"""simple docstring"""





from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=3_0		,			a=2		,			a=3		,			a=True		,			a=True		,			a=3_2		,			a=2		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=1_0		,			a=0.02		,			a=3		,			a=None		,			)       ->					Tuple:
             lowercase__			:		List[Any]       =	parent
             lowercase__			:		str       =	batch_size
             lowercase__			:		Tuple       =	image_size
             lowercase__			:		Tuple       =	patch_size
             lowercase__			:		Dict       =	num_channels
             lowercase__			:		Optional[int]       =	is_training
             lowercase__			:		Optional[int]       =	use_labels
             lowercase__			:		Optional[int]       =	hidden_size
             lowercase__			:		Union[str, Any]       =	num_hidden_layers
             lowercase__			:		int       =	num_attention_heads
             lowercase__			:		Union[str, Any]       =	intermediate_size
             lowercase__			:		Union[str, Any]       =	hidden_act
             lowercase__			:		Union[str, Any]       =	hidden_dropout_prob
             lowercase__			:		Optional[int]       =	attention_probs_dropout_prob
             lowercase__			:		Dict       =	type_sequence_label_size
             lowercase__			:		int       =	initializer_range
             lowercase__			:		Optional[Any]       =	scope

             # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
             lowercase__			:		str       =	(image_size // patch_size) ** 2
             lowercase__			:		Optional[Any]       =	num_patches + 1


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		List[str]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__			:		int       =	None
             if self.use_labels:
                    lowercase__			:		Dict       =	ids_tensor([self.batch_size]		,			self.type_sequence_label_size							)

             lowercase__			:		Optional[int]       =	self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             return ViTConfig(
                 image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			is_decoder=UpperCamelCase__		,			initializer_range=self.initializer_range		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[str]:
             lowercase__			:		str       =	TFViTModel(config=UpperCamelCase__							)
             lowercase__			:		Dict       =	model(UpperCamelCase__		,			training=UpperCamelCase__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)

             # Test with an image with different size than the one specified in config.
             lowercase__			:		Tuple       =	self.image_size // 2
             lowercase__			:		Dict       =	pixel_values[:, :, :image_size, :image_size]
             lowercase__			:		Dict       =	model(UpperCamelCase__		,			interpolate_pos_encoding=UpperCamelCase__		,			training=UpperCamelCase__							)
             lowercase__			:		int       =	(image_size // self.patch_size) ** 2 + 1
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		List[str]       =	self.type_sequence_label_size
             lowercase__			:		Optional[Any]       =	TFViTForImageClassification(UpperCamelCase__							)
             lowercase__			:		Tuple       =	model(UpperCamelCase__		,			labels=UpperCamelCase__		,			training=UpperCamelCase__							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)							)

             # Test with an image with different size than the one specified in config.
             lowercase__			:		str       =	self.image_size // 2
             lowercase__			:		str       =	pixel_values[:, :, :image_size, :image_size]
             lowercase__			:		str       =	model(UpperCamelCase__		,			interpolate_pos_encoding=UpperCamelCase__		,			training=UpperCamelCase__							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)							)

             # test greyscale images
             lowercase__			:		Optional[Any]       =	1
             lowercase__			:		Tuple       =	TFViTForImageClassification(UpperCamelCase__							)
             lowercase__			:		Union[str, Any]       =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__			:		Optional[int]       =	model(UpperCamelCase__							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		str       =	self.prepare_config_and_inputs()
             lowercase__			:		Optional[int]       =	config_and_inputs
             lowercase__			:		List[str]       =	{"""pixel_values""": pixel_values}
             return config, inputs_dict





@require_tf
class      UpperCAmelCase_	(    _lowercase ,							_lowercase ,							unittest.TestCase):
      lowerCamelCase__		:    str   =  (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
      lowerCamelCase__		:    Optional[Any]   =  (
          {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
          if is_tf_available()
          else {}
      )

      lowerCamelCase__		:    Tuple   =  False
      lowerCamelCase__		:    Any   =  False
      lowerCamelCase__		:    Optional[int]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		List[str]       =	TFViTModelTester(self							)
             lowercase__			:		Any       =	ConfigTester(self		,			config_class=UpperCamelCase__		,			has_text_modality=UpperCamelCase__		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             self.config_tester.run_common_tests()


      @unittest.skip(reason='ViT does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             pass


      @unittest.skip(reason='ViT does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		Optional[Any]       =	model_class(UpperCamelCase__							)
                    self.assertIsInstance(model.get_input_embeddings()		,			(tf.keras.layers.Layer)							)
                    lowercase__			:		Any       =	model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(UpperCamelCase__		,			tf.keras.layers.Layer							)							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Any       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		List[str]       =	model_class(UpperCamelCase__							)
                    lowercase__			:		Any       =	inspect.signature(model.call							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__			:		str       =	[*signature.parameters.keys()]

                    lowercase__			:		List[str]       =	["""pixel_values"""]
                    self.assertListEqual(arg_names[:1]		,			UpperCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Optional[int]       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		List[str]       =	TFViTModel.from_pretrained('google/vit-base-patch16-224'							)
             self.assertIsNotNone(UpperCamelCase__							)





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image






@require_tf
@require_vision
class      UpperCAmelCase_	(    unittest.TestCase):


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'							) if is_vision_available() else None


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		str       =	TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'							)

             lowercase__			:		str       =	self.default_image_processor
             lowercase__			:		Optional[Any]       =	prepare_img()
             lowercase__			:		Optional[int]       =	image_processor(images=UpperCamelCase__		,			return_tensors='tf'							)

             # forward pass
             lowercase__			:		Union[str, Any]       =	model(**UpperCamelCase__							)

             # verify the logits
             lowercase__			:		int       =	tf.TensorShape((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape		,			UpperCamelCase__							)

             lowercase__			:		Dict       =	tf.constant([-0.2_744, 0.8_215, -0.0_836]							)

             tf.debugging.assert_near(outputs.logits[0, :3]		,			UpperCamelCase__		,			atol=1e-4							)









'''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''']))

