

'''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 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=30    ,			_snake_case=2    ,			_snake_case=3    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=32    ,			_snake_case=2    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=10    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=None    ,			)							->				Optional[int]:
								UpperCAmelCase_		:      Tuple       	=					parent
								UpperCAmelCase_		:      List[str]       	=					batch_size
								UpperCAmelCase_		:      List[Any]       	=					image_size
								UpperCAmelCase_		:      Any       	=					patch_size
								UpperCAmelCase_		:      List[Any]       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					is_training
								UpperCAmelCase_		:      Tuple       	=					use_labels
								UpperCAmelCase_		:      Any       	=					hidden_size
								UpperCAmelCase_		:      Union[str, Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Any       	=					num_attention_heads
								UpperCAmelCase_		:      str       	=					intermediate_size
								UpperCAmelCase_		:      Any       	=					hidden_act
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      int       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Tuple       	=					type_sequence_label_size
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      Dict       	=					scope

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


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Union[str, Any]       	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:      Optional[Any]       	=					None
								if self.use_labels:
									UpperCAmelCase_		:      List[str]       	=					ids_tensor([self.batch_size]    ,			self.type_sequence_label_size)

								UpperCAmelCase_		:      str       	=					self.get_config()

								return config, pixel_values, labels


							def 						_snake_case							(			self)							->				Union[str, Any]:
								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=lowerCamelCase__    ,			initializer_range=self.initializer_range    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Any       	=					TFViTModel(config=lowerCamelCase__)
								UpperCAmelCase_		:      Union[str, Any]       	=					model(lowerCamelCase__    ,			training=lowerCamelCase__)
								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.
								UpperCAmelCase_		:      str       	=					self.image_size // 2
								UpperCAmelCase_		:      List[str]       	=					pixel_values[:, :, :image_size, :image_size]
								UpperCAmelCase_		:      int       	=					model(lowerCamelCase__    ,			interpolate_pos_encoding=lowerCamelCase__    ,			training=lowerCamelCase__)
								UpperCAmelCase_		:      Any       	=					(image_size // self.patch_size) ** 2 + 1
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					self.type_sequence_label_size
								UpperCAmelCase_		:      Dict       	=					TFViTForImageClassification(lowerCamelCase__)
								UpperCAmelCase_		:      Dict       	=					model(lowerCamelCase__    ,			labels=lowerCamelCase__    ,			training=lowerCamelCase__)
								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.
								UpperCAmelCase_		:      List[str]       	=					self.image_size // 2
								UpperCAmelCase_		:      Optional[Any]       	=					pixel_values[:, :, :image_size, :image_size]
								UpperCAmelCase_		:      Dict       	=					model(lowerCamelCase__    ,			interpolate_pos_encoding=lowerCamelCase__    ,			training=lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size))

								# test greyscale images
								UpperCAmelCase_		:      Optional[Any]       	=					1
								UpperCAmelCase_		:      Optional[int]       	=					TFViTForImageClassification(lowerCamelCase__)
								UpperCAmelCase_		:      Optional[Any]       	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								UpperCAmelCase_		:      List[Any]       	=					model(lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size))





							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      Dict       	=					self.prepare_config_and_inputs()
								UpperCAmelCase_		:      List[Any]       	=					config_and_inputs
								UpperCAmelCase_		:      Any       	=					{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_tf
class 					lowercase						(  __lowerCAmelCase,					__lowerCAmelCase,					unittest.TestCase ):
							_lowerCamelCase		:      List[str]=   (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
							_lowerCamelCase		:      int=   (
							    {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							_lowerCamelCase		:      Dict=   False
							_lowerCamelCase		:      str=   False
							_lowerCamelCase		:      int=   False


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Optional[int]       	=					TFViTModelTester(self)
								UpperCAmelCase_		:      Dict       	=					ConfigTester(self    ,			config_class=lowerCamelCase__    ,			has_text_modality=lowerCamelCase__    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				Optional[int]:
								self.config_tester.run_common_tests()


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


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


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Dict       	=					model_class(lowerCamelCase__)
									self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer))
									UpperCAmelCase_		:      Tuple       	=					model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowerCamelCase__    ,			tf.keras.layers.Layer))


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_		:      Optional[Any]       	=					['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,			lowerCamelCase__)


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Dict       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__)





							@slow
							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      List[str]       	=					TFViTModel.from_pretrained('google/vit-base-patch16-224')
								self.assertIsNotNone(lowerCamelCase__)







def 						SCREAMING_SNAKE_CASE(  )    ->    Optional[int]:
	UpperCAmelCase_		:      Optional[int]       	=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







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


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





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

								UpperCAmelCase_		:      str       	=					self.default_image_processor
								UpperCAmelCase_		:      Optional[int]       	=					prepare_img()
								UpperCAmelCase_		:      str       	=					image_processor(images=lowerCamelCase__    ,			return_tensors='tf')

								# forward pass
								UpperCAmelCase_		:      List[Any]       	=					model(**lowerCamelCase__)

								# verify the logits
								UpperCAmelCase_		:      Optional[int]       	=					tf.TensorShape((1, 1000))
								self.assertEqual(outputs.logits.shape    ,			lowerCamelCase__)

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

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





'''simple docstring'''


from __future__ import annotations

import requests

lowerCAmelCase__										= 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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1   ,UpperCamelCase = "new"   ,UpperCamelCase = None  )    ->    dict:
	UpperCAmelCase_		:      Dict       	=					wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase  ) - valid_terms  )  ):
		UpperCAmelCase_		:      str       	=					f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					requests.get(
	    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""   ,headers={'User-agent': 'A random string'}   ,)
	if response.status_code == 4_2_9:
		raise requests.HTTPError

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

	UpperCAmelCase_		:      List[str]       	=					{}
	for id_ in range(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					{
		    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"]))






