





'''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 : List[Any]			):
   # This defines a "chinese character" as anything in the CJK Unicode block:
   #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
   #
   # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
   # despite its name. The modern Korean Hangul alphabet is a different block,
   # as is Japanese Hiragana and Katakana. Those alphabets are used to write
   # space-separated words, so they are not treated specially and handled
   # like the all of the other languages.
   if (
       (cp >= 0x4E00 and cp <= 0x9FFF)
       or (cp >= 0x3400 and cp <= 0x4DBF)  #
       or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
       or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
       or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
       or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
       or (cp >= 0xF900 and cp <= 0xFAFF)
       or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
   ):  #
      return True

   return False
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   # word like '180' or '身高' or '神'
   for char in word:
      lowercase_      :List[str]         =			ord(__lowerCamelCase			)
      if not _is_chinese_char(__lowerCamelCase			):
         return 0
   return 1
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):
   lowercase_      :Optional[Any]         =			set()

   for token in tokens:
      lowercase_      :Optional[Any]         =			len(__lowerCamelCase			) > 1 and is_chinese(__lowerCamelCase			)
      if chinese_word:
         word_set.add(__lowerCamelCase			)
   lowercase_      :Any         =			list(__lowerCamelCase			)
   return word_list
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : set()			):
   if not chinese_word_set:
      return bert_tokens
   lowercase_      :Tuple         =			max([len(__lowerCamelCase			) for w in chinese_word_set]			)

   lowercase_      :Optional[int]         =			bert_tokens
   lowercase_      ,							lowercase_      :int         =			0, len(__lowerCamelCase			)
   while start < end:
      lowercase_      :Optional[int]         =			True
      if is_chinese(bert_word[start]			):
         lowercase_      :Dict         =			min(end - start     ,__lowerCamelCase			)
         for i in range(__lowerCamelCase     ,1     ,-1			):
            lowercase_      :Optional[Any]         =			"".join(bert_word[start : start + i]			)
            if whole_word in chinese_word_set:
               for j in range(start + 1     ,start + i			):
                  lowercase_      :List[Any]         =			"##" + bert_word[j]
               lowercase_      :List[Any]         =			start + i
               lowercase_      :str         =			False
               break
      if single_word:
         start += 1
   return bert_word
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : LTP     ,__lowerCamelCase : BertTokenizer			):
   lowercase_      :Union[str, Any]         =			[]

   for i in range(0     ,len(__lowerCamelCase			)     ,1_00			):
      lowercase_      :Tuple         =			ltp_tokenizer.pipeline(lines[i : i + 1_00]     ,tasks=["cws"]			).cws
      lowercase_      :Optional[int]         =			[get_chinese_word(__lowerCamelCase			) for r in res]
      ltp_res.extend(__lowerCamelCase			)
   assert len(__lowerCamelCase			) == len(__lowerCamelCase			)

   lowercase_      :Dict         =			[]
   for i in range(0     ,len(__lowerCamelCase			)     ,1_00			):
      lowercase_      :Optional[Any]         =			bert_tokenizer(lines[i : i + 1_00]     ,add_special_tokens=__lowerCamelCase     ,truncation=__lowerCamelCase     ,max_length=5_12			)
      bert_res.extend(res["input_ids"]			)
   assert len(__lowerCamelCase			) == len(__lowerCamelCase			)

   lowercase_      :Optional[int]         =			[]
   for input_ids, chinese_word in zip(__lowerCamelCase     ,__lowerCamelCase			):
      lowercase_      :List[Any]         =			[]
      for id in input_ids:
         lowercase_      :Union[str, Any]         =			bert_tokenizer._convert_id_to_token(__lowerCamelCase			)
         input_tokens.append(__lowerCamelCase			)
      lowercase_      :Optional[int]         =			add_sub_symbol(__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :Any         =			[]
      # 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] == "##":
            lowercase_      :List[str]         =			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 : Any			):
   # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
   # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
   with open(args.file_name     ,"r"     ,encoding="utf-8"			) as f:
      lowercase_      :Union[str, Any]         =			f.readlines()
   lowercase_      :List[str]         =			[line.strip() for line in data if len(__lowerCamelCase			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   lowercase_      :Dict         =			LTP(args.ltp			)  # faster in GPU device
   lowercase_      :int         =			BertTokenizer.from_pretrained(args.bert			)

   lowercase_      :Dict         =			prepare_ref(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)

   with open(args.save_path     ,"w"     ,encoding="utf-8"			) as f:
      lowercase_      :Dict         =			[json.dumps(__lowerCamelCase			) + "\n" for ref in ref_ids]
      f.writelines(__lowerCamelCase			)


if __name__ == "__main__":
 lowerCAmelCase :					Any 							=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''',
 )

 lowerCAmelCase :					int 							=parser.parse_args()
 main(args)









'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

lowerCAmelCase :					Any 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):
   if isinstance(__lowerCamelCase     ,(list, tuple)			) and isinstance(videos[0]     ,(list, tuple)			) and is_valid_image(videos[0][0]			):
      return videos

   elif isinstance(__lowerCamelCase     ,(list, tuple)			) and is_valid_image(videos[0]			):
      return [videos]

   elif is_valid_image(__lowerCamelCase			):
      return [[videos]]

   raise ValueError(F'Could not make batched video from {videos}'			)


class 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: List[str]     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = True     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: bool = True     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				**lowercase		: Tuple     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :Any         =			size if size is not None else {"shortest_edge": 256}
        lowercase_      :int         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        lowercase_      :str         =			crop_size if crop_size is not None else {"height": 224, "width": 224}
        lowercase_      :List[str]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

        lowercase_      :List[str]         =			do_resize
        lowercase_      :Any         =			size
        lowercase_      :Union[str, Any]         =			do_center_crop
        lowercase_      :Union[str, Any]         =			crop_size
        lowercase_      :Optional[Any]         =			resample
        lowercase_      :List[str]         =			do_rescale
        lowercase_      :List[Any]         =			rescale_factor
        lowercase_      :Dict         =			offset
        lowercase_      :Optional[Any]         =			do_normalize
        lowercase_      :Dict         =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase_      :Optional[int]         =			image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Optional[Any]     ,				):





        """simple docstring"""




        lowercase_      :List[Any]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        if "shortest_edge" in size:
           lowercase_      :int         =			get_resize_output_image_size(lowercase     ,				size["shortest_edge"]     ,				default_to_square=lowercase						)
        elif "height" in size and "width" in size:
           lowercase_      :Union[str, Any]         =			(size["height"], size["width"])
        else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
        return resize(lowercase     ,				size=lowercase     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: str     ,				):





        """simple docstring"""




        lowercase_      :Any         =			get_size_dict(lowercase						)
        if "height" not in size or "width" not in size:
           raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'						)
        return center_crop(lowercase     ,				size=(size["height"], size["width"])     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: np.ndarray     ,				lowercase		: Union[int, float]     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: List[str]     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			image.astype(np.floataa						)
        if offset:
           lowercase_      :List[str]         =			image - (scale / 2)
        return rescale(lowercase     ,				scale=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: np.ndarray     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




        return normalize(lowercase     ,				mean=lowercase     ,				std=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: ImageInput     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = None     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = None     ,				lowercase		: float = None     ,				lowercase		: bool = None     ,				lowercase		: bool = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[ChannelDimension] = ChannelDimension.FIRST     ,				):





        """simple docstring"""




        if do_resize and size is None or resample is None:
           raise ValueError("Size and resample 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."						)

        if offset and not do_rescale:
           raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        lowercase_      :Optional[int]         =			to_numpy_array(lowercase						)

        if do_resize:
           lowercase_      :Tuple         =			self.resize(image=lowercase     ,				size=lowercase     ,				resample=lowercase						)

        if do_center_crop:
           lowercase_      :Any         =			self.center_crop(lowercase     ,				size=lowercase						)

        if do_rescale:
           lowercase_      :Optional[Any]         =			self.rescale(image=lowercase     ,				scale=lowercase     ,				offset=lowercase						)

        if do_normalize:
           lowercase_      :Tuple         =			self.normalize(image=lowercase     ,				mean=lowercase     ,				std=lowercase						)

        lowercase_      :Optional[Any]         =			to_channel_dimension_format(lowercase     ,				lowercase						)
        return image







     def 							lowercase__				(			self		: Dict     ,				lowercase		: ImageInput     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = None     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = None     ,				lowercase		: float = None     ,				lowercase		: bool = None     ,				lowercase		: bool = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: ChannelDimension = ChannelDimension.FIRST     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        lowercase_      :str         =			do_resize if do_resize is not None else self.do_resize
        lowercase_      :Optional[Any]         =			resample if resample is not None else self.resample
        lowercase_      :Tuple         =			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_      :Dict         =			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :Optional[int]         =			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :Dict         =			offset if offset is not None else self.offset
        lowercase_      :Tuple         =			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_      :int         =			image_mean if image_mean is not None else self.image_mean
        lowercase_      :Optional[int]         =			image_std if image_std is not None else self.image_std

        lowercase_      :int         =			size if size is not None else self.size
        lowercase_      :Optional[int]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        lowercase_      :List[Any]         =			crop_size if crop_size is not None else self.crop_size
        lowercase_      :List[str]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

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

        lowercase_      :List[str]         =			make_batched(lowercase						)

        lowercase_      :List[Any]         =			[
            [
                self._preprocess_image(
                    image=lowercase     ,				do_resize=lowercase     ,				size=lowercase     ,				resample=lowercase     ,				do_center_crop=lowercase     ,				crop_size=lowercase     ,				do_rescale=lowercase     ,				rescale_factor=lowercase     ,				offset=lowercase     ,				do_normalize=lowercase     ,				image_mean=lowercase     ,				image_std=lowercase     ,				data_format=lowercase     ,				)
                for img in video
            ]
            for video in videos
        ]

        lowercase_      :Optional[int]         =			{"pixel_values": videos}
        return BatchFeature(data=lowercase     ,				tensor_type=lowercase						)


