





"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		__lowercase     (   snake_case_  :  int						)   ->Tuple:



    '''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 		__lowercase     (   snake_case_  :  str						)   ->Dict:



    '''simple docstring'''







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







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



    '''simple docstring'''







    __A    :						str						 =     set()

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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  set()						)   ->Any:



    '''simple docstring'''







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

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







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



    '''simple docstring'''







    __A    :						Optional[Any]						 =     []

    for i in range(0				,len(snake_case_						)				,100						):
        __A    :						int						 =     ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        __A    :						List[Any]						 =     [get_chinese_word(snake_case_						) for r in res]
        ltp_res.extend(snake_case_						)
    assert len(snake_case_						) == len(snake_case_						)

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

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



    '''simple docstring'''







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

    __A    :						Optional[Any]						 =     prepare_ref(snake_case_				,snake_case_				,snake_case_						)

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


if __name__ == "__main__":
       a_ =			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""")

       a_ =			parser.parse_args()
       main(args)











"""simple docstring"""



import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch


a_ =			logging.get_logger(__name__)






class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     if not conversation_id:
         __A    :						List[Any]						 =     uuid.uuida()
     if past_user_inputs is None:
         __A    :						List[str]						 =     []
     if generated_responses is None:
         __A    :						Tuple						 =     []

     __A    :						uuid.UUID						 =     conversation_id
     __A    :						List[str]						 =     past_user_inputs
     __A    :						List[str]						 =     generated_responses
     __A    :						Optional[str]						 =     text

 def __eq__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     if not isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         return False
     if self.uuid == other.uuid:
         return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = False  ):



     '''simple docstring'''




     if self.new_user_input:
         if overwrite:
             logger.warning(
                 F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                 F"""with: \"{text}\"."""  )
             __A    :						str						 =     text
         else:
             logger.warning(
                 F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                 F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""  )
     else:
         __A    :						Union[str, Any]						 =     text

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input  )
     __A    :						List[Any]						 =     None

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     self.generated_responses.append(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for user_input, generated_response in zip(self.past_user_inputs					,						self.generated_responses  ):
         yield True, user_input
         yield False, generated_response
     if self.new_user_input:
         yield True, self.new_user_input







 def __repr__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     F"""Conversation id: {self.uuid} \n"""
     for is_user, text in self.iter_texts():
         __A    :						Tuple						 =     '''user''' if is_user else '''bot'''
         output += F"""{name} >> {text} \n"""
     return output






@add_end_docstrings(
    SCREAMING_SNAKE_CASE__							,							R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """							,							)
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						*__lowerCamelCase					,						**__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(*__lowerCamelCase					,						**__lowerCamelCase  )
     if self.tokenizer.pad_token_id is None:
         __A    :						Union[str, Any]						 =     self.tokenizer.eos_token

 def 					UpperCamelCase__(    self					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     {}
     __A    :						List[str]						 =     {}
     __A    :						Any						 =     {}

     if min_length_for_response is not None:
         __A    :						int						 =     min_length_for_response
     if minimum_tokens is not None:
         __A    :						Any						 =     minimum_tokens

     if "max_length" in generate_kwargs:
         __A    :						List[Any]						 =     generate_kwargs['''max_length''']
         # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
         __A    :						str						 =     clean_up_tokenization_spaces

     if generate_kwargs:
         forward_params.update(__lowerCamelCase  )
     return preprocess_params, forward_params, postprocess_params

 def __call__(    self					,						__lowerCamelCase					,						__lowerCamelCase=0					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     super().__call__(__lowerCamelCase					,						num_workers=__lowerCamelCase					,						**__lowerCamelCase  )
     if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) and len(__lowerCamelCase  ) == 1:
         return outputs[0]
     return outputs

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=32  ):



     '''simple docstring'''




     if not isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''  )
     if conversation.new_user_input is None:
         raise ValueError(
             F"""Conversation with UUID {type(conversation.uuid  )} does not contain new user input to process. """
             '''Add user inputs with the conversation\'s `add_user_input` method'''  )
     if hasattr(self.tokenizer					,						'''_build_conversation_input_ids'''  ):
         __A    :						List[Any]						 =     self.tokenizer._build_conversation_input_ids(__lowerCamelCase  )
     else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         __A    :						int						 =     self._legacy_parse_and_tokenize(__lowerCamelCase  )

     if self.framework == "pt":
         __A    :						Union[str, Any]						 =     torch.LongTensor([input_ids]  )
     elif self.framework == "tf":
         __A    :						int						 =     tf.constant([input_ids]  )
     return {"input_ids": input_ids, "conversation": conversation}

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=10					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     generate_kwargs.get('''max_length'''					,						self.model.config.max_length  )

     __A    :						str						 =     model_inputs['''input_ids'''].shape[1]
     if max_length - minimum_tokens < n:
         logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""  )
         __A    :						str						 =     max_length - minimum_tokens
         __A    :						Any						 =     model_inputs['''input_ids'''][:, -trim:]
         if "attention_mask" in model_inputs:
             __A    :						Union[str, Any]						 =     model_inputs['''attention_mask'''][:, -trim:]
     __A    :						Dict						 =     model_inputs.pop('''conversation'''  )
     __A    :						List[str]						 =     max_length
     __A    :						Dict						 =     self.model.generate(**__lowerCamelCase					,						**__lowerCamelCase  )
     if self.model.config.is_encoder_decoder:
         __A    :						Any						 =     1
     else:
         __A    :						List[Any]						 =     n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=True  ):



     '''simple docstring'''




     __A    :						int						 =     model_outputs['''output_ids''']
     __A    :						Optional[int]						 =     self.tokenizer.decode(
         output_ids[0]					,						skip_special_tokens=__lowerCamelCase					,						clean_up_tokenization_spaces=__lowerCamelCase					,						)
     __A    :						Dict						 =     model_outputs['''conversation''']
     conversation.mark_processed()
     conversation.append_response(__lowerCamelCase  )
     return conversation







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.tokenizer.eos_token_id
     __A    :						List[str]						 =     []
     for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
             input_ids.extend(self.tokenizer.encode(__lowerCamelCase					,						add_special_tokens=__lowerCamelCase  ) + [eos_token_id]  )
         else:
             input_ids.extend(self.tokenizer.encode(__lowerCamelCase					,						add_special_tokens=__lowerCamelCase  )  )

     if len(__lowerCamelCase  ) > self.tokenizer.model_max_length:
         __A    :						List[str]						 =     input_ids[-self.tokenizer.model_max_length :]
     return input_ids




