



"""simple docstring"""






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 			__snake_case			(      _lowercase    ,	unittest.TestCase):
  snake_case__							:  Optional[int]         =		DebertaTokenizer
  snake_case__							:  Optional[int]         =		True
  snake_case__							:  Dict         =		DebertaTokenizerFast




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowerCamelCase		:							Any            =					[
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''[UNK]''',
         ]
         _lowerCamelCase		:							Any            =					dict(zip(__lowerCAmelCase		,   range(len(__lowerCAmelCase						)						)						)						)
         _lowerCamelCase		:							Dict            =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         _lowerCamelCase		:							Dict            =					{'''unk_token''': '''[UNK]'''}

         _lowerCamelCase		:							List[Any]            =					os.path.join(self.tmpdirname		,   VOCAB_FILES_NAMES['''vocab_file''']						)
         _lowerCamelCase		:							List[Any]            =					os.path.join(self.tmpdirname		,   VOCAB_FILES_NAMES['''merges_file''']						)
         with open(self.vocab_file		,   '''w'''		,   encoding='''utf-8'''						) as fp:
                fp.write(json.dumps(__lowerCAmelCase						) + '''\n'''						)
         with open(self.merges_file		,   '''w'''		,   encoding='''utf-8'''						) as fp:
                fp.write('''\n'''.join(__lowerCAmelCase						)						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   **__lowerCAmelCase	:		Any						):






         """simple docstring"""






         kwargs.update(self.special_tokens_map						)
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,   **__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					'''lower newer'''
         _lowerCamelCase		:							Any            =					'''lower newer'''
         return input_text, output_text




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					self.get_tokenizer()
         _lowerCamelCase		:							List[Any]            =					'''lower newer'''
         _lowerCamelCase		:							int            =					['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         _lowerCamelCase		:							Tuple            =					tokenizer.tokenize(__lowerCAmelCase						)
         self.assertListEqual(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Optional[int]            =					tokens + [tokenizer.unk_token]
         _lowerCamelCase		:							Optional[Any]            =					[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase						)		,   __lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					self.get_tokenizer()
         _lowerCamelCase		:							List[Any]            =					tokenizer('''Hello'''		,   '''World'''						)
         _lowerCamelCase		:							Any            =					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['''token_type_ids''']		,   __lowerCAmelCase						)




  @slow
  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

         _lowerCamelCase		:							List[str]            =					tokenizer.encode('''sequence builders'''		,   add_special_tokens=__lowerCAmelCase						)
         _lowerCamelCase		:							List[Any]            =					tokenizer.encode('''multi-sequence build'''		,   add_special_tokens=__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					tokenizer.encode(
             '''sequence builders'''		,   add_special_tokens=__lowerCAmelCase		,   add_prefix_space=__lowerCAmelCase						)
         _lowerCamelCase		:							List[Any]            =					tokenizer.encode(
             '''sequence builders'''		,   '''multi-sequence build'''		,   add_special_tokens=__lowerCAmelCase		,   add_prefix_space=__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase		,   __lowerCAmelCase						)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode




  @slow
  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					[self.tokenizer_class]
         if self.test_rust_tokenizer:
                tokenizer_classes.append(self.rust_tokenizer_class						)

         for tokenizer_class in tokenizer_classes:
                _lowerCamelCase		:							List[Any]            =					tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

                _lowerCamelCase		:							Optional[Any]            =					[
                    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                    '''ALBERT incorporates two parameter reduction techniques''',
                    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                    ''' vocabulary embedding.''',
                ]

                _lowerCamelCase		:							Optional[int]            =					tokenizer(__lowerCAmelCase		,   padding=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					[tokenizer.decode(__lowerCAmelCase		,   skip_special_tokens=__lowerCAmelCase						) for seq in encoding['''input_ids''']]

                # fmt: off
                _lowerCamelCase		:							Any            =					{
                    '''input_ids''': [
                        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
                    ],
                    '''token_type_ids''': [
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                    ],
                    '''attention_mask''': [
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                    ]
                }
                # fmt: on

                _lowerCamelCase		:							Any            =					[
                    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                    '''ALBERT incorporates two parameter reduction techniques''',
                    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                    ''' vocabulary embedding.''',
                ]

                self.assertDictEqual(encoding.data		,   __lowerCAmelCase						)

                for expected, decoded in zip(__lowerCAmelCase		,   __lowerCAmelCase						):
                       self.assertEqual(__lowerCAmelCase		,   __lowerCAmelCase						)






from __future__ import annotations

import requests

lowerCAmelCase       		=    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							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							Optional[int]	    =	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

						__UpperCAmelCase			:							List[str]	    =	response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(lowercase_      )}

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							str	    =	{
												    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"""]))

