



"""simple docstring"""







from __future__ import annotations

import requests

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

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

							A         =					{}
							for id_ in range(lowerCamelCase__			):
														A         =					{
														    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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


A    =			['bert-base-uncased', 'bert-base-cased']
A    =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():






							class      UpperCAmelCase__		(      tf.keras.Model     ):



									def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]				)			->						List[str]:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFAutoModel.from_config(snake_case				)



									def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						Dict:






																'''simple docstring'''

																A         =					self.tokenizer(snake_case				)
																A         =					self.bert(**snake_case				)
																return out["pooler_output"]






@require_tf
@require_tensorflow_text
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    BertTokenizer.from_pretrained(snake_case				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									A         =					[TFBertTokenizer.from_pretrained(snake_case				) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(snake_case      ,							use_fast_bert_tokenizer=snake_case				)
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers				) == len(self.tf_tokenizers				)

									A         =					[
									    'This is a straightforward English test sentence.',
									    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
									    'Now we\'re going to add some Chinese: 一 二 三 一二三',
									    'And some much more rare Chinese: 齉 堃 齉堃',
									    'Je vais aussi écrire en français pour tester les accents',
									    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
									]
									A         =					list(zip(self.test_sentences      ,							self.test_sentences[::-1]				)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,							self.tf_tokenizers				):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tokenizer(snake_case      ,							return_tensors='tf'      ,							padding='longest'				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in python_outputs.keys():
																														self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape				)				)
																														self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]      ,							tf.intaa				) == tf_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf_tokenizer(self.paired_sentences				)
																A         =					tf_tokenizer(
																    text=[sentence[0] for sentence in self.paired_sentences]      ,							text_pair=[sentence[1] for sentence in self.paired_sentences]      ,							)
																for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]      ,							tf.intaa				) == separated_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Any				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor(self.test_sentences				)
																A         =					model(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							model.save(snake_case				)
																							A         =					tf.keras.models.load_model(snake_case				)
																A         =					loaded_model(snake_case				)
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output				)				)      ,							1E-5				)



