
'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





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





def        _A						(							lowercase__		):
						lowercase__							       =				set()

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





def        _A						(							lowercase__						,						lowercase__		):
						if not chinese_word_set:
												return bert_tokens
						lowercase__							       =				max([len(lowercase__		) for w in chinese_word_set]		)

						lowercase__							       =				bert_tokens
						lowercase__       ,  lowercase__							       =				0, len(lowercase__		)
						while start < end:
												lowercase__							       =				True
												if is_chinese(bert_word[start]		):
																		lowercase__							       =				min(end - start						,						lowercase__		)
																		for i in range(lowercase__						,						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						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				[]

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

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

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

						assert len(lowercase__		) == len(lowercase__		)

						return ref_ids





def        _A						(							lowercase__		):
						# 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(lowercase__		) > 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(lowercase__						,						lowercase__						,						lowercase__		)

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


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







"""simple docstring"""


import requests

UpperCAmelCase__     						=  """"""  # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase__     						=  """https://api.openweathermap.org/data/2.5/"""






def 			__UpperCAmelCase    (	lowercase = "Chicago"      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """weather"""      ,params=locals()						).json()






def 			__UpperCAmelCase    (	lowercase = "Kolkata, India"      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """forecast"""      ,params=locals()						).json()






def 			__UpperCAmelCase    (	lowercase = 55.68      ,lowercase = 12.57      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """onecall"""      ,params=locals()						).json()


if __name__ == "__main__":
    from pprint import pprint

    while True:
        UpperCAmelCase__     						=  input("""Enter a location:""").strip()
        if location:
            pprint(current_weather(location))
        else:
            break
