



'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			XLMTokenizer
	lowercase__  				=			False





	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_UpperCamelCase					:		Dict 				=   [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							_UpperCamelCase					:		int 				=   dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
							_UpperCamelCase					:		Dict 				=   ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							_UpperCamelCase					:		List[Any] 				=   os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['vocab_file'] )
							_UpperCamelCase					:		List[str] 				=   os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file ,'w' ) as fp:
													fp.write(json.dumps(lowerCamelCase__ ) )
							with open(self.merges_file ,'w' ) as fp:
													fp.write('\n'.join(lowerCamelCase__ ) )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   'lower newer'
							_UpperCamelCase					:		Dict 				=   'lower newer'
							return input_text, output_text





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   XLMTokenizer(self.vocab_file ,self.merges_file )

							_UpperCamelCase					:		List[str] 				=   'lower'
							_UpperCamelCase					:		int 				=   ['low', 'er</w>']
							_UpperCamelCase					:		int 				=   tokenizer.tokenize(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   tokens + ['<unk>']
							_UpperCamelCase					:		List[Any] 				=   [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   XLMTokenizer.from_pretrained('xlm-mlm-en-2048' )

							_UpperCamelCase					:		List[Any] 				=   tokenizer.encode('sequence builders' ,add_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   tokenizer.encode('multi-sequence build' ,add_special_tokens=lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ ,lowerCamelCase__ )

							assert encoded_sentence == [0] + text + [1]
							assert encoded_pair == [0] + text + [1] + text_a + [1]










'''simple docstring'''







from __future__ import annotations

import requests

_A	:			str         =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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase = 1				,			UpperCamelCase = "new"				,			UpperCamelCase = None      )      ->       dict:
							lowerCamelCase__					:	Any         =       wanted_data or []
							if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase      ) - valid_terms      )      ):
														lowerCamelCase__					:	str         =       f'''Invalid search term: {invalid_search_terms}'''
														raise ValueError(UpperCamelCase      )
							lowerCamelCase__					:	str         =       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__					:	Optional[int]         =       response.json()
							if not wanted_data:
														return {id_: data["data"]["children"][id_] for id_ in range(UpperCamelCase      )}

							lowerCamelCase__					:	Dict         =       {}
							for id_ in range(UpperCamelCase      ):
														lowerCamelCase__					:	Union[str, Any]         =       {
														    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''']))

