
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       int:

				"""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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       Union[str, Any]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Tuple         =							set()

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						set()       ) ->       Union[str, Any]:

				"""simple docstring"""







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

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						LTP							,						__UpperCamelCase :						BertTokenizer       ) ->       Tuple:

				"""simple docstring"""







				A__  :	Optional[Any]         =							[]

				for i in range(0							,						len(__UpperCamelCase       )							,						1_00       ):
								A__  :	str         =							ltp_tokenizer.pipeline(lines[i : i + 1_00]							,						tasks=['''cws''']       ).cws
								A__  :	List[Any]         =							[get_chinese_word(__UpperCamelCase       ) for r in res]
								ltp_res.extend(__UpperCamelCase       )
				assert len(__UpperCamelCase       ) == len(__UpperCamelCase       )

				A__  :	str         =							[]
				for i in range(0							,						len(__UpperCamelCase       )							,						1_00       ):
								A__  :	Dict         =							bert_tokenizer(lines[i : i + 1_00]							,						add_special_tokens=__UpperCamelCase							,						truncation=__UpperCamelCase							,						max_length=5_12       )
								bert_res.extend(res['''input_ids''']       )
				assert len(__UpperCamelCase       ) == len(__UpperCamelCase       )

				A__  :	Tuple         =							[]
				for input_ids, chinese_word in zip(__UpperCamelCase							,						__UpperCamelCase       ):
								A__  :	Optional[int]         =							[]
								for id in input_ids:
												A__  :	List[str]         =							bert_tokenizer._convert_id_to_token(__UpperCamelCase       )
												input_tokens.append(__UpperCamelCase       )
								A__  :	List[Any]         =							add_sub_symbol(__UpperCamelCase							,						__UpperCamelCase       )
								A__  :	Optional[int]         =							[]
								# 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__  :	List[str]         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       str:

				"""simple docstring"""







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

				A__  :	Optional[Any]         =							prepare_ref(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Tuple          =			argparse.ArgumentParser(description='prepare_chinese_ref')
					parser.add_argument(
					    '--file_name',
					    required=False,
					    type=str,
					    default='./resources/chinese-demo.txt',
					    help='file need process, same as training data in lm',
					)
					parser.add_argument(
					    '--ltp',
					    required=False,
					    type=str,
					    default='./resources/ltp',
					    help='resources for LTP tokenizer, usually a path',
					)
					parser.add_argument(
					    '--bert',
					    required=False,
					    type=str,
					    default='./resources/robert',
					    help='resources for Bert tokenizer',
					)
					parser.add_argument(
					    '--save_path',
					    required=False,
					    type=str,
					    default='./resources/ref.txt',
					    help='path to save res',
					)

					_SCREAMING_SNAKE_CASE    :      int          =			parser.parse_args()
					main(args)

from dataclasses import asdict, dataclass
from typing import Optional

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


_SCREAMING_SNAKE_CASE    :      Dict          =			logging.get_logger(__name__)

# TODO Update this
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			{
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"esm"





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=768				,       UpperCamelCase__=12				,       UpperCamelCase__=12				,       UpperCamelCase__=3072				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=1026				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-12				,       UpperCamelCase__="absolute"				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       **UpperCamelCase__				,       ):
					super().__init__(pad_token_id=UpperCamelCase__				,       mask_token_id=UpperCamelCase__				,       **UpperCamelCase__		)

					A__  :	Optional[Any]         =							vocab_size
					A__  :	int         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Tuple         =							num_attention_heads
					A__  :	str         =							intermediate_size
					A__  :	List[str]         =							hidden_dropout_prob
					A__  :	Optional[Any]         =							attention_probs_dropout_prob
					A__  :	int         =							max_position_embeddings
					A__  :	List[str]         =							initializer_range
					A__  :	List[Any]         =							layer_norm_eps
					A__  :	int         =							position_embedding_type
					A__  :	Optional[Any]         =							use_cache
					A__  :	Optional[int]         =							emb_layer_norm_before
					A__  :	List[str]         =							token_dropout
					A__  :	Tuple         =							is_folding_model
					if is_folding_model:
									if esmfold_config is None:
													logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
													A__  :	List[Any]         =							EsmFoldConfig()
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Optional[int]         =							EsmFoldConfig(**UpperCamelCase__		)
									A__  :	int         =							esmfold_config
									if vocab_list is None:
													logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
													A__  :	Any         =							get_default_vocab_list()
									else:
													A__  :	Dict         =							vocab_list
					else:
									A__  :	Optional[Any]         =							None
									A__  :	Tuple         =							None
					if self.esmfold_config is not None and getattr(self.esmfold_config				,       '''use_esm_attn_map'''				,       UpperCamelCase__		):
									raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							super().to_dict()
					if isinstance(self.esmfold_config				,       UpperCamelCase__		):
									A__  :	Dict         =							self.esmfold_config.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None
	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				0

	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False

	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.trunk is None:
									A__  :	Tuple         =							TrunkConfig()
					elif isinstance(self.trunk				,       UpperCamelCase__		):
									A__  :	List[Any]         =							TrunkConfig(**self.trunk		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							asdict(self		)
					A__  :	int         =							self.trunk.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				48
	_lowerCAmelCase							       =				1_024
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.structure_module is None:
									A__  :	str         =							StructureModuleConfig()
					elif isinstance(self.structure_module				,       UpperCamelCase__		):
									A__  :	str         =							StructureModuleConfig(**self.structure_module		)

					if self.max_recycles <= 0:
									raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}."		)
					if self.sequence_state_dim % self.sequence_state_dim != 0:
									raise ValueError(
									    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
									    F" {self.sequence_state_dim} and {self.sequence_state_dim}."		)
					if self.pairwise_state_dim % self.pairwise_state_dim != 0:
									raise ValueError(
									    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
									    F" {self.pairwise_state_dim} and {self.pairwise_state_dim}."		)

					A__  :	Tuple         =							self.sequence_state_dim // self.sequence_head_width
					A__  :	int         =							self.pairwise_state_dim // self.pairwise_head_width

					if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
									raise ValueError(
									    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
									    F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."		)
					if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
									raise ValueError(
									    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
									    F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."		)
					if self.pairwise_state_dim % 2 != 0:
									raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."		)

					if self.dropout >= 0.4:
									raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}."		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							asdict(self		)
					A__  :	Optional[int]         =							self.structure_module.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				384
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				16
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				12
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				0.1
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				1
	_lowerCAmelCase							       =				2
	_lowerCAmelCase							       =				7
	_lowerCAmelCase							       =				10
	_lowerCAmelCase							       =				1e-8
	_lowerCAmelCase							       =				1e5





	def   __snake_case				(			self		):
					return asdict(self		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				return (
				    "<cls>",
				    "<pad>",
				    "<eos>",
				    "<unk>",
				    "L",
				    "A",
				    "G",
				    "V",
				    "S",
				    "E",
				    "R",
				    "T",
				    "I",
				    "D",
				    "P",
				    "K",
				    "Q",
				    "N",
				    "F",
				    "Y",
				    "M",
				    "H",
				    "W",
				    "C",
				    "X",
				    "B",
				    "U",
				    "Z",
				    "O",
				    ".",
				    "-",
				    "<null_1>",
				    "<mask>",
				)