

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''


     if (
         (cp >= 0x4_e00 and cp <= 0x9_fff)
         or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
         or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
         or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
         or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
         or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
         or (cp >= 0xf_900 and cp <= 0xf_aff)
         or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
     ):  #
          return True

     return False




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''


     for char in word:
          __magic_name__						       =    ord(__UpperCamelCase			)
          if not _is_chinese_char(__UpperCamelCase			):
               return 0
     return 1




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    set()

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




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''


     if not chinese_word_set:
          return bert_tokens
     __magic_name__						       =    max([len(__UpperCamelCase			) for w in chinese_word_set]			)

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




def 						UpperCamelCase	(			a		,						a		,						a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    []

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

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

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

     assert len(__UpperCamelCase			) == len(__UpperCamelCase			)

     return ref_ids




def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






     '''simple docstring'''


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

     __magic_name__						       =    prepare_ref(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase			)

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


if __name__ == "__main__":
      _lowerCAmelCase         =						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         =						parser.parse_args()
      main(args)









'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



 def __call__(   self		:   Tuple			,				a__		:   List[str]      ):
      __magic_name__						       =    []
      for img in imgs:
           __magic_name__       ,  __magic_name__						       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           __magic_name__						       =    np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1      )
           if size == 0:
                return img
           __magic_name__						       =    size * 1.0 / min(a__			,				a__      )
           if h < w:
                __magic_name__       ,  __magic_name__						       =    size, scale * w
           else:
                __magic_name__       ,  __magic_name__						       =    scale * h, size
           if max(a__			,				a__      ) > self.max_size:
                __magic_name__						       =    self.max_size * 1.0 / max(a__			,				a__      )
                __magic_name__						       =    newh * scale
                __magic_name__						       =    neww * scale
           __magic_name__						       =    int(neww + 0.5      )
           __magic_name__						       =    int(newh + 0.5      )

           if img.dtype == np.uinta:
                __magic_name__						       =    Image.fromarray(a__      )
                __magic_name__						       =    pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR      )
                __magic_name__						       =    np.asarray(a__      )
           else:
                __magic_name__						       =    img.permute(2			,				0			,				1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                __magic_name__						       =    nn.functional.interpolate(
                    a__			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=a__      ).squeeze(0      )
           img_augs.append(a__      )

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]			,				a__		:   Tuple      ):
      __magic_name__						       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST      )
      __magic_name__						       =    cfg.INPUT.FORMAT
      __magic_name__						       =    cfg.SIZE_DIVISIBILITY
      __magic_name__						       =    cfg.PAD_VALUE
      __magic_name__						       =    cfg.INPUT.MAX_SIZE_TEST
      __magic_name__						       =    cfg.MODEL.DEVICE
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    lambda a__      : (x - self.pixel_mean) / self.pixel_std



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict      ):
      __magic_name__						       =    tuple(max(a__      ) for s in zip(*[img.shape for img in images]      )      )
      __magic_name__						       =    [im.shape[-2:] for im in images]
      __magic_name__						       =    [
          nn.functional.pad(
              a__			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
          for size, im in zip(a__			,				a__      )
      ]

      return torch.stack(a__      ), torch.tensor(a__      )



 def __call__(   self		:   Dict			,				a__		:   Dict			,				a__		:   List[str]=False      ):
      with torch.no_grad():
           if not isinstance(a__			,				a__      ):
                __magic_name__						       =    [images]
           if single_image:
                assert len(a__      ) == 1
           for i in range(len(a__      )      ):
                if isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(a__			,				images.pop(a__      ).to(self.device      ).float()      )
                elif not isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(
                         a__			,				torch.as_tensor(img_tensorize(images.pop(a__      )			,				input_format=self.input_format      )      )
                         .to(self.device      )
                         .float()			,				)
            # resize smallest edge
           __magic_name__						       =    torch.tensor([im.shape[:2] for im in images]      )
           __magic_name__						       =    self.aug(a__      )
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           __magic_name__						       =    [self.normalizer(a__      ) for x in images]
           # now pad them to do the following operations
           __magic_name__       ,  __magic_name__						       =    self.pad(a__      )
           # Normalize

           if self.size_divisibility > 0:
                raise NotImplementedError()
           # pad
           __magic_name__						       =    torch.true_divide(a__			,				a__      )
           if single_image:
                return images[0], sizes[0], scales_yx[0]
           else:
                return images, sizes, scales_yx




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    box_size
     tensor[:, 0].clamp_(min=0		,						max=a			)
     tensor[:, 1].clamp_(min=0		,						max=a			)
     tensor[:, 2].clamp_(min=0		,						max=a			)
     tensor[:, 3].clamp_(min=0		,						max=a			)






