


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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""pixel_values"""]







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






            '''simple docstring'''




            super().__init__(**A_					)
            __UpperCamelCase =			size if size is not None else {'height': 384, 'width': 384}
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)

            __UpperCamelCase =			do_resize
            __UpperCamelCase =			size
            __UpperCamelCase =			resample
            __UpperCamelCase =			do_rescale
            __UpperCamelCase =			rescale_factor
            __UpperCamelCase =			do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            __UpperCamelCase =			image_std if image_std is not None else OPENAI_CLIP_STD
            __UpperCamelCase =			do_convert_rgb







       def 		snake_case_	(			self: Union[str, Any],A_: np.ndarray,A_: Dict[str, int],A_: PILImageResampling = PILImageResampling.BICUBIC,A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
            __UpperCamelCase =			(size['height'], size['width'])
            return resize(A_,size=A_,resample=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: Optional[Any],A_: np.ndarray,A_: Union[int, float],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




            return rescale(A_,scale=A_,data_format=A_,**A_					)







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






            '''simple docstring'''




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







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






            '''simple docstring'''




            __UpperCamelCase =			do_resize if do_resize is not None else self.do_resize
            __UpperCamelCase =			resample if resample is not None else self.resample
            __UpperCamelCase =			do_rescale if do_rescale is not None else self.do_rescale
            __UpperCamelCase =			rescale_factor if rescale_factor is not None else self.rescale_factor
            __UpperCamelCase =			do_normalize if do_normalize is not None else self.do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else self.image_mean
            __UpperCamelCase =			image_std if image_std is not None else self.image_std
            __UpperCamelCase =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            __UpperCamelCase =			size if size is not None else self.size
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)

            __UpperCamelCase =			make_list_of_images(A_					)

            if not valid_images(A_					):
                 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_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.'					)

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                 __UpperCamelCase =			[convert_to_rgb(A_					) for image in images]

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

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

            if do_rescale:
                 __UpperCamelCase =			[self.rescale(image=A_,scale=A_					) for image in images]

            if do_normalize:
                 __UpperCamelCase =			[self.normalize(image=A_,mean=A_,std=A_					) for image in images]

            __UpperCamelCase =			[to_channel_dimension_format(A_,A_					) for image in images]

            __UpperCamelCase =			BatchFeature(data={'pixel_values': images},tensor_type=A_					)

            return encoded_outputs






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 	_UpperCamelCase					(    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 >= 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_ )    ->List[str]:
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCAmelCase      					=  ord(lowerCAmelCase_ )
      if not _is_chinese_char(lowerCAmelCase_ ):
         return 0
   return 1
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  set()

   for token in tokens:
      UpperCAmelCase      					=  len(lowerCAmelCase_ ) > 1 and is_chinese(lowerCAmelCase_ )
      if chinese_word:
         word_set.add(lowerCAmelCase_ )
   UpperCAmelCase      					=  list(lowerCAmelCase_ )
   return word_list
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   if not chinese_word_set:
      return bert_tokens
   UpperCAmelCase      					=  max([len(lowerCAmelCase_ ) for w in chinese_word_set] )

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

   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]	,    tasks=["""cws"""] ).cws
      UpperCAmelCase      					=  [get_chinese_word(lowerCAmelCase_ ) for r in res]
      ltp_res.extend(lowerCAmelCase_ )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   UpperCAmelCase      					=  []
   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  bert_tokenizer(lines[i : i + 1_0_0]	,    add_special_tokens=lowerCAmelCase_	,    truncation=lowerCAmelCase_	,    max_length=5_1_2 )
      bert_res.extend(res["""input_ids"""] )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

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

   UpperCAmelCase      					=  prepare_ref(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

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


if __name__ == "__main__":
 __a	 =      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""",
 )

 __a	 =      parser.parse_args()
 main(args)



