


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Any										=						(PNDMScheduler,)
	_lowerCAmelCase     :		int										=						(('''num_inference_steps''', 5_0),)


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							List[str]	    =	{
							    '''num_train_timesteps''': 1_0_0_0,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowercase__)
							return config


	def  A(    self				,     lowercase__=0				,     **lowercase__):
							__UpperCAmelCase			:							Union[str, Any]	    =	dict(self.forward_default_kwargs)
							__UpperCAmelCase			:							Tuple	    =	kwargs.pop('''num_inference_steps'''				,     lowercase__)
							__UpperCAmelCase			:							List[str]	    =	self.dummy_sample
							__UpperCAmelCase			:							Dict	    =	0.1 * sample
							__UpperCAmelCase			:							int	    =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							Dict	    =	self.get_scheduler_config(**lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	scheduler_class(**lowercase__)
													scheduler.set_timesteps(lowercase__)
													# copy over dummy past residuals
													__UpperCAmelCase			:							int	    =	dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__)
																			__UpperCAmelCase			:							Optional[Any]	    =	scheduler_class.from_pretrained(lowercase__)
																			new_scheduler.set_timesteps(lowercase__)
																			# copy over dummy past residuals
																			__UpperCAmelCase			:							Tuple	    =	dummy_past_residuals[:]

													__UpperCAmelCase			:							str	    =	scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Union[str, Any]	    =	new_scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

													assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

													__UpperCAmelCase			:							str	    =	scheduler.step_plms(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Any	    =	new_scheduler.step_plms(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

													assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


	def  A(    self):
							pass


	def  A(    self				,     lowercase__=0				,     **lowercase__):
							__UpperCAmelCase			:							int	    =	dict(self.forward_default_kwargs)
							__UpperCAmelCase			:							List[str]	    =	kwargs.pop('''num_inference_steps'''				,     lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	self.dummy_sample
							__UpperCAmelCase			:							Tuple	    =	0.1 * sample
							__UpperCAmelCase			:							int	    =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							Union[str, Any]	    =	self.get_scheduler_config()
													__UpperCAmelCase			:							Any	    =	scheduler_class(**lowercase__)
													scheduler.set_timesteps(lowercase__)

													# copy over dummy past residuals (must be after setting timesteps)
													__UpperCAmelCase			:							Any	    =	dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__)
																			__UpperCAmelCase			:							int	    =	scheduler_class.from_pretrained(lowercase__)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(lowercase__)

																			# copy over dummy past residual (must be after setting timesteps)
																			__UpperCAmelCase			:							Any	    =	dummy_past_residuals[:]

													__UpperCAmelCase			:							int	    =	scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Any	    =	new_scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

													assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

													__UpperCAmelCase			:							Optional[Any]	    =	scheduler.step_plms(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Tuple	    =	new_scheduler.step_plms(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

													assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							str	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							Optional[Any]	    =	self.get_scheduler_config(**lowercase__)
							__UpperCAmelCase			:							int	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	1_0
							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_model()
							__UpperCAmelCase			:							Tuple	    =	self.dummy_sample_deter
							scheduler.set_timesteps(lowercase__)

							for i, t in enumerate(scheduler.prk_timesteps):
													__UpperCAmelCase			:							str	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							List[str]	    =	scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps):
													__UpperCAmelCase			:							Optional[Any]	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	scheduler.step_plms(lowercase__				,     lowercase__				,     lowercase__).prev_sample

							return sample


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	dict(self.forward_default_kwargs)

							__UpperCAmelCase			:							int	    =	kwargs.pop('''num_inference_steps'''				,     lowercase__)

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							List[str]	    =	self.get_scheduler_config()
													__UpperCAmelCase			:							int	    =	scheduler_class(**lowercase__)

													__UpperCAmelCase			:							Dict	    =	self.dummy_sample
													__UpperCAmelCase			:							str	    =	0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase__				,     '''set_timesteps'''):
																			scheduler.set_timesteps(lowercase__)
													elif num_inference_steps is not None and not hasattr(lowercase__				,     '''set_timesteps'''):
																			__UpperCAmelCase			:							Optional[int]	    =	num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__UpperCAmelCase			:							int	    =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													__UpperCAmelCase			:							int	    =	dummy_past_residuals[:]

													__UpperCAmelCase			:							List[str]	    =	scheduler.step_prk(lowercase__				,     0				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Optional[Any]	    =	scheduler.step_prk(lowercase__				,     1				,     lowercase__				,     **lowercase__).prev_sample

													self.assertEqual(output_a.shape				,     sample.shape)
													self.assertEqual(output_a.shape				,     output_a.shape)

													__UpperCAmelCase			:							List[str]	    =	scheduler.step_plms(lowercase__				,     0				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							Dict	    =	scheduler.step_plms(lowercase__				,     1				,     lowercase__				,     **lowercase__).prev_sample

													self.assertEqual(output_a.shape				,     sample.shape)
													self.assertEqual(output_a.shape				,     output_a.shape)


	def  A(    self):
							for timesteps in [1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowercase__)


	def  A(    self):
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowercase__)

							__UpperCAmelCase			:							Any	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							int	    =	self.get_scheduler_config(steps_offset=1)
							__UpperCAmelCase			:							Optional[int]	    =	scheduler_class(**lowercase__)
							scheduler.set_timesteps(1_0)
							assert torch.equal(
							    scheduler.timesteps				,     torch.LongTensor(
							        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1])				,     )


	def  A(    self):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]				,     [0.0_0_2, 0.0_2]):
													self.check_over_configs(beta_start=lowercase__				,     beta_end=lowercase__)


	def  A(    self):
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowercase__)


	def  A(    self):
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase__)


	def  A(    self):
							for t in [1, 5, 1_0]:
													self.check_over_forward(time_step=lowercase__)


	def  A(    self):
							for t, num_inference_steps in zip([1, 5, 1_0]				,     [1_0, 5_0, 1_0_0]):
													self.check_over_forward(num_inference_steps=lowercase__)


	def  A(    self):
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							__UpperCAmelCase			:							Optional[int]	    =	2_7

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							Tuple	    =	self.dummy_sample
													__UpperCAmelCase			:							Any	    =	0.1 * sample

													__UpperCAmelCase			:							Any	    =	self.get_scheduler_config()
													__UpperCAmelCase			:							Union[str, Any]	    =	scheduler_class(**lowercase__)

													scheduler.set_timesteps(lowercase__)

													# before power of 3 fix, would error on first step, so we only need to do two
													for i, t in enumerate(scheduler.prk_timesteps[:2]):
																			__UpperCAmelCase			:							Union[str, Any]	    =	scheduler.step_prk(lowercase__				,     lowercase__				,     lowercase__).prev_sample


	def  A(    self):
							with self.assertRaises(lowercase__):
													__UpperCAmelCase			:							Dict	    =	self.scheduler_classes[0]
													__UpperCAmelCase			:							int	    =	self.get_scheduler_config()
													__UpperCAmelCase			:							int	    =	scheduler_class(**lowercase__)

													scheduler.step_plms(self.dummy_sample				,     1				,     self.dummy_sample).prev_sample


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.full_loop()
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							str	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_9_8.1_3_1_8) < 1e-2
							assert abs(result_mean.item() - 0.2_5_8_0) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	self.full_loop(prediction_type='''v_prediction''')
							__UpperCAmelCase			:							Dict	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							int	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 6_7.3_9_8_6) < 1e-2
							assert abs(result_mean.item() - 0.0_8_7_8) < 1e-3


	def  A(    self):
							# We specify different beta, so that the first alpha is 0.99
							__UpperCAmelCase			:							Dict	    =	self.full_loop(set_alpha_to_one=lowercase__				,     beta_start=0.0_1)
							__UpperCAmelCase			:							int	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							str	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 2_3_0.0_3_9_9) < 1e-2
							assert abs(result_mean.item() - 0.2_9_9_5) < 1e-3


	def  A(    self):
							# We specify different beta, so that the first alpha is 0.99
							__UpperCAmelCase			:							Any	    =	self.full_loop(set_alpha_to_one=lowercase__				,     beta_start=0.0_1)
							__UpperCAmelCase			:							Tuple	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_8_6.9_4_8_2) < 1e-2
							assert abs(result_mean.item() - 0.2_4_3_4) < 1e-3





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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							Optional[int]	    =	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

						__UpperCAmelCase			:							List[str]	    =	response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(lowercase_      )}

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							str	    =	{
												    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"""]))

