


'''simple docstring'''






from __future__ import annotations

import requests

__UpperCAmelCase       							=				set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)


def    _snake_case  (	A					,       A = 1					,       A = "new"					,       A = None			)		->       dict:
							lowerCAmelCase__			  =      wanted_data or []
							if invalid_search_terms := ", ".join(sorted(set(A			) - valid_terms			)			):
														lowerCAmelCase__			  =      F"""Invalid search term: {invalid_search_terms}"""
														raise ValueError(A			)
							lowerCAmelCase__			  =      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

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

							lowerCAmelCase__			  =      {}
							for id_ in range(A			):
														lowerCAmelCase__			  =      {
														    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 logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d