




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

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


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

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

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

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

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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:
    __a			=					f.readlines()
  __a			=					[line.strip() for line in data if len(a_			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  __a			=					LTP(args.ltp			)  # faster in GPU device
  __a			=					BertTokenizer.from_pretrained(args.bert			)

  __a			=					prepare_ref(a_				,					a_				,					a_			)

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


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser(description="prepare_chinese_ref")
  parser.add_argument(
      "--file_name",
      type=str,
      default="./resources/chinese-demo.txt",
      help="file need process, same as training data in lm",
  )
  parser.add_argument(
      "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
  )
  parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
  parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)









'''simple docstring'''


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 (
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     return normalize(UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					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_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     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.'      )

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

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

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

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

     __a			=					[to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      ) for image in images]

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



