





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

_snake_case  :   str						   =  logging.get_logger(__name__)


_snake_case  :   int						   =  Dict[str, Any]
_snake_case  :   Optional[int]						   =  List[Prediction]



@add_end_docstrings(_UpperCamelCase   )
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def __init__(    self			:		str ,  *lowerCAmelCase_			:		List[str] ,  **lowerCAmelCase_			:		Any					)							->						Optional[Any]:
     super().__init__(*_snake_case ,  **_snake_case					)

     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     lowercase		(    self			:		int ,  **lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     __lowerCAmelCase							     =     {}
     if "threshold" in kwargs:
        __lowerCAmelCase							     =     kwargs['threshold']
     return {}, {}, postprocess_kwargs






  def __call__(    self			:		Optional[int] ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		str					)							->						str:
     return super().__call__(*_snake_case ,  **_snake_case					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Dict					)							->						Dict:
     __lowerCAmelCase							     =     load_image(_snake_case					)
     __lowerCAmelCase							     =     torch.IntTensor([[image.height, image.width]]					)
     __lowerCAmelCase							     =     self.image_processor(images=[image] ,  return_tensors='pt'					)
     if self.tokenizer is not None:
        __lowerCAmelCase							     =     self.tokenizer(text=inputs['words'] ,  boxes=inputs['boxes'] ,  return_tensors='pt'					)
     __lowerCAmelCase							     =     target_size
     return inputs






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     __lowerCAmelCase							     =     model_inputs.pop('target_size'					)
     __lowerCAmelCase							     =     self.model(**_snake_case					)
     __lowerCAmelCase							     =     outputs.__class__({'target_size': target_size, **outputs}					)
     if self.tokenizer is not None:
        __lowerCAmelCase							     =     model_inputs['bbox']
     return model_outputs






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		List[str]=0.9					)							->						Tuple:
     __lowerCAmelCase							     =     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.
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     target_size[0].tolist()

        def unnormalize(lowerCAmelCase_			:		Optional[int]					):
           return self._get_bounding_box(
               torch.Tensor(
                   [
                       (width * bbox[0] / 1_0_0_0),
                       (height * bbox[1] / 1_0_0_0),
                       (width * bbox[2] / 1_0_0_0),
                       (height * bbox[3] / 1_0_0_0),
                   ]					)					)

        __lowerCAmelCase     ,     __lowerCAmelCase							     =     model_outputs['logits'].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
        __lowerCAmelCase							     =     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
        __lowerCAmelCase							     =     [unnormalize(_snake_case					) for bbox in model_outputs['bbox'].squeeze(0					)]
        __lowerCAmelCase							     =     ['score', 'label', 'box']
        __lowerCAmelCase							     =     [dict(zip(_snake_case ,  _snake_case					)					) for vals in zip(scores.tolist() ,  _snake_case ,  _snake_case					) if vals[0] > threshold]
     else:
        # This is a regular ForObjectDetectionModel
        __lowerCAmelCase							     =     self.image_processor.post_process_object_detection(_snake_case ,  _snake_case ,  _snake_case					)
        __lowerCAmelCase							     =     raw_annotations[0]
        __lowerCAmelCase							     =     raw_annotation['scores']
        __lowerCAmelCase							     =     raw_annotation['labels']
        __lowerCAmelCase							     =     raw_annotation['boxes']

        __lowerCAmelCase							     =     scores.tolist()
        __lowerCAmelCase							     =     [self.model.config.idalabel[label.item()] for label in labels]
        __lowerCAmelCase							     =     [self._get_bounding_box(_snake_case					) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        __lowerCAmelCase							     =     ['score', 'label', 'box']
        __lowerCAmelCase							     =     [
            dict(zip(_snake_case ,  _snake_case					)					)
            for vals in zip(raw_annotation['scores'] ,  raw_annotation['labels'] ,  raw_annotation['boxes']					)
        ]

     return annotation






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		"torch.Tensor"					)							->						int:


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








import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

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

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     set()

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

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

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

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      LTP,     lowerCAmelCase_				:      BertTokenizer     ):
   __lowerCAmelCase							     =     []

   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     ltp_tokenizer.pipeline(lines[i : i + 100],     tasks=['cws']     ).cws
      __lowerCAmelCase							     =     [get_chinese_word(lowerCAmelCase_     ) for r in res]
      ltp_res.extend(lowerCAmelCase_     )
   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

   __lowerCAmelCase							     =     []
   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     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_     )

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

   __lowerCAmelCase							     =     prepare_ref(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   int						   =  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',
     )

     _snake_case  :   List[str]						   =  parser.parse_args()
     main(args)

