






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 					lowercase_ (    __snake_case     :							Dict      )     -> str:





				'''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 					lowercase_ (    __snake_case     :							int      )     -> str:





				'''simple docstring'''


				for char in word:
								snake_case__      :Dict         =   ord(__snake_case      )
								if not _is_chinese_char(__snake_case      ):
												return 0
				return 1





def 					lowercase_ (    __snake_case     :							Optional[int]      )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Any         =   set()

				for token in tokens:
								snake_case__      :Any         =   len(__snake_case      ) > 1 and is_chinese(__snake_case      )
								if chinese_word:
												word_set.add(__snake_case      )
				snake_case__      :str         =   list(__snake_case      )
				return word_list





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Optional[int]      )     -> List[str]:





				'''simple docstring'''


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

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





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							Tuple	,	__snake_case     :							Any      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :str         =   []

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

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

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

				assert len(__snake_case      ) == len(__snake_case      )

				return ref_ids





def 					lowercase_ (    __snake_case     :							List[str]      )     -> Dict:





				'''simple docstring'''


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

				snake_case__      :List[str]         =   prepare_ref(__snake_case	,	__snake_case	,	__snake_case      )

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


if __name__ == "__main__":
	__UpperCAmelCase							:   Dict							=  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",
	)

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







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


__UpperCAmelCase							:   Optional[Any]							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> Any:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							int      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   x.reshape(-1	,	3      )
				snake_case__      :List[str]         =   squared_euclidean_distance(__snake_case	,	__snake_case      )
				return np.argmin(__snake_case	,	axis=1      )


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :List[Any]         =   size if size is not None else {"height": 256, "width": 256}
					snake_case__      :str         =   get_size_dict(UpperCamelCase       )
					snake_case__      :Dict         =   np.array(UpperCamelCase       ) if clusters is not None else None
					snake_case__      :str         =   do_resize
					snake_case__      :List[str]         =   size
					snake_case__      :List[Any]         =   resample
					snake_case__      :Union[str, Any]         =   do_normalize
					snake_case__      :int         =   do_color_quantize






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :List[str]         =   get_size_dict(UpperCamelCase       )
					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(
					    UpperCamelCase					,size=(size["height"], size["width"])					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,)						->    np.ndarray:
					snake_case__      :Tuple         =   rescale(image=UpperCamelCase					,scale=1 / 127.5					,data_format=UpperCamelCase       )
					snake_case__      :List[Any]         =   image - 1
					return image






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Optional[int]         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :int         =   size if size is not None else self.size
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase       )
					snake_case__      :str         =   resample if resample is not None else self.resample
					snake_case__      :Dict         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :Tuple         =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					snake_case__      :List[Any]         =   clusters if clusters is not None else self.clusters
					snake_case__      :str         =   np.array(UpperCamelCase       )

					snake_case__      :int         =   make_list_of_images(UpperCamelCase       )

					if not valid_images(UpperCamelCase       ):
									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.
					snake_case__      :Union[str, Any]         =   [to_numpy_array(UpperCamelCase       ) for image in images]

					if do_resize:
									snake_case__      :int         =   [self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       ) for image in images]

					if do_normalize:
									snake_case__      :Any         =   [self.normalize(image=UpperCamelCase       ) for image in images]

					if do_color_quantize:
									snake_case__      :Optional[Any]         =   [to_channel_dimension_format(UpperCamelCase					,ChannelDimension.LAST       ) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									snake_case__      :Union[str, Any]         =   np.array(UpperCamelCase       )
									snake_case__      :Optional[int]         =   color_quantize(UpperCamelCase					,UpperCamelCase       ).reshape(images.shape[:-1]       )

									# flatten to (batch_size, height*width)
									snake_case__      :List[Any]         =   images.shape[0]
									snake_case__      :str         =   images.reshape(UpperCamelCase					,-1       )

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

					snake_case__      :List[str]         =   {"input_ids": images}
					return BatchFeature(data=UpperCamelCase					,tensor_type=UpperCamelCase       )