

'''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


__snake_case	: Optional[int]       				=							logging.get_logger(__name__)
class        A  :





						def __init__(	self	,    snake_case_ = None	,    snake_case_ = None	,    snake_case_=None	,    snake_case_=None			)			->							str:



										if not conversation_id:
														_a        =   uuid.uuida()
										if past_user_inputs is None:
														_a        =   []
										if generated_responses is None:
														_a        =   []

										_a        =   conversation_id
										_a        =   past_user_inputs
										_a        =   generated_responses
										_a        =   text





						def __eq__(	self	,    snake_case_			)			->							Optional[Any]:



										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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_ = False			)			->							str:



										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        =   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        =   text





						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:



										if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input			)
										_a        =   None





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							Any:
										self.generated_responses.append(_lowerCamelCase			)





						def __lowerCAmelCase   (	self			)			->							Any:



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



@add_end_docstrings(
    A__  ,    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        A  (	A__ ):





						def __init__(	self	,    *snake_case_	,    **snake_case_			)			->							str:
										super().__init__(*_lowerCamelCase	,    **_lowerCamelCase			)
										if self.tokenizer.pad_token_id is None:
														_a        =   self.tokenizer.eos_token





						def __lowerCAmelCase   (	self	,    snake_case_=None	,    snake_case_=None	,    snake_case_=None	,    **snake_case_			)			->							Optional[Any]:
										_a        =   {}
										_a        =   {}
										_a        =   {}

										if min_length_for_response is not None:
														_a        =   min_length_for_response
										if minimum_tokens is not None:
														_a        =   minimum_tokens

										if "max_length" in generate_kwargs:
														_a        =   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        =   clean_up_tokenization_spaces

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





						def __call__(	self	,    snake_case_	,    snake_case_=0	,    **snake_case_			)			->							Any:
										_a        =   super().__call__(_lowerCamelCase	,    num_workers=_lowerCamelCase	,    **_lowerCamelCase			)
										if isinstance(_lowerCamelCase	,    _lowerCamelCase			) and len(_lowerCamelCase			) == 1:
														return outputs[0]
										return outputs





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=3_2			)			->							str:



										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        =   self.tokenizer._build_conversation_input_ids(_lowerCamelCase			)
										else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														_a        =   self._legacy_parse_and_tokenize(_lowerCamelCase			)

										if self.framework == "pt":
														_a        =   torch.LongTensor([input_ids]			)
										elif self.framework == "tf":
														_a        =   tf.constant([input_ids]			)
										return {"input_ids": input_ids, "conversation": conversation}





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=1_0	,    **snake_case_			)			->							int:
										_a        =   generate_kwargs.get("max_length"	,    self.model.config.max_length			)

										_a        =   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        =   max_length - minimum_tokens
														_a        =   model_inputs['input_ids'][:, -trim:]
														if "attention_mask" in model_inputs:
																		_a        =   model_inputs['attention_mask'][:, -trim:]
										_a        =   model_inputs.pop("conversation"			)
										_a        =   max_length
										_a        =   self.model.generate(**_lowerCamelCase	,    **_lowerCamelCase			)
										if self.model.config.is_encoder_decoder:
														_a        =   1
										else:
														_a        =   n
										return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=True			)			->							Dict:
										_a        =   model_outputs['output_ids']
										_a        =   self.tokenizer.decode(
										    output_ids[0]	,    skip_special_tokens=_lowerCamelCase	,    clean_up_tokenization_spaces=_lowerCamelCase	,    )
										_a        =   model_outputs['conversation']
										conversation.mark_processed()
										conversation.append_response(_lowerCamelCase			)
										return conversation





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							int:
										_a        =   self.tokenizer.eos_token_id
										_a        =   []
										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        =   input_ids[-self.tokenizer.model_max_length :]
										return input_ids







"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




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




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=set()

 for token in tokens:
  UpperCamelCase_     :					Optional[int]   	=len(__lowercase	) > 1 and is_chinese(__lowercase	)
  if chinese_word:
   word_set.add(__lowercase	)
 UpperCamelCase_     :					Tuple   	=list(__lowercase	)
 return word_list




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

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]

 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					Union[str, Any]   	=ltp_tokenizer.seg(lines[i : i + 1_00]	)[0]
  UpperCamelCase_     :					int   	=[get_chinese_word(__lowercase	) for r in res]
  ltp_res.extend(__lowercase	)
 assert len(__lowercase	) == len(__lowercase	)

 UpperCamelCase_     :					Dict   	=[]
 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					int   	=bert_tokenizer(lines[i : i + 1_00]						,	add_special_tokens=__lowercase						,	truncation=__lowercase						,	max_length=5_12	)
  bert_res.extend(res['input_ids']	)
 assert len(__lowercase	) == len(__lowercase	)

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

 assert len(__lowercase	) == len(__lowercase	)

 return ref_ids




def       A_  (				__lowercase	):
 # 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:
  UpperCamelCase_     :					Tuple   	=f.readlines()
 UpperCamelCase_     :					Optional[int]   	=[line.strip() for line in data if len(__lowercase	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 UpperCamelCase_     :					Optional[Any]   	=LTP(args.ltp	)  # faster in GPU device
 UpperCamelCase_     :					Dict   	=BertTokenizer.from_pretrained(args.bert	)

 UpperCamelCase_     :					int   	=prepare_ref(__lowercase						,	__lowercase						,	__lowercase	)

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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							=     parser.parse_args()
 main(args)
