






'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
			from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				int=3			,							UpperCamelCase__:				Union[str, Any]=32			,							UpperCamelCase__:				Any=3			,							UpperCamelCase__:				Optional[int]=10			,							UpperCamelCase__:				List[str]=[10, 20, 30, 40]			,							UpperCamelCase__:				Tuple=[1, 1, 2, 1]			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				str="relu"			,							UpperCamelCase__:				Any=3			,							UpperCamelCase__:				Optional[int]=None			,							):
														lowerCamelCase__					:	List[str]         =       parent
														lowerCamelCase__					:	str         =       batch_size
														lowerCamelCase__					:	str         =       image_size
														lowerCamelCase__					:	List[str]         =       num_channels
														lowerCamelCase__					:	List[str]         =       embeddings_size
														lowerCamelCase__					:	Dict         =       hidden_sizes
														lowerCamelCase__					:	Optional[Any]         =       depths
														lowerCamelCase__					:	Dict         =       is_training
														lowerCamelCase__					:	str         =       use_labels
														lowerCamelCase__					:	Any         =       hidden_act
														lowerCamelCase__					:	str         =       num_labels
														lowerCamelCase__					:	List[str]         =       scope
														lowerCamelCase__					:	Optional[int]         =       len(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Dict         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCamelCase__					:	str         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Any     ):
														return ResNetConfig(
														    num_channels=self.num_channels			,							embeddings_size=self.embeddings_size			,							hidden_sizes=self.hidden_sizes			,							depths=self.depths			,							hidden_act=self.hidden_act			,							num_labels=self.num_labels			,							image_size=self.image_size			,							)




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       TFResNetModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__     )
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,							)




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.num_labels
														lowerCamelCase__					:	Dict         =       TFResNetForImageClassification(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	Union[str, Any]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
							a				      =       (
							    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       TFResNetModelTester(self     )
														lowerCamelCase__					:	Any         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()




							def 		lowerCamelCase_				(							self:				int     ):
														return




							@unittest.skip(reason="""ResNet does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@unittest.skip(reason="""ResNet does not support input and output embeddings"""     )
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														def check_hidden_states_output(UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				int     ):
																					lowerCamelCase__					:	List[str]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Tuple         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

																					lowerCamelCase__					:	Tuple         =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					lowerCamelCase__					:	Optional[int]         =       self.model_tester.num_stages
																					self.assertEqual(len(UpperCamelCase__     )			,							expected_num_stages + 1     )

																					# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]     )			,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,							)

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__					:	Tuple         =       ["""basic""", """bottleneck"""]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												lowerCamelCase__					:	Tuple         =       layer_type
																												lowerCamelCase__					:	Optional[Any]         =       True
																												check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

																												# check that output_hidden_states also work using config
																												del inputs_dict["output_hidden_states"]
																												lowerCamelCase__					:	Union[str, Any]         =       True

																												check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	str         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	List[str]         =       TFResNetModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	List[str]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




							@cached_property
							def 		lowerCamelCase_				(							self:				int     ):
														return (
														    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
														    if is_vision_available()
														    else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

														lowerCamelCase__					:	Union[str, Any]         =       self.default_image_processor
														lowerCamelCase__					:	Optional[Any]         =       prepare_img()
														lowerCamelCase__					:	Optional[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# forward pass
														lowerCamelCase__					:	Optional[int]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	List[Any]         =       tf.TensorShape((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       tf.constant([-11.1_069, -9.7_877, -8.3_777]     )

														self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,							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''']))

