


'''simple docstring'''


from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__magic_name__			=      logging.get_logger(__name__)


__magic_name__			=      Dict[str, Any]
__magic_name__			=      List[Prediction]



@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       Optional[Any]	,*_a :       List[str]	,**_a :       List[str] ):

        '''simple docstring'''




        super().__init__(*_a	,**_a )

        if self.framework == "tf":
              raise ValueError(f'The {self.__class__} is only available in PyTorch.' )

        requires_backends(self	,"""vision""" )
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )






  def        _a							(							self :       List[Any]	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  str          =				{}
        if "threshold" in kwargs:
              A_						:  Tuple          =				kwargs["""threshold"""]
        return {}, {}, postprocess_kwargs






  def __call__(							self :       Optional[int]	,*_a :       Tuple	,**_a :       Tuple ):

        '''simple docstring'''




        return super().__call__(*_a	,**_a )






  def        _a							(							self :       Optional[Any]	,_a :       Any ):

        '''simple docstring'''




        A_						:  Optional[int]          =				load_image(_a )
        A_						:  List[Any]          =				torch.IntTensor([[image.height, image.width]] )
        A_						:  Union[str, Any]          =				self.image_processor(images=[image]	,return_tensors="""pt""" )
        if self.tokenizer is not None:
              A_						:  List[str]          =				self.tokenizer(text=inputs["""words"""]	,boxes=inputs["""boxes"""]	,return_tensors="""pt""" )
        A_						:  Tuple          =				target_size
        return inputs






  def        _a							(							self :       Any	,_a :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				model_inputs.pop("""target_size""" )
        A_						:  str          =				self.model(**_a )
        A_						:  Optional[Any]          =				outputs.__class__({"""target_size""": target_size, **outputs} )
        if self.tokenizer is not None:
              A_						:  Dict          =				model_inputs["""bbox"""]
        return model_outputs






  def        _a							(							self :       Optional[int]	,_a :       Optional[Any]	,_a :       Dict=0.9 ):

        '''simple docstring'''




        A_						:  Tuple          =				model_outputs["""target_size"""]
        if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              A_   ,					A_						:  Optional[Any]          =				target_size[0].tolist()

              def unnormalize(_a :       List[Any] ):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ] ) )

              A_   ,					A_						:  Dict          =				model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
              A_						:  int          =				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              A_						:  Optional[Any]          =				[unnormalize(_a ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
              A_						:  Union[str, Any]          =				["""score""", """label""", """box"""]
              A_						:  Tuple          =				[dict(zip(_a	,_a ) ) for vals in zip(scores.tolist()	,_a	,_a ) if vals[0] > threshold]
        else:
              # This is a regular ForObjectDetectionModel
              A_						:  Optional[Any]          =				self.image_processor.post_process_object_detection(_a	,_a	,_a )
              A_						:  Any          =				raw_annotations[0]
              A_						:  Tuple          =				raw_annotation["""scores"""]
              A_						:  Dict          =				raw_annotation["""labels"""]
              A_						:  int          =				raw_annotation["""boxes"""]

              A_						:  Union[str, Any]          =				scores.tolist()
              A_						:  int          =				[self.model.config.idalabel[label.item()] for label in labels]
              A_						:  str          =				[self._get_bounding_box(_a ) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              A_						:  int          =				["""score""", """label""", """box"""]
              A_						:  Optional[int]          =				[
                  dict(zip(_a	,_a ) )
                  for vals in zip(raw_annotation["""scores"""]	,raw_annotation["""labels"""]	,raw_annotation["""boxes"""] )
              ]

        return annotation






  def        _a							(							self :       int	,_a :       "torch.Tensor" ):

        '''simple docstring'''




        if self.framework != "pt":
              raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
        A_   ,					A_   ,					A_   ,					A_						:  Tuple          =				box.int().tolist()
        A_						:  Optional[int]          =				{
            """xmin""": xmin,
            """ymin""": ymin,
            """xmax""": xmax,
            """ymax""": ymax,
        }
        return bbox




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      lowerCamelCase					(      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      lowerCamelCase					(      lowerCamelCase  :      str):
      # word like '180' or '身高' or '神'
      for char in word:
            A_						:  Optional[Any]          =				ord(lowerCamelCase)
            if not _is_chinese_char(lowerCamelCase):
                  return 0
      return 1




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				bert_tokenizer(lines[i : i + 100]							,						add_special_tokens=lowerCamelCase							,						truncation=lowerCamelCase							,						max_length=512)
            bert_res.extend(res["""input_ids"""])
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  Union[str, Any]          =				[]
      for input_ids, chinese_word in zip(lowerCamelCase							,						lowerCamelCase):
            A_						:  List[Any]          =				[]
            for id in input_ids:
                  A_						:  List[Any]          =				bert_tokenizer._convert_id_to_token(lowerCamelCase)
                  input_tokens.append(lowerCamelCase)
            A_						:  int          =				add_sub_symbol(lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				[]
            # 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] == "##":
                        A_						:  Optional[Any]          =				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      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      # 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_						:  Optional[int]          =				f.readlines()
      A_						:  Union[str, Any]          =				[line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      A_						:  Optional[Any]          =				LTP(args.ltp)  # faster in GPU device
      A_						:  Dict          =				BertTokenizer.from_pretrained(args.bert)

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

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


if __name__ == "__main__":
     __magic_name__			=      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',
     )

     __magic_name__			=      parser.parse_args()
     main(args)
