			do_resize
        lowerCAmelCase_       :    Any								=			do_rescale
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    List[str]								=			do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size
        lowerCAmelCase_       :    Optional[Any]								=			size
        lowerCAmelCase_       :    Tuple								=			resample
        lowerCAmelCase_       :    Optional[int]								=			rescale_factor
        lowerCAmelCase_       :    Optional[Any]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BILINEAR   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if "shortest_edge" in size:
          lowerCAmelCase_       :    Any								=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,      size=size['shortest_edge']   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          lowerCAmelCase_       :    Union[str, Any]								=			(size['height'], size['width'])
        else:
          raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"	)
        return resize(SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Any								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(SCREAMING_SNAKE_CASE_   ,      size=(size['height'], size['width'])   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				float   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return rescale(SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        return normalize(SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				ImageInput   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = None   ,      SCREAMING_SNAKE_CASE_    :				bool = None   ,      SCREAMING_SNAKE_CASE_    :				int = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_       :    Optional[int]								=			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_       :    Optional[Any]								=			do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_       :    Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_       :    Optional[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			resample if resample is not None else self.resample
        lowerCAmelCase_       :    Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_       :    str								=			image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else self.image_std

        lowerCAmelCase_       :    Tuple								=			size if size is not None else self.size
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if not is_batched(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[Any]								=			[images]

        if not valid_images(SCREAMING_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.'	)

        # All transformations expect numpy arrays.
        lowerCAmelCase_       :    str								=			[to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

        if do_resize:
          lowerCAmelCase_       :    Any								=			[self.resize(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_center_crop:
          lowerCAmelCase_       :    Optional[int]								=			[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_rescale:
          lowerCAmelCase_       :    Tuple								=			[self.rescale(image=SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_normalize:
          lowerCAmelCase_       :    str								=			[self.normalize(image=SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Any								=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Optional[Any]								=			{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,      tensor_type=SCREAMING_SNAKE_CASE_	)





