


'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy




@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''						,			SCREAMING_SNAKE_CASE						,			)
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				RobertaConfig
				_snake_case						       =				'''roberta'''

				def __init__(     self							,      a_							)    ->  Optional[int]:
					super().__init__(a_							)

					lowercase						:		Tuple   					=		RobertaEmbeddings(a_							)
					self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''						,			SCREAMING_SNAKE_CASE						,			)
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				RobertaConfig
				_snake_case						       =				'''roberta'''

				def __init__(     self							,      a_							)    ->  Dict:
					super().__init__(a_							)
					lowercase						:		Optional[Any]   					=		config.num_labels
					lowercase						:		Optional[Any]   					=		config.num_hidden_layers

					lowercase						:		str   					=		DeeRobertaModel(a_							)
					lowercase						:		List[str]   					=		nn.Dropout(config.hidden_dropout_prob							)
					lowercase						:		Union[str, Any]   					=		nn.Linear(config.hidden_size							,      self.config.num_labels							)


				@add_start_docstrings_to_model_forward(a_							)
				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=-1							,      a_=False							,      )    ->  List[str]:
					lowercase						:		Optional[int]   					=		self.num_layers
					try:
						lowercase						:		Tuple   					=		self.roberta(
						    a_							,      attention_mask=a_							,      token_type_ids=a_							,      position_ids=a_							,      head_mask=a_							,      inputs_embeds=a_							,      )

						lowercase						:		Tuple   					=		outputs[1]

						lowercase						:		str   					=		self.dropout(a_							)
						lowercase						:		List[str]   					=		self.classifier(a_							)
						lowercase						:		Union[str, Any]   					=		(logits,) + outputs[2:]  # add hidden states and attention if they are here
					except HighwayException as e:
						lowercase						:		List[str]   					=		e.message
						lowercase						:		Union[str, Any]   					=		e.exit_layer
						lowercase						:		List[str]   					=		outputs[0]

					if not self.training:
						lowercase						:		Optional[int]   					=		entropy(a_							)
						lowercase						:		List[Any]   					=		[]
						lowercase						:		Union[str, Any]   					=		[]
					if labels is not None:
						if self.num_labels == 1:
							#  We are doing regression
							lowercase						:		List[str]   					=		MSELoss()
							lowercase						:		Any   					=		loss_fct(logits.view(-1							)							,      labels.view(-1							)							)
						else:
							lowercase						:		Any   					=		CrossEntropyLoss()
							lowercase						:		Optional[Any]   					=		loss_fct(logits.view(-1							,      self.num_labels							)							,      labels.view(-1							)							)

						# work with highway exits
						lowercase						:		Dict   					=		[]
						for highway_exit in outputs[-1]:
							lowercase						:		Dict   					=		highway_exit[0]
							if not self.training:
								highway_logits_all.append(a_							)
								highway_entropy.append(highway_exit[2]							)
							if self.num_labels == 1:
								#  We are doing regression
								lowercase						:		Tuple   					=		MSELoss()
								lowercase						:		Tuple   					=		loss_fct(highway_logits.view(-1							)							,      labels.view(-1							)							)
							else:
								lowercase						:		Dict   					=		CrossEntropyLoss()
								lowercase						:		Dict   					=		loss_fct(highway_logits.view(-1							,      self.num_labels							)							,      labels.view(-1							)							)
							highway_losses.append(a_							)

						if train_highway:
							lowercase						:		Optional[Any]   					=		(sum(highway_losses[:-1]							),) + outputs
							# exclude the final highway, of course
						else:
							lowercase						:		str   					=		(loss,) + outputs
					if not self.training:
						lowercase						:		Optional[int]   					=		outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
							lowercase						:		Optional[int]   					=		(
							    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
							)  # use the highway of the last layer

					return outputs  # (loss), logits, (hidden_states), (attentions), entropy






'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




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




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Any   					=		set()

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




def 	_A  (       A							,A							)  ->				Tuple:
	if not chinese_word_set:
		return bert_tokens
	lowercase						:		Optional[Any]   					=		max([len(A							) for w in chinese_word_set]							)

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]

	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		ltp_tokenizer.seg(lines[i : i + 1_0_0]							)[0]
		lowercase						:		Dict   					=		[get_chinese_word(A							) for r in res]
		ltp_res.extend(A							)
	assert len(A							) == len(A							)

	lowercase						:		List[str]   					=		[]
	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		bert_tokenizer(lines[i : i + 1_0_0]							,add_special_tokens=A							,truncation=A							,max_length=5_1_2							)
		bert_res.extend(res["input_ids"]							)
	assert len(A							) == len(A							)

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

	assert len(A							) == len(A							)

	return ref_ids




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

	lowercase						:		List[str]   					=		prepare_ref(A							,A							,A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : List[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""")

					lowerCAmelCase   : Union[str, Any]             =				parser.parse_args()
					main(args)


