






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				# 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 >= 0X4E_00 and cp <= 0X9F_FF)
				    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
				    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
				    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
				    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
				    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
				    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
				    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
				):  #
								return True

				return False






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				# word like '180' or '身高' or '神'
				for char in word:
								lowercase__    =			ord(lowerCamelCase_				)
								if not _is_chinese_char(lowerCamelCase_				):
												return 0
				return 1






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			set()

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






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				if not chinese_word_set:
								return bert_tokens
				lowercase__    =			max([len(lowerCamelCase_				) for w in chinese_word_set]				)

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






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]

				for i in range(0		,   len(lowerCamelCase_				)		,   100				):
								lowercase__    =			ltp_tokenizer.pipeline(lines[i : i + 100]		,   tasks=['''cws''']				).cws
								lowercase__    =			[get_chinese_word(lowerCamelCase_				) for r in res]
								ltp_res.extend(lowerCamelCase_				)
				assert len(lowerCamelCase_				) == len(lowerCamelCase_				)

				lowercase__    =			[]
				for i in range(0		,   len(lowerCamelCase_				)		,   100				):
								lowercase__    =			bert_tokenizer(lines[i : i + 100]		,   add_special_tokens=lowerCamelCase_		,   truncation=lowerCamelCase_		,   max_length=512				)
								bert_res.extend(res['''input_ids''']				)
				assert len(lowerCamelCase_				) == len(lowerCamelCase_				)

				lowercase__    =			[]
				for input_ids, chinese_word in zip(lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			[]
								for id in input_ids:
												lowercase__    =			bert_tokenizer._convert_id_to_token(lowerCamelCase_				)
												input_tokens.append(lowerCamelCase_				)
								lowercase__    =			add_sub_symbol(lowerCamelCase_		,   lowerCamelCase_				)
								lowercase__    =			[]
								# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
								for i, token in enumerate(lowerCamelCase_				):
												if token[:2] == "##":
																lowercase__    =			token[2:]
																# save chinese tokens' pos
																if len(lowerCamelCase_				) == 1 and _is_chinese_char(ord(lowerCamelCase_				)				):
																				ref_id.append(lowerCamelCase_				)
								ref_ids.append(lowerCamelCase_				)

				assert len(lowerCamelCase_				) == len(lowerCamelCase_				)

				return ref_ids






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				# 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__    =			f.readlines()
				lowercase__    =			[line.strip() for line in data if len(lowerCamelCase_				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				lowercase__    =			LTP(args.ltp				)  # faster in GPU device
				lowercase__    =			BertTokenizer.from_pretrained(args.bert				)

				lowercase__    =			prepare_ref(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)

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


if __name__ == "__main__":
							A__	:			Any			   = 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',
							)

							A__	:			Dict			   = parser.parse_args()
							main(args)












from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				lowercase__    =			[]
				lowercase__    =			[]

				for rt in rc.restypes:
								lowercase__    =			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
								lowercase__    =			{name: i for i, name in enumerate(lowerCamelCase_				)}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14				)
				restype_atomaa_to_atomaa_list.append([0] * 37				)
				restype_atomaa_mask_list.append([0.0] * 14				)

				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.floataa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			protein['''aatype'''].to(torch.long				)

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			restype_atomaa_mask[protein_aatype]

				lowercase__    =			residx_atomaa_mask
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				lowercase__    =			torch.zeros([21, 37]		,   dtype=torch.floataa		,   device=protein['''aatype'''].device				)
				for restype, restype_letter in enumerate(rc.restypes				):
								lowercase__    =			rc.restype_atoa[restype_letter]
								lowercase__    =			rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowercase__    =			rc.atom_order[atom_name]
												lowercase__    =			1

				lowercase__    =			restype_atomaa_mask[protein_aatype]
				lowercase__    =			residx_atomaa_mask

				return protein






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			tree_map(lambda lowerCamelCase_				: torch.tensor(lowerCamelCase_		,   device=batch['''aatype'''].device				)		,   lowerCamelCase_		,   np.ndarray				)
				lowercase__    =			tensor_tree_map(lambda lowerCamelCase_				: np.array(lowerCamelCase_				)		,   make_atomaa_masks(lowerCamelCase_				)				)
				return out




