"""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      UpperCAmelCase_	(    _lowercase ,							unittest.TestCase):
      lowerCamelCase__		:    List[Any]   =  DebertaTokenizer
      lowerCamelCase__		:    str   =  True
      lowerCamelCase__		:    List[Any]   =  DebertaTokenizerFast


      def 		_UpperCAmelCase       (   self							)       ->					int:
             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             lowercase__			:		Optional[int]       =	[
                 """l""",
                 """o""",
                 """w""",
                 """e""",
                 """r""",
                 """s""",
                 """t""",
                 """i""",
                 """d""",
                 """n""",
                 """\u0120""",
                 """\u0120l""",
                 """\u0120n""",
                 """\u0120lo""",
                 """\u0120low""",
                 """er""",
                 """\u0120lowest""",
                 """\u0120newer""",
                 """\u0120wider""",
                 """[UNK]""",
             ]
             lowercase__			:		Union[str, Any]       =	dict(zip(UpperCamelCase__		,			range(len(UpperCamelCase__							)							)							)							)
             lowercase__			:		Optional[Any]       =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
             lowercase__			:		Optional[Any]       =	{"""unk_token""": """[UNK]"""}

             lowercase__			:		Union[str, Any]       =	os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['vocab_file']							)
             lowercase__			:		Optional[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(UpperCamelCase__							) + '\n'							)
             with open(self.merges_file		,			'w'		,			encoding='utf-8'							) as fp:
                    fp.write('\n'.join(UpperCamelCase__							)							)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					int:
             kwargs.update(self.special_tokens_map							)
             return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**UpperCamelCase__							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					str:
             lowercase__			:		Tuple       =	"""lower newer"""
             lowercase__			:		Any       =	"""lower newer"""
             return input_text, output_text


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Union[str, Any]       =	self.get_tokenizer()
             lowercase__			:		int       =	"""lower newer"""
             lowercase__			:		List[Any]       =	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
             lowercase__			:		Any       =	tokenizer.tokenize(UpperCamelCase__							)
             self.assertListEqual(UpperCamelCase__		,			UpperCamelCase__							)

             lowercase__			:		List[Any]       =	tokens + [tokenizer.unk_token]
             lowercase__			:		Any       =	[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)		,			UpperCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		List[str]       =	self.get_tokenizer()
             lowercase__			:		Union[str, Any]       =	tokenizer('Hello'		,			'World'							)
             lowercase__			:		Optional[Any]       =	[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
             self.assertListEqual(tokd['token_type_ids']		,			UpperCamelCase__							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Union[str, Any]       =	self.tokenizer_class.from_pretrained('microsoft/deberta-base'							)

             lowercase__			:		List[str]       =	tokenizer.encode('sequence builders'		,			add_special_tokens=UpperCamelCase__							)
             lowercase__			:		Optional[int]       =	tokenizer.encode('multi-sequence build'		,			add_special_tokens=UpperCamelCase__							)

             lowercase__			:		str       =	tokenizer.encode(
                 'sequence builders'		,			add_special_tokens=UpperCamelCase__		,			add_prefix_space=UpperCamelCase__							)
             lowercase__			:		List[str]       =	tokenizer.encode(
                 'sequence builders'		,			'multi-sequence build'		,			add_special_tokens=UpperCamelCase__		,			add_prefix_space=UpperCamelCase__							)

             lowercase__			:		Union[str, Any]       =	tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
             lowercase__			:		List[Any]       =	tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		,			UpperCamelCase__							)

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		Tuple       =	[self.tokenizer_class]
             if self.test_rust_tokenizer:
                    tokenizer_classes.append(self.rust_tokenizer_class							)

             for tokenizer_class in tokenizer_classes:
                    lowercase__			:		Tuple       =	tokenizer_class.from_pretrained('microsoft/deberta-base'							)

                    lowercase__			:		List[str]       =	[
                        """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.""",
                    ]

                    lowercase__			:		List[Any]       =	tokenizer(UpperCamelCase__		,			padding=UpperCamelCase__							)
                    lowercase__			:		int       =	[tokenizer.decode(UpperCamelCase__		,			skip_special_tokens=UpperCamelCase__							) for seq in encoding["""input_ids"""]]

                    # fmt: off
                    lowercase__			:		Union[str, 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

                    lowercase__			:		str       =	[
                        """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		,			UpperCamelCase__							)

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









'''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''']))

