
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()  )}  )
	snake_case_					    =     field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}  )
	snake_case_					    =     field(
	    default=128			,			metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Overwrite the cached training and evaluation sets"}  )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.task_name.lower()





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "train"
	snake_case_					    =     "dev"
	snake_case_					    =     "test"





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42






	def __init__(       self					: Union[str, Any]							,A					: GlueDataTrainingArguments							,A					: PreTrainedTokenizerBase							,A					: Optional[int] = None							,A					: Union[str, Split] = Split.train							,A					: Optional[str] = None							,):
					warnings.warn(
					    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
					    "library. You can have a look at this example script for pointers: "
					    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"							,A							,)
					__A   			= args
					__A   			= glue_processors[args.task_name]()
					__A   			= glue_output_modes[args.task_name]
					if isinstance(A							,A ):
									try:
													__A   			= Split[mode]
									except KeyError:
													raise KeyError("mode is not a valid split name" )
        # Load data features from cache or dataset file
					__A   			= os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir							,f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''							,)
					__A   			= self.processor.get_labels()
					if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
					    "RobertaTokenizer",
					    "RobertaTokenizerFast",
					    "XLMRobertaTokenizer",
					    "BartTokenizer",
					    "BartTokenizerFast",
					):
									# HACK(label indices are swapped in RoBERTa pretrained model)
									__A			,							__A   			= label_list[2], label_list[1]
					__A   			= label_list

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__A   			= cached_features_file + ".lock"
					with FileLock(A ):
									if os.path.exists(A ) and not args.overwrite_cache:
													__A   			= time.time()
													__A   			= torch.load(A )
													logger.info(
													    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''							,time.time() - start )
									else:
													logger.info(f'''Creating features from dataset file at {args.data_dir}''' )

													if mode == Split.dev:
																	__A   			= self.processor.get_dev_examples(args.data_dir )
													elif mode == Split.test:
																	__A   			= self.processor.get_test_examples(args.data_dir )
													else:
																	__A   			= self.processor.get_train_examples(args.data_dir )
													if limit_length is not None:
																	__A   			= examples[:limit_length]
													__A   			= glue_convert_examples_to_features(
													    A							,A							,max_length=args.max_seq_length							,label_list=A							,output_mode=self.output_mode							,)
													__A   			= time.time()
													torch.save(self.features							,A )
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )






	def __len__(       self					: int ):
					return len(self.features )






	def __getitem__(       self					: Tuple							,A					: List[Any] ):
					return self.features[i]







	def 				UpperCamelCase_  (       self					: List[Any] ):
					return self.label_list


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

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

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""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(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= requests.get(url + F'''&page={i + 2}'''		,   headers=a_						).json()
												job_time.update({job["name"]: extract_time_from_single_job(a_						) 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__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[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"]}''')
