import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				for char in word:
								__UpperCamelCase  :Union[str, Any]    						=	ord(_A			)
								if not _is_chinese_char(_A			):
												return 0
				return 1



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Any    						=	set()

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				if not chinese_word_set:
								return bert_tokens
				__UpperCamelCase  :List[str]    						=	max([len(_A			) for w in chinese_word_set]			)

				__UpperCamelCase  :Tuple    						=	bert_tokens
				__UpperCamelCase  :Tuple    						=	0, len(_A			)
				while start < end:
								__UpperCamelCase  :Optional[Any]    						=	True
								if is_chinese(bert_word[start]			):
												__UpperCamelCase  :Any    						=	min(end - start						,    _A			)
												for i in range(_A						,    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  :Dict    						=	"##" + bert_word[j]
																				__UpperCamelCase  :str    						=	start + i
																				__UpperCamelCase  :List[str]    						=	False
																				break
								if single_word:
												start += 1
				return bert_word



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Optional[Any]    						=	[]

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

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

				__UpperCamelCase  :List[str]    						=	[]
				for input_ids, chinese_word in zip(_A						,    _A			):
								__UpperCamelCase  :Union[str, Any]    						=	[]
								for id in input_ids:
												__UpperCamelCase  :List[Any]    						=	bert_tokenizer._convert_id_to_token(_A			)
												input_tokens.append(_A			)
								__UpperCamelCase  :int    						=	add_sub_symbol(_A						,    _A			)
								__UpperCamelCase  :List[str]    						=	[]
								# 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] == "##":
																__UpperCamelCase  :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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







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

				__UpperCamelCase  :Dict    						=	prepare_ref(_A						,    _A						,    _A			)

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


if __name__ == "__main__":
				__lowercase											=      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''')

				__lowercase											=      parser.parse_args()
				main(args)






import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_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)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





