

'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_	= logging.get_logger(__name__)

A_	= {
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'sew-d'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=32   ,			SCREAMING_SNAKE_CASE_=768   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=3072   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=512   ,			SCREAMING_SNAKE_CASE_=256   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=("p2c", "c2p")   ,			SCREAMING_SNAKE_CASE_="layer_norm"   ,			SCREAMING_SNAKE_CASE_="gelu_python"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=1E-7   ,			SCREAMING_SNAKE_CASE_=1E-5   ,			SCREAMING_SNAKE_CASE_="group"   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,			SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,			SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=128   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=0.05   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=0   ,			SCREAMING_SNAKE_CASE_="mean"   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=256   ,			SCREAMING_SNAKE_CASE_=0   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=2   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Optional[int]:






							'''simple docstring'''



							super().__init__(**SCREAMING_SNAKE_CASE_   ,			pad_token_id=SCREAMING_SNAKE_CASE_   ,			bos_token_id=SCREAMING_SNAKE_CASE_   ,			eos_token_id=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      feat_extract_norm
							lowerCamelCase_  				=      feat_extract_activation
							lowerCamelCase_  				=      list(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      list(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      list(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      conv_bias
							lowerCamelCase_  				=      num_conv_pos_embeddings
							lowerCamelCase_  				=      num_conv_pos_embedding_groups
							lowerCamelCase_  				=      len(self.conv_dim		)
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      squeeze_factor
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      position_buckets
							lowerCamelCase_  				=      share_att_key
							lowerCamelCase_  				=      relative_attention
							lowerCamelCase_  				=      norm_rel_ebd
							lowerCamelCase_  				=      list(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      hidden_dropout
							lowerCamelCase_  				=      attention_dropout
							lowerCamelCase_  				=      activation_dropout
							lowerCamelCase_  				=      feat_proj_dropout
							lowerCamelCase_  				=      final_dropout
							lowerCamelCase_  				=      layer_norm_eps
							lowerCamelCase_  				=      feature_layer_norm_eps
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      vocab_size

							if (
							    (len(self.conv_stride		) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
							    or (len(self.conv_dim		) != self.num_feat_extract_layers)
							):
												raise ValueError(
												    'Configuration for convolutional layers is incorrect.'
												    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
												    f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
												    f'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase_  				=      apply_spec_augment
							lowerCamelCase_  				=      mask_time_prob
							lowerCamelCase_  				=      mask_time_length
							lowerCamelCase_  				=      mask_time_min_masks
							lowerCamelCase_  				=      mask_feature_prob
							lowerCamelCase_  				=      mask_feature_length
							lowerCamelCase_  				=      mask_feature_min_masks

							# ctc loss
							lowerCamelCase_  				=      ctc_loss_reduction
							lowerCamelCase_  				=      ctc_zero_infinity

							# sequence classification
							lowerCamelCase_  				=      use_weighted_layer_sum
							lowerCamelCase_  				=      classifier_proj_size






		@property
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							return functools.reduce(operator.mul   ,			self.conv_stride   ,			1		)







'''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)


