






"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def       _A    (__a							)				-> Union[str, Any]:
    """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       _A    (__a							)				-> List[str]:
    """simple docstring"""






    for char in word:
        SCREAMING_SNAKE_CASE_   :						int							=	ord(SCREAMING_SNAKE_CASE__							)
        if not _is_chinese_char(SCREAMING_SNAKE_CASE__							):
            return 0
    return 1



def       _A    (__a							)				-> Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	set()

    for token in tokens:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(SCREAMING_SNAKE_CASE__							) > 1 and is_chinese(SCREAMING_SNAKE_CASE__							)
        if chinese_word:
            word_set.add(SCREAMING_SNAKE_CASE__							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	list(SCREAMING_SNAKE_CASE__							)
    return word_list



def       _A    (__a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






    if not chinese_word_set:
        return bert_tokens
    SCREAMING_SNAKE_CASE_   :						Dict							=	max([len(SCREAMING_SNAKE_CASE__							) for w in chinese_word_set]							)

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



def       _A    (__a					,       __a					,       __a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[]

    for i in range(0					,       len(SCREAMING_SNAKE_CASE__							)					,       1_00							):
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	ltp_tokenizer.pipeline(lines[i : i + 1_00]					,       tasks=['''cws''']							).cws
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	[get_chinese_word(SCREAMING_SNAKE_CASE__							) for r in res]
        ltp_res.extend(SCREAMING_SNAKE_CASE__							)
    assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

    SCREAMING_SNAKE_CASE_   :						Dict							=	[]
    for i in range(0					,       len(SCREAMING_SNAKE_CASE__							)					,       1_00							):
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	bert_tokenizer(lines[i : i + 1_00]					,       add_special_tokens=SCREAMING_SNAKE_CASE__					,       truncation=SCREAMING_SNAKE_CASE__					,       max_length=5_12							)
        bert_res.extend(res['''input_ids''']							)
    assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

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

    assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

    return ref_ids



def       _A    (__a							)				-> Dict:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_ref(SCREAMING_SNAKE_CASE__					,       SCREAMING_SNAKE_CASE__					,       SCREAMING_SNAKE_CASE__							)

    with open(args.save_path					,       '''w'''					,       encoding='''utf-8'''							) as f:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[json.dumps(SCREAMING_SNAKE_CASE__							) + """\n""" for ref in ref_ids]
        f.writelines(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
      UpperCAmelCase_     :				Tuple         =				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)










"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			*lowercase_  :						Any		,			**lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


