import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def      lowerCAmelCase_    ( snake_case_	:			Optional[int]      )   ->				str:


     '''simple docstring'''




     if (
         (cp >= 0x4e00 and cp <= 0x9fff)
         or (cp >= 0x3400 and cp <= 0x4dbf)  #
         or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
         or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
         or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
         or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
         or (cp >= 0xf900 and cp <= 0xfaff)
         or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
     ):  #
          return True

     return False





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				Any:


     '''simple docstring'''




     for char in word:
          UpperCAmelCase_	   =       ord(snake_case_      )
          if not _is_chinese_char(snake_case_      ):
               return 0
     return 1





def      lowerCAmelCase_    ( snake_case_	:			List[str]      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       set()

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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			set()      )   ->				Tuple:


     '''simple docstring'''




     if not chinese_word_set:
          return bert_tokens
     UpperCAmelCase_	   =       max([len(snake_case_      ) for w in chinese_word_set]      )

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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			LTP ,   snake_case_	:			BertTokenizer      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       []

     for i in range(0 ,   len(snake_case_      ) ,   1_00      ):
          UpperCAmelCase_	   =       ltp_tokenizer.seg(lines[i : i + 1_00]      )[0]
          UpperCAmelCase_	   =       [get_chinese_word(snake_case_      ) for r in res]
          ltp_res.extend(snake_case_      )
     assert len(snake_case_      ) == len(snake_case_      )

     UpperCAmelCase_	   =       []
     for i in range(0 ,   len(snake_case_      ) ,   1_00      ):
          UpperCAmelCase_	   =       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_      )

     UpperCAmelCase_	   =       []
     for input_ids, chinese_word in zip(snake_case_ ,   snake_case_      ):
          UpperCAmelCase_	   =       []
          for id in input_ids:
               UpperCAmelCase_	   =       bert_tokenizer._convert_id_to_token(snake_case_      )
               input_tokens.append(snake_case_      )
          UpperCAmelCase_	   =       add_sub_symbol(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       []
          # 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] == "##":
                    UpperCAmelCase_	   =       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      lowerCAmelCase_    ( snake_case_	:			Optional[int]      )   ->				List[str]:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       prepare_ref(snake_case_ ,   snake_case_ ,   snake_case_      )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =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')

    SCREAMING_SNAKE_CASE_:					Any  =parser.parse_args()
    main(args)





'''simple docstring'''





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   Dict       ,      __a   :   Any			):
         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [label.strip() for label in labels.split(","			) if label.strip()]
         return labels






    def __call__(self   :   Union[str, Any]       ,      __a   :   Optional[int]       ,      __a   :   Optional[int]       ,      __a   :   int			):
         if len(__a			) == 0 or len(__a			) == 0:
              raise ValueError("You must include at least one label and at least one sequence."			)
         if hypothesis_template.format(labels[0]			) == hypothesis_template:
              raise ValueError(
                  (
                      "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
                      "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
                  ).format(__a			)			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [sequences]

         UpperCAmelCase_	   =       []
         for sequence in sequences:
              sequence_pairs.extend([[sequence, hypothesis_template.format(__a			)] for label in labels]			)

         return sequence_pairs, sequences




@add_end_docstrings(UpperCamelCase__     )
class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Optional[Any]       ,      __a   :   Union[str, Any]=ZeroShotClassificationArgumentHandler()       ,      *__a   :   Optional[int]       ,      **__a   :   List[str]			):
         UpperCAmelCase_	   =       args_parser
         super().__init__(*__a       ,      **__a			)
         if self.entailment_id == -1:
              logger.warning(
                  "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
                  "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."			)



    @property
    def  _lowercase						(self   :   str			):
         for label, ind in self.model.config.labelaid.items():
              if label.lower().startswith("entail"			):
                   return ind
         return -1



    def  _lowercase						(self   :   Any       ,      __a   :   Any       ,      __a   :   int=True       ,      __a   :   Dict=True       ,      __a   :   Any=TruncationStrategy.ONLY_FIRST       ,      **__a   :   Tuple			):
         UpperCAmelCase_	   =       self.framework
         if self.tokenizer.pad_token is None:
              # Override for tokenizers not supporting padding
              logger.error(
                  "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                  " `pad_token=eos_token`"			)
              UpperCAmelCase_	   =       self.tokenizer.eos_token
         try:
              UpperCAmelCase_	   =       self.tokenizer(
                  __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=__a       ,      )
         except Exception as e:
              if "too short" in str(__a			):
                   # tokenizers might yell that we want to truncate
                   # to a value that is not even reached by the input.
                   # In that case we don't want to truncate.
                   # It seems there's not a really better way to catch that
                   # exception.

                   UpperCAmelCase_	   =       self.tokenizer(
                       __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,      )
              else:
                   raise e

         return inputs



    def  _lowercase						(self   :   List[str]       ,      **__a   :   Tuple			):
         if kwargs.get("multi_class"       ,      __a			) is not None:
              UpperCAmelCase_	   =       kwargs["multi_class"]
              logger.warning(
                  "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
                  "`multi_class` will be removed in a future version of Transformers."			)
         UpperCAmelCase_	   =       {}
         if "candidate_labels" in kwargs:
              UpperCAmelCase_	   =       self._args_parser._parse_labels(kwargs["candidate_labels"]			)
         if "hypothesis_template" in kwargs:
              UpperCAmelCase_	   =       kwargs["hypothesis_template"]

         UpperCAmelCase_	   =       {}
         if "multi_label" in kwargs:
              UpperCAmelCase_	   =       kwargs["multi_label"]
         return preprocess_params, {}, postprocess_params



    def __call__(self   :   Tuple       ,      __a   :   Union[str, List[str]]       ,      *__a   :   Optional[Any]       ,      **__a   :   Tuple       ,      ):
         if len(__a			) == 0:
              pass
         elif len(__a			) == 1 and "candidate_labels" not in kwargs:
              UpperCAmelCase_	   =       args[0]
         else:
              raise ValueError(f"""Unable to understand extra arguments {args}"""			)

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



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   Optional[Any]       ,      __a   :   List[str]=None       ,      __a   :   Any="This example is {}."			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self._args_parser(__a       ,      __a       ,      __a			)

         for i, (candidate_label, sequence_pair) in enumerate(zip(__a       ,      __a			)			):
              UpperCAmelCase_	   =       self._parse_and_tokenize([sequence_pair]			)

              yield {
                  "candidate_label": candidate_label,
                  "sequence": sequences[0],
                  "is_last": i == len(__a			) - 1,
                  **model_input,
              }



    def  _lowercase						(self   :   List[str]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       inputs["candidate_label"]
         UpperCAmelCase_	   =       inputs["sequence"]
         UpperCAmelCase_	   =       {k: inputs[k] for k in self.tokenizer.model_input_names}
         UpperCAmelCase_	   =       self.model(**__a			)

         UpperCAmelCase_	   =       {
             "candidate_label": candidate_label,
             "sequence": sequence,
             "is_last": inputs["is_last"],
             **outputs,
         }
         return model_outputs






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   List[str]       ,      __a   :   Tuple=False			):
         UpperCAmelCase_	   =       [outputs["candidate_label"] for outputs in model_outputs]
         UpperCAmelCase_	   =       [outputs["sequence"] for outputs in model_outputs]
         UpperCAmelCase_	   =       np.concatenate([output["logits"].numpy() for output in model_outputs]			)
         UpperCAmelCase_	   =       logits.shape[0]
         UpperCAmelCase_	   =       len(__a			)
         UpperCAmelCase_	   =       N // n
         UpperCAmelCase_	   =       logits.reshape((num_sequences, n, -1)			)

         if multi_label or len(__a			) == 1:
              # softmax over the entailment vs. contradiction dim for each label independently
              UpperCAmelCase_	   =       self.entailment_id
              UpperCAmelCase_	   =       -1 if entailment_id == 0 else 0
              UpperCAmelCase_	   =       reshaped_outputs[..., [contradiction_id, entailment_id]]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)
              UpperCAmelCase_	   =       scores[..., 1]
         else:
              # softmax the "entailment" logits over all candidate labels
              UpperCAmelCase_	   =       reshaped_outputs[..., self.entailment_id]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)

         UpperCAmelCase_	   =       list(reversed(scores[0].argsort()			)			)
         return {
             "sequence": sequences[0],
             "labels": [candidate_labels[i] for i in top_inds],
             "scores": scores[0, top_inds].tolist(),
         }




