




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 		snake_case_   (UpperCamelCase	:  str				):





							'''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 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							for char in word:
														_a     					=    ord(UpperCamelCase				)
														if not _is_chinese_char(UpperCamelCase				):
																					return 0
							return 1
def 		snake_case_   (UpperCamelCase	:  List[str]				):





							'''simple docstring'''


							_a     					=    set()

							for token in tokens:
														_a     					=    len(UpperCamelCase				) > 1 and is_chinese(UpperCamelCase				)
														if chinese_word:
																					word_set.add(UpperCamelCase				)
							_a     					=    list(UpperCamelCase				)
							return word_list
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  set()				):





							'''simple docstring'''


							if not chinese_word_set:
														return bert_tokens
							_a     					=    max([len(UpperCamelCase				) for w in chinese_word_set]				)

							_a     					=    bert_tokens
							_a					,							_a     					=    0, len(UpperCamelCase				)
							while start < end:
														_a     					=    True
														if is_chinese(bert_word[start]				):
																					_a     					=    min(end - start			,						UpperCamelCase				)
																					for i in range(UpperCamelCase			,						1			,						-1				):
																												_a     					=    ''''''.join(bert_word[start : start + i]				)
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1			,						start + i				):
																																										_a     					=    '''##''' + bert_word[j]
																																			_a     					=    start + i
																																			_a     					=    False
																																			break
														if single_word:
																					start += 1
							return bert_word
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  LTP			,						UpperCamelCase	:  BertTokenizer				):





							'''simple docstring'''


							_a     					=    []

							for i in range(0			,						len(UpperCamelCase				)			,						100				):
														_a     					=    ltp_tokenizer.seg(lines[i : i + 100]				)[0]
														_a     					=    [get_chinese_word(UpperCamelCase				) for r in res]
														ltp_res.extend(UpperCamelCase				)
							assert len(UpperCamelCase				) == len(UpperCamelCase				)

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

							_a     					=    []
							for input_ids, chinese_word in zip(UpperCamelCase			,						UpperCamelCase				):
														_a     					=    []
														for id in input_ids:
																					_a     					=    bert_tokenizer._convert_id_to_token(UpperCamelCase				)
																					input_tokens.append(UpperCamelCase				)
														_a     					=    add_sub_symbol(UpperCamelCase			,						UpperCamelCase				)
														_a     					=    []
														# 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     					=    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 		snake_case_   (UpperCamelCase	:  Any				):





							'''simple docstring'''


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

							_a     					=    prepare_ref(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)

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


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

				_snake_case				:				str       							=		parser.parse_args()
				main(args)







'''simple docstring'''


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    {}

							_a     					=    job['''started_at''']
							_a     					=    job['''completed_at''']

							_a     					=    date_parser.parse(UpperCamelCase				)
							_a     					=    date_parser.parse(UpperCamelCase				)

							_a     					=    round((end_datetime - start_datetime).total_seconds() / 60.0				)

							_a     					=    start
							_a     					=    end
							_a     					=    duration_in_min

							return job_info
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  int=None				):





							'''simple docstring'''


							_a     					=    None
							if token is not None:
														_a     					=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

							_a     					=    f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
							_a     					=    requests.get(UpperCamelCase			,						headers=UpperCamelCase				).json()
							_a     					=    {}

							try:
														job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)
														_a     					=    math.ceil((result['''total_count'''] - 100) / 100				)

														for i in range(UpperCamelCase				):
																					_a     					=    requests.get(url + f'&page={i + 2}'			,						headers=UpperCamelCase				).json()
																					job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)

														return job_time
							except Exception:
														print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'				)

							return {}


if __name__ == "__main__":
				_snake_case				:				int       							=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
				_snake_case				:				Tuple       							=		parser.parse_args()

				_snake_case				:				int       							=		get_job_time(args.workflow_run_id)
				_snake_case				:				int       							=		dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

				for k, v in job_time.items():
								print(F'''{k}: {v['duration']}''')

