





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


_snake_case  :   List[Any]						   =  logging.get_logger(__name__)

def   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     b.T
   __lowerCAmelCase							     =     np.sum(np.square(lowerCAmelCase_     ),     axis=1     )
   __lowerCAmelCase							     =     np.sum(np.square(lowerCAmelCase_     ),     axis=0     )
   __lowerCAmelCase							     =     np.matmul(lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     aa[:, None] - 2 * ab + ba[None, :]
   return d

def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Union[str, Any]     ):
   __lowerCAmelCase							     =     x.reshape(-1,     3     )
   __lowerCAmelCase							     =     squared_euclidean_distance(lowerCAmelCase_,     lowerCAmelCase_     )
   return np.argmin(lowerCAmelCase_,     axis=1     )




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""pixel_values"""]






  def __init__(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[Union[List[List[int]], np.ndarray]] = None ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  **lowerCAmelCase_			:		Union[str, Any] ,  )							->						None:
     super().__init__(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     np.array(lowerCAmelCase_					) if clusters is not None else None
     __lowerCAmelCase							     =     do_resize
     __lowerCAmelCase							     =     size
     __lowerCAmelCase							     =     resample
     __lowerCAmelCase							     =     do_normalize
     __lowerCAmelCase							     =     do_color_quantize






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Dict[str, int] ,  lowerCAmelCase_			:		PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""					)
     return resize(
         lowerCAmelCase_ ,  size=(size['height'], size['width']) ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     rescale(image=lowerCAmelCase_ ,  scale=1 / 1_27.5 ,  data_format=lowerCAmelCase_					)
     __lowerCAmelCase							     =     image - 1
     return image






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		ImageInput ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		PILImageResampling = None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Optional[bool] = None ,  lowerCAmelCase_			:		Optional[Union[List[List[int]], np.ndarray]] = None ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,  **lowerCAmelCase_			:		Union[str, Any] ,  )							->						PIL.Image.Image:
     __lowerCAmelCase							     =     do_resize if do_resize is not None else self.do_resize
     __lowerCAmelCase							     =     size if size is not None else self.size
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     resample if resample is not None else self.resample
     __lowerCAmelCase							     =     do_normalize if do_normalize is not None else self.do_normalize
     __lowerCAmelCase							     =     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
     __lowerCAmelCase							     =     clusters if clusters is not None else self.clusters
     __lowerCAmelCase							     =     np.array(lowerCAmelCase_					)

     __lowerCAmelCase							     =     make_list_of_images(lowerCAmelCase_					)

     if not valid_images(lowerCAmelCase_					):
        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 or resample is None:
        raise ValueError('Size and resample must be specified if do_resize is True.'					)

     if do_color_quantize and clusters is None:
        raise ValueError('Clusters must be specified if do_color_quantize is True.'					)

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

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

     if do_normalize:
        __lowerCAmelCase							     =     [self.normalize(image=lowerCAmelCase_					) for image in images]

     if do_color_quantize:
        __lowerCAmelCase							     =     [to_channel_dimension_format(lowerCAmelCase_ ,  ChannelDimension.LAST					) for image in images]
        # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
        __lowerCAmelCase							     =     np.array(lowerCAmelCase_					)
        __lowerCAmelCase							     =     color_quantize(lowerCAmelCase_ ,  lowerCAmelCase_					).reshape(images.shape[:-1]					)

        # flatten to (batch_size, height*width)
        __lowerCAmelCase							     =     images.shape[0]
        __lowerCAmelCase							     =     images.reshape(lowerCAmelCase_ ,  -1					)

        # We need to convert back to a list of images to keep consistent behaviour across processors.
        __lowerCAmelCase							     =     list(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     [to_channel_dimension_format(lowerCAmelCase_ ,  lowerCAmelCase_					) for image in images]

     __lowerCAmelCase							     =     {'input_ids': images}
     return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_					)








import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def   a_   (			lowerCAmelCase_				:      Optional[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 >= 0X20000 and cp <= 0X2a6df)  #
       or (cp >= 0X2a700 and cp <= 0X2b73f)  #
       or (cp >= 0X2b740 and cp <= 0X2b81f)  #
       or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
       or (cp >= 0Xf900 and cp <= 0Xfaff)
       or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
   ):  #
      return True

   return False

def   a_   (			lowerCAmelCase_				:      str     ):
   # word like '180' or '身高' or '神'
   for char in word:
      __lowerCAmelCase							     =     ord(lowerCAmelCase_     )
      if not _is_chinese_char(lowerCAmelCase_     ):
         return 0
   return 1

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     set()

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

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      set()     ):
   if not chinese_word_set:
      return bert_tokens
   __lowerCAmelCase							     =     max([len(lowerCAmelCase_     ) for w in chinese_word_set]     )

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

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      LTP,     lowerCAmelCase_				:      BertTokenizer     ):
   __lowerCAmelCase							     =     []

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

   __lowerCAmelCase							     =     []
   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     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							     =     []
   for input_ids, chinese_word in zip(lowerCAmelCase_,     lowerCAmelCase_     ):
      __lowerCAmelCase							     =     []
      for id in input_ids:
         __lowerCAmelCase							     =     bert_tokenizer._convert_id_to_token(lowerCAmelCase_     )
         input_tokens.append(lowerCAmelCase_     )
      __lowerCAmelCase							     =     add_sub_symbol(lowerCAmelCase_,     lowerCAmelCase_     )
      __lowerCAmelCase							     =     []
      # 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							     =     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   a_   (			lowerCAmelCase_				:      int     ):
   # 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:
      __lowerCAmelCase							     =     f.readlines()
   __lowerCAmelCase							     =     [line.strip() for line in data if len(lowerCAmelCase_     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   __lowerCAmelCase							     =     LTP(args.ltp     )  # faster in GPU device
   __lowerCAmelCase							     =     BertTokenizer.from_pretrained(args.bert     )

   __lowerCAmelCase							     =     prepare_ref(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   int						   =  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',
     )

     _snake_case  :   List[str]						   =  parser.parse_args()
     main(args)

