






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]				):
			# 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 >= 0x4e00 and cp <= 0x9fff)
			    or (cp >= 0x3400 and cp <= 0x4dbf)  #
			    or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
			    or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
			    or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
			    or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
			    or (cp >= 0xf900 and cp <= 0xfaff)
			    or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
			):  #
						return True

			return False

def        UpperCAmelCase__		(						lowerCamelCase_       :       str				):
			# word like '180' or '身高' or '神'
			for char in word:
						__a						:  Optional[int]									=     ord(lowerCamelCase_				)
						if not _is_chinese_char(lowerCamelCase_				):
									return 0
			return 1

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]				):
			__a						:  Union[str, Any]									=     set()

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       set()				):
			if not chinese_word_set:
						return bert_tokens
			__a						:  Tuple									=     max([len(lowerCamelCase_				) for w in chinese_word_set]				)

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       LTP   ,  lowerCamelCase_       :       BertTokenizer				):
			__a						:  Union[str, Any]									=     []

			for i in range(0   ,  len(lowerCamelCase_				)   ,  1_0_0				):
						__a						:  List[Any]									=     ltp_tokenizer.pipeline(lines[i : i + 1_0_0]   ,  tasks=['cws']				).cws
						__a						:  Optional[int]									=     [get_chinese_word(lowerCamelCase_				) for r in res]
						ltp_res.extend(lowerCamelCase_				)
			assert len(lowerCamelCase_				) == len(lowerCamelCase_				)

			__a						:  Optional[Any]									=     []
			for i in range(0   ,  len(lowerCamelCase_				)   ,  1_0_0				):
						__a						:  Union[str, Any]									=     bert_tokenizer(lines[i : i + 1_0_0]   ,  add_special_tokens=lowerCamelCase_   ,  truncation=lowerCamelCase_   ,  max_length=5_1_2				)
						bert_res.extend(res['input_ids']				)
			assert len(lowerCamelCase_				) == len(lowerCamelCase_				)

			__a						:  Union[str, Any]									=     []
			for input_ids, chinese_word in zip(lowerCamelCase_   ,  lowerCamelCase_				):
						__a						:  str									=     []
						for id in input_ids:
									__a						:  Optional[int]									=     bert_tokenizer._convert_id_to_token(lowerCamelCase_				)
									input_tokens.append(lowerCamelCase_				)
						__a						:  int									=     add_sub_symbol(lowerCamelCase_   ,  lowerCamelCase_				)
						__a						:  List[Any]									=     []
						# 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] == "##":
												__a						:  Dict									=     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        UpperCAmelCase__		(						lowerCamelCase_       :       str				):
			# 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:
						__a						:  int									=     f.readlines()
			__a						:  Optional[Any]									=     [line.strip() for line in data if len(lowerCamelCase_				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			__a						:  int									=     LTP(args.ltp				)  # faster in GPU device
			__a						:  Dict									=     BertTokenizer.from_pretrained(args.bert				)

			__a						:  Union[str, Any]									=     prepare_ref(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			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__				     =			parser.parse_args()
							main(args)









from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

