



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):


					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							(UpperCAmelCase__       :							str					):


					for char in word:
										SCREAMING_SNAKE_CASE           =    ord(__UpperCamelCase					)
										if not _is_chinese_char(__UpperCamelCase					):
															return 0
					return 1





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					SCREAMING_SNAKE_CASE           =    set()

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							set()					):


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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							LTP ,	UpperCAmelCase__       :							BertTokenizer					):
					SCREAMING_SNAKE_CASE           =    []

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

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

					SCREAMING_SNAKE_CASE           =    []
					for input_ids, chinese_word in zip(__UpperCamelCase ,	__UpperCamelCase					):
										SCREAMING_SNAKE_CASE           =    []
										for id in input_ids:
															SCREAMING_SNAKE_CASE           =    bert_tokenizer._convert_id_to_token(__UpperCamelCase					)
															input_tokens.append(__UpperCamelCase					)
										SCREAMING_SNAKE_CASE           =    add_sub_symbol(__UpperCamelCase ,	__UpperCamelCase					)
										SCREAMING_SNAKE_CASE           =    []
										# 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] == "##":
																				SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):


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

					SCREAMING_SNAKE_CASE           =    prepare_ref(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

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


if __name__ == "__main__":
							_lowerCamelCase   :				Optional[int]     			=						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   :				Any     			=						parser.parse_args()
							main(args)





import functools
import operator

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


_lowerCamelCase   :				int     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Union[str, Any]     			=						{
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							"""unispeech-sat"""




							def __init__(  self				:   str		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   Union[str, Any]=768		,							_UpperCamelCase				:   Tuple=12		,							_UpperCamelCase				:   List[str]=12		,							_UpperCamelCase				:   Tuple=3_072		,							_UpperCamelCase				:   List[str]="gelu"		,							_UpperCamelCase				:   Tuple=0.1		,							_UpperCamelCase				:   Any=0.1		,							_UpperCamelCase				:   Union[str, Any]=0.1		,							_UpperCamelCase				:   Dict=0.0		,							_UpperCamelCase				:   Tuple=0.0		,							_UpperCamelCase				:   Tuple=0.1		,							_UpperCamelCase				:   Optional[Any]=0.1		,							_UpperCamelCase				:   Tuple=0.0_2		,							_UpperCamelCase				:   Optional[int]=1e-5		,							_UpperCamelCase				:   Union[str, Any]="group"		,							_UpperCamelCase				:   Optional[int]="gelu"		,							_UpperCamelCase				:   Tuple=(512, 512, 512, 512, 512, 512, 512)		,							_UpperCamelCase				:   List[str]=(5, 2, 2, 2, 2, 2, 2)		,							_UpperCamelCase				:   Optional[int]=(10, 3, 3, 3, 3, 2, 2)		,							_UpperCamelCase				:   Optional[int]=False		,							_UpperCamelCase				:   Dict=128		,							_UpperCamelCase				:   Optional[int]=16		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   Optional[Any]=0.0_5		,							_UpperCamelCase				:   Union[str, Any]=10		,							_UpperCamelCase				:   Union[str, Any]=2		,							_UpperCamelCase				:   str=0.0		,							_UpperCamelCase				:   List[Any]=10		,							_UpperCamelCase				:   Optional[int]=0		,							_UpperCamelCase				:   Any=320		,							_UpperCamelCase				:   List[Any]=2		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   str=100		,							_UpperCamelCase				:   int=256		,							_UpperCamelCase				:   Optional[Any]=256		,							_UpperCamelCase				:   List[Any]=0.1		,							_UpperCamelCase				:   str="mean"		,							_UpperCamelCase				:   int=False		,							_UpperCamelCase				:   Optional[Any]=False		,							_UpperCamelCase				:   Any=256		,							_UpperCamelCase				:   str=(512, 512, 512, 512, 1_500)		,							_UpperCamelCase				:   List[Any]=(5, 3, 3, 1, 1)		,							_UpperCamelCase				:   Union[str, Any]=(1, 2, 3, 1, 1)		,							_UpperCamelCase				:   Any=512		,							_UpperCamelCase				:   str=0		,							_UpperCamelCase				:   int=1		,							_UpperCamelCase				:   Any=2		,							_UpperCamelCase				:   Optional[Any]=504		,							**_UpperCamelCase				:   str		,							)   ->			int:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase		,							pad_token_id=_UpperCamelCase		,							bos_token_id=_UpperCamelCase		,							eos_token_id=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    feat_extract_norm
												SCREAMING_SNAKE_CASE           =    feat_extract_activation
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    conv_bias
												SCREAMING_SNAKE_CASE           =    num_conv_pos_embeddings
												SCREAMING_SNAKE_CASE           =    num_conv_pos_embedding_groups
												SCREAMING_SNAKE_CASE           =    len(self.conv_dim )
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    intermediate_size
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    hidden_dropout
												SCREAMING_SNAKE_CASE           =    attention_dropout
												SCREAMING_SNAKE_CASE           =    activation_dropout
												SCREAMING_SNAKE_CASE           =    feat_proj_dropout
												SCREAMING_SNAKE_CASE           =    final_dropout
												SCREAMING_SNAKE_CASE           =    layerdrop
												SCREAMING_SNAKE_CASE           =    layer_norm_eps
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    num_clusters
												SCREAMING_SNAKE_CASE           =    do_stable_layer_norm
												SCREAMING_SNAKE_CASE           =    use_weighted_layer_sum

												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
												SCREAMING_SNAKE_CASE           =    apply_spec_augment
												SCREAMING_SNAKE_CASE           =    mask_time_prob
												SCREAMING_SNAKE_CASE           =    mask_time_length
												SCREAMING_SNAKE_CASE           =    mask_time_min_masks
												SCREAMING_SNAKE_CASE           =    mask_feature_prob
												SCREAMING_SNAKE_CASE           =    mask_feature_length
												SCREAMING_SNAKE_CASE           =    mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												SCREAMING_SNAKE_CASE           =    num_codevectors_per_group
												SCREAMING_SNAKE_CASE           =    num_codevector_groups
												SCREAMING_SNAKE_CASE           =    contrastive_logits_temperature
												SCREAMING_SNAKE_CASE           =    feat_quantizer_dropout
												SCREAMING_SNAKE_CASE           =    num_negatives
												SCREAMING_SNAKE_CASE           =    codevector_dim
												SCREAMING_SNAKE_CASE           =    proj_codevector_dim
												SCREAMING_SNAKE_CASE           =    diversity_loss_weight

												# ctc loss
												SCREAMING_SNAKE_CASE           =    ctc_loss_reduction
												SCREAMING_SNAKE_CASE           =    ctc_zero_infinity

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												SCREAMING_SNAKE_CASE           =    classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    xvector_output_dim





							@property
							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

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




