import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def       __lowerCAmelCase      (						A    ):
	# 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 >= 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       __lowerCAmelCase      (						A    ):
	# word like '180' or '身高' or '神'
	for char in word:
		UpperCAmelCase_       				=			ord(A    )
		if not _is_chinese_char(A    ):
			return 0
	return 1



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			set()

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



def       __lowerCAmelCase      (						A       ,     A    ):
	if not chinese_word_set:
		return bert_tokens
	UpperCAmelCase_       				=			max([len(A    ) for w in chinese_word_set]    )

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



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[]

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

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

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

	assert len(A    ) == len(A    )

	return ref_ids



def       __lowerCAmelCase      (						A    ):
	# 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:
		UpperCAmelCase_       				=			f.readlines()
	UpperCAmelCase_       				=			[line.strip() for line in data if len(A    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	UpperCAmelCase_       				=			LTP(args.ltp    )  # faster in GPU device
	UpperCAmelCase_       				=			BertTokenizer.from_pretrained(args.bert    )

	UpperCAmelCase_       				=			prepare_ref(A       ,     A       ,     A    )

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


if __name__ == "__main__":
				_a:    Dict 							=			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:    List[Any] 							=			parser.parse_args()
				main(args)


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)