



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def A    (							__UpperCamelCase       )    ->   List[str]:
			# 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    (							__UpperCamelCase       )    ->   str:
			# word like '180' or '身高' or '神'
			for char in word:
						A__	  =     ord(__UpperCamelCase       )
						if not _is_chinese_char(__UpperCamelCase       ):
									return 0
			return 1


def A    (							__UpperCamelCase       )    ->   str:
			A__	  =     set()

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			if not chinese_word_set:
						return bert_tokens
			A__	  =     max([len(__UpperCamelCase       ) for w in chinese_word_set]       )

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


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     []

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

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

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

			assert len(__UpperCamelCase       ) == len(__UpperCamelCase       )

			return ref_ids


def A    (							__UpperCamelCase       )    ->   List[str]:
			# 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__	  =     f.readlines()
			A__	  =     [line.strip() for line in data if len(__UpperCamelCase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			A__	  =     LTP(args.ltp       )  # faster in GPU device
			A__	  =     BertTokenizer.from_pretrained(args.bert       )

			A__	  =     prepare_ref(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

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


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

							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							main(args)






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
__lowerCamelCase				=			False
__lowerCamelCase				=			False


def      UpperCamelCase   (       __lowerCamelCase	:					Namespace       ):
       return TrainCommand(__lowerCamelCase       )






class     UpperCAmelCase						(		A_					):



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   ArgumentParser					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= parser.add_parser("train"       ,		help="CLI tool to train a model on a task."					)

          train_parser.add_argument(
              "--train_data"       ,		type=snake_case__       ,		required=snake_case__       ,		help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."       ,		)
          train_parser.add_argument(
              "--column_label"       ,		type=snake_case__       ,		default=0       ,		help="Column of the dataset csv file with example labels."					)
          train_parser.add_argument(
              "--column_text"       ,		type=snake_case__       ,		default=1       ,		help="Column of the dataset csv file with example texts."					)
          train_parser.add_argument(
              "--column_id"       ,		type=snake_case__       ,		default=2       ,		help="Column of the dataset csv file with example ids."					)
          train_parser.add_argument(
              "--skip_first_row"       ,		action="store_true"       ,		help="Skip the first row of the csv file (headers)."					)

          train_parser.add_argument("--validation_data"       ,		type=snake_case__       ,		default=""       ,		help="path to validation dataset."					)
          train_parser.add_argument(
              "--validation_split"       ,		type=snake_case__       ,		default=0.1       ,		help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."       ,		)

          train_parser.add_argument("--output"       ,		type=snake_case__       ,		default="./"       ,		help="path to saved the trained model."					)

          train_parser.add_argument(
              "--task"       ,		type=snake_case__       ,		default="text_classification"       ,		help="Task to train the model on."					)
          train_parser.add_argument(
              "--model"       ,		type=snake_case__       ,		default="bert-base-uncased"       ,		help="Model's name or path to stored model."					)
          train_parser.add_argument("--train_batch_size"       ,		type=snake_case__       ,		default=32       ,		help="Batch size for training."					)
          train_parser.add_argument("--valid_batch_size"       ,		type=snake_case__       ,		default=64       ,		help="Batch size for validation."					)
          train_parser.add_argument("--learning_rate"       ,		type=snake_case__       ,		default=3e-5       ,		help="Learning rate."					)
          train_parser.add_argument("--adam_epsilon"       ,		type=snake_case__       ,		default=1e-08       ,		help="Epsilon for Adam optimizer."					)
          train_parser.set_defaults(func=snake_case__					)



   def __init__(self						:   List[Any]       ,		snake_case__						:   Namespace					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Any     							= logging.get_logger("transformers-cli/training"					)

          snake_case			:  List[Any]     							= "tf" if is_tf_available() else "torch"

          os.makedirs(args.output       ,		exist_ok=snake_case__					)
          snake_case			:  Any     							= args.output

          snake_case			:  List[Any]     							= args.column_label
          snake_case			:  Tuple     							= args.column_text
          snake_case			:  str     							= args.column_id

          self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
          if args.task == "text_classification":
                 snake_case			:  Optional[int]     							= TextClassificationPipeline.from_pretrained(args.model					)
          elif args.task == "token_classification":
                 raise NotImplementedError
          elif args.task == "question_answering":
                 raise NotImplementedError

          self.logger.info(f"""Loading dataset from {args.train_data}"""					)
          snake_case			:  Tuple     							= Processor.create_from_csv(
              args.train_data       ,		column_label=args.column_label       ,		column_text=args.column_text       ,		column_id=args.column_id       ,		skip_first_row=args.skip_first_row       ,		)
          snake_case			:  Optional[Any]     							= None
          if args.validation_data:
                 self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
                 snake_case			:  Dict     							= Processor.create_from_csv(
                     args.validation_data       ,		column_label=args.column_label       ,		column_text=args.column_text       ,		column_id=args.column_id       ,		skip_first_row=args.skip_first_row       ,		)

          snake_case			:  Union[str, Any]     							= args.validation_split
          snake_case			:  Optional[Any]     							= args.train_batch_size
          snake_case			:  List[str]     							= args.valid_batch_size
          snake_case			:  List[Any]     							= args.learning_rate
          snake_case			:  List[Any]     							= args.adam_epsilon



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


          if self.framework == "tf":
                 return self.run_tf()
          return self.run_torch()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          raise NotImplementedError



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          self.pipeline.fit(
              self.train_dataset       ,		validation_data=self.valid_dataset       ,		validation_split=self.validation_split       ,		learning_rate=self.learning_rate       ,		adam_epsilon=self.adam_epsilon       ,		train_batch_size=self.train_batch_size       ,		valid_batch_size=self.valid_batch_size       ,		)

          # Save trained pipeline
          self.pipeline.save_pretrained(self.output					)





