


"""simple docstring"""


from __future__ import annotations

import requests

a_       							=    set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)






def      a__ (				__lowercase     ,			__lowercase = 1     ,			__lowercase = "new"     ,			__lowercase = None   )	->	dict:
				_A					  =			wanted_data or []
				if invalid_search_terms := ", ".join(sorted(set(__lowercase   ) - valid_terms   )   ):
								_A					  =			f"""Invalid search term: {invalid_search_terms}"""
								raise ValueError(__lowercase   )
				_A					  =			requests.get(
				    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""     ,			headers={"User-agent": "A random string"}     ,			)
				if response.status_code == 429:
								raise requests.HTTPError

				_A					  =			response.json()
				if not wanted_data:
								return {id_: data["data"]["children"][id_] for id_ in range(__lowercase   )}

				_A					  =			{}
				for id_ in range(__lowercase   ):
								_A					  =			{
								    item: data["data"]["children"][id_]["data"][item] for item in wanted_data
								}
				return data_dict


if __name__ == "__main__":
		# If you get Error 429, that means you are rate limited.Try after some time
		print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))



"""simple docstring"""


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


a_       							=    logging.get_logger(__name__)
@dataclass
class snake_case      :
		__UpperCamelCase           =			field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
		__UpperCamelCase           =			field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase           =			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.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Overwrite the cached training and evaluation sets'})







		def  a_ (					self      :       Optional[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.task_name.lower()





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'train'
		__UpperCamelCase           =			'dev'
		__UpperCamelCase           =			'test'





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42







		def __init__(					self      :       Optional[int]    ,   a__      :       GlueDataTrainingArguments    ,   a__      :       PreTrainedTokenizerBase    ,   a__      :       Optional[int] = None    ,   a__      :       Union[str, Split] = Split.train    ,   a__      :       Optional[str] = None    ,   )       ->						Tuple:





						'''simple docstring'''







						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      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						return len(self.features		)







		def __getitem__(					self      :       Tuple    ,   a__      :       Union[str, Any]		)       ->						InputFeatures:





						'''simple docstring'''







						return self.features[i]







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						return self.label_list