

'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class 					lowercase						(  unittest.TestCase ):


							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=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=10    ,			_snake_case=0.02    ,			)							->				Any:
								UpperCAmelCase_		:      List[Any]       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      str       	=					patch_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_channels
								UpperCAmelCase_		:      Optional[int]       	=					is_training
								UpperCAmelCase_		:      Optional[Any]       	=					use_labels
								UpperCAmelCase_		:      str       	=					hidden_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      List[Any]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Any       	=					hidden_act
								UpperCAmelCase_		:      List[Any]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Tuple       	=					type_sequence_label_size
								UpperCAmelCase_		:      str       	=					initializer_range

								# 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_		:      int       	=					num_patches + 1


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

								UpperCAmelCase_		:      Dict       	=					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=_snake_case    ,			initializer_range=self.initializer_range    ,			)

								return config, pixel_values


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					FlaxViTModel(config=_snake_case)
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_		:      Any       	=					(self.image_size, self.image_size)
								UpperCAmelCase_		:      Tuple       	=					(self.patch_size, self.patch_size)
								UpperCAmelCase_		:      int       	=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, num_patches + 1, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.type_sequence_label_size
								UpperCAmelCase_		:      int       	=					FlaxViTForImageClassification(config=_snake_case)
								UpperCAmelCase_		:      str       	=					model(_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size))

								# test greyscale images
								UpperCAmelCase_		:      Tuple       	=					1
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxViTForImageClassification(_snake_case)

								UpperCAmelCase_		:      int       	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								UpperCAmelCase_		:      Optional[Any]       	=					model(_snake_case)





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



@require_flax
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      str=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


							def 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxViTModelTester(self)
								UpperCAmelCase_		:      Any       	=					ConfigTester(self    ,			config_class=_snake_case    ,			has_text_modality=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				str:
								self.config_tester.run_common_tests()


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


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


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      int       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Union[str, Any]       	=					model_class(_snake_case)
									UpperCAmelCase_		:      List[str]       	=					inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_		:      Optional[Any]       	=					[*signature.parameters.keys()]

									UpperCAmelCase_		:      Dict       	=					['pixel_values']
									self.assertListEqual(arg_names[:1]    ,			_snake_case)


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

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      List[Any]       	=					self._prepare_for_class(_snake_case    ,			_snake_case)
										UpperCAmelCase_		:      List[Any]       	=					model_class(_snake_case)

										@jax.jit
										def model_jitted(_snake_case    ,			**_snake_case):
											return model(pixel_values=_snake_case    ,			**_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Dict       	=					model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      Optional[Any]       	=					model_jitted(**_snake_case).to_tuple()

										self.assertEqual(len(_snake_case)    ,			len(_snake_case))
										for jitted_output, output in zip(_snake_case    ,			_snake_case):
											self.assertEqual(jitted_output.shape    ,			output.shape)





							@slow
							def 						_snake_case							(			self)							->				int:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:      Tuple       	=					model_class_name.from_pretrained('google/vit-base-patch16-224')
									UpperCAmelCase_		:      Optional[Any]       	=					model(np.ones((1, 3, 224, 224)))
									self.assertIsNotNone(_snake_case)





'''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"]))






