
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     _a					(						lowerCamelCase,	lowerCamelCase = 1,	lowerCamelCase = "new",	lowerCamelCase = None							):
 lowerCamelCase		:	Tuple							=      wanted_data or []
 if invalid_search_terms := ", ".join(sorted(set(lowerCamelCase							) - valid_terms							)							):
  lowerCamelCase		:	Tuple							=      F'''Invalid search term: {invalid_search_terms}'''
  raise ValueError(lowerCamelCase							)
 lowerCamelCase		:	Tuple							=      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		:	List[Any]							=      response.json()
 if not wanted_data:
  return {id_: data["data"]["children"][id_] for id_ in range(lowerCamelCase							)}

 lowerCamelCase		:	Dict							=      {}
 for id_ in range(lowerCamelCase							):
  lowerCamelCase		:	Dict							=      {
      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"""]))






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




