




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							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 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							for char in word:
														_a     					=    ord(UpperCamelCase				)
														if not _is_chinese_char(UpperCamelCase				):
																					return 0
							return 1
def 		snake_case_   (UpperCamelCase	:  List[str]				):





							'''simple docstring'''


							_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 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  set()				):





							'''simple docstring'''


							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 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  LTP			,						UpperCamelCase	:  BertTokenizer				):





							'''simple docstring'''


							_a     					=    []

							for i in range(0			,						len(UpperCamelCase				)			,						100				):
														_a     					=    ltp_tokenizer.seg(lines[i : i + 100]				)[0]
														_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 		snake_case_   (UpperCamelCase	:  Any				):





							'''simple docstring'''


							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__":
				_snake_case				:				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')

				_snake_case				:				str       							=		parser.parse_args()
				main(args)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

