

'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__										= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase__										= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}



class 					lowercase						(  UpperCAmelCase__ ):
							_lowerCamelCase		:      int=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Optional[Any]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Any=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      int=   ["input_ids", "attention_mask"]
							_lowerCamelCase		:      List[Any]=   RobertaTokenizer


							def __init__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case="replace"    ,			_snake_case="<s>"    ,			_snake_case="</s>"    ,			_snake_case="</s>"    ,			_snake_case="<s>"    ,			_snake_case="<unk>"    ,			_snake_case="<pad>"    ,			_snake_case="<mask>"    ,			_snake_case=False    ,			_snake_case=True    ,			**_snake_case    ,			)							->				Any:
								super().__init__(
								    lowerCamelCase__    ,			lowerCamelCase__    ,			tokenizer_file=lowerCamelCase__    ,			errors=lowerCamelCase__    ,			bos_token=lowerCamelCase__    ,			eos_token=lowerCamelCase__    ,			sep_token=lowerCamelCase__    ,			cls_token=lowerCamelCase__    ,			unk_token=lowerCamelCase__    ,			pad_token=lowerCamelCase__    ,			mask_token=lowerCamelCase__    ,			add_prefix_space=lowerCamelCase__    ,			trim_offsets=lowerCamelCase__    ,			**lowerCamelCase__    ,			)

								UpperCAmelCase_		:      Tuple       	=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
								if pre_tok_state.get('add_prefix_space'    ,			lowerCamelCase__) != add_prefix_space:
									UpperCAmelCase_		:      Optional[Any]       	=					getattr(lowerCamelCase__    ,			pre_tok_state.pop('type'))
									UpperCAmelCase_		:      Dict       	=					add_prefix_space
									UpperCAmelCase_		:      Union[str, Any]       	=					pre_tok_class(**lowerCamelCase__)

								UpperCAmelCase_		:      List[Any]       	=					add_prefix_space

								UpperCAmelCase_		:      Optional[int]       	=					"post_processor"
								UpperCAmelCase_		:      Optional[Any]       	=					getattr(self.backend_tokenizer    ,			lowerCamelCase__    ,			lowerCamelCase__)
								if tokenizer_component_instance:
									UpperCAmelCase_		:      Tuple       	=					json.loads(tokenizer_component_instance.__getstate__())

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
										UpperCAmelCase_		:      List[str]       	=					tuple(state['sep'])
									if "cls" in state:
										UpperCAmelCase_		:      str       	=					tuple(state['cls'])

									UpperCAmelCase_		:      List[str]       	=					False

									if state.get('add_prefix_space'    ,			lowerCamelCase__) != add_prefix_space:
										UpperCAmelCase_		:      Any       	=					add_prefix_space
										UpperCAmelCase_		:      Any       	=					True

									if state.get('trim_offsets'    ,			lowerCamelCase__) != trim_offsets:
										UpperCAmelCase_		:      int       	=					trim_offsets
										UpperCAmelCase_		:      Union[str, Any]       	=					True

									if changes_to_apply:
										UpperCAmelCase_		:      Any       	=					getattr(lowerCamelCase__    ,			state.pop('type'))
										UpperCAmelCase_		:      Optional[int]       	=					component_class(**lowerCamelCase__)
										setattr(self.backend_tokenizer    ,			lowerCamelCase__    ,			lowerCamelCase__)


							@property
							def 						_snake_case							(			self)							->				str:







								if self._mask_token is None:
									if self.verbose:
										logger.error('Using mask_token, but it is not set yet.')
									return None
								return str(self._mask_token)


							@mask_token.setter
							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								UpperCAmelCase_		:      List[str]       	=					AddedToken(lowerCamelCase__    ,			lstrip=lowerCamelCase__    ,			rstrip=lowerCamelCase__) if isinstance(lowerCamelCase__    ,			lowerCamelCase__) else value
								UpperCAmelCase_		:      Any       	=					value


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				BatchEncoding:
								UpperCAmelCase_		:      int       	=					kwargs.get('is_split_into_words'    ,			lowerCamelCase__)
								assert self.add_prefix_space or not is_split_into_words, (
								    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*lowerCamelCase__    ,			**lowerCamelCase__)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				BatchEncoding:
								UpperCAmelCase_		:      Dict       	=					kwargs.get('is_split_into_words'    ,			lowerCamelCase__)

								assert self.add_prefix_space or not is_split_into_words, (
								    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*lowerCamelCase__    ,			**lowerCamelCase__)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								UpperCAmelCase_		:      List[Any]       	=					self._tokenizer.model.save(lowerCamelCase__    ,			name=lowerCamelCase__)
								return tuple(lowerCamelCase__)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
									return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								UpperCAmelCase_		:      Tuple       	=					[self.sep_token_id]
								UpperCAmelCase_		:      List[Any]       	=					[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]





'''simple docstring'''


from __future__ import annotations

import requests

lowerCAmelCase__										= 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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1   ,UpperCamelCase = "new"   ,UpperCamelCase = None  )    ->    dict:
	UpperCAmelCase_		:      Dict       	=					wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase  ) - valid_terms  )  ):
		UpperCAmelCase_		:      str       	=					f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					requests.get(
	    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""   ,headers={'User-agent': 'A random string'}   ,)
	if response.status_code == 4_2_9:
		raise requests.HTTPError

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

	UpperCAmelCase_		:      List[str]       	=					{}
	for id_ in range(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					{
		    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"]))






