






'''simple docstring'''







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase	(				_lowercase		):
							a				      =       (DDPMParallelScheduler,)




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							**UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       {
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														    """variance_type""": """fixed_small""",
														    """clip_sample""": True,
														}

														config.update(**UpperCamelCase__     )
														return config




							def 		lowerCamelCase_				(							self:				Tuple     ):
														for timesteps in [1, 5, 100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,							[0.002, 0.02, 0.2, 2]     ):
																					self.check_over_configs(beta_start=UpperCamelCase__			,							beta_end=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														self.check_over_configs(thresholding=UpperCamelCase__     )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=UpperCamelCase__			,							prediction_type=UpperCamelCase__			,							sample_max_value=UpperCamelCase__			,							)




							def 		lowerCamelCase_				(							self:				str     ):
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														for t in [0, 500, 999]:
																					self.check_over_forward(time_step=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	int         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Union[str, Any]         =       self.get_scheduler_config()
														lowerCamelCase__					:	Union[str, Any]         =       scheduler_class(**UpperCamelCase__     )

														assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.00_979     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1e-5




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	int         =       self.scheduler_classes[0]
														lowerCamelCase__					:	List[Any]         =       self.get_scheduler_config()
														lowerCamelCase__					:	List[str]         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	str         =       len(UpperCamelCase__     )

														lowerCamelCase__					:	str         =       self.dummy_model()
														lowerCamelCase__					:	int         =       self.dummy_sample_deter
														lowerCamelCase__					:	Optional[int]         =       self.dummy_sample_deter + 0.1
														lowerCamelCase__					:	Optional[int]         =       self.dummy_sample_deter - 0.1

														lowerCamelCase__					:	Union[str, Any]         =       samplea.shape[0]
														lowerCamelCase__					:	Union[str, Any]         =       torch.stack([samplea, samplea, samplea]			,							dim=0     )
														lowerCamelCase__					:	str         =       torch.arange(UpperCamelCase__     )[0:3, None].repeat(1			,							UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       model(samples.flatten(0			,							1     )			,							timesteps.flatten(0			,							1     )     )
														lowerCamelCase__					:	Dict         =       scheduler.batch_step_no_noise(UpperCamelCase__			,							timesteps.flatten(0			,							1     )			,							samples.flatten(0			,							1     )     )

														lowerCamelCase__					:	Union[str, Any]         =       torch.sum(torch.abs(UpperCamelCase__     )     )
														lowerCamelCase__					:	List[str]         =       torch.mean(torch.abs(UpperCamelCase__     )     )

														assert abs(result_sum.item() - 1_153.1_833     ) < 1e-2
														assert abs(result_mean.item() - 0.5_005     ) < 1e-3




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[Any]         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Dict         =       self.get_scheduler_config()
														lowerCamelCase__					:	List[Any]         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       len(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       self.dummy_model()
														lowerCamelCase__					:	int         =       self.dummy_sample_deter
														lowerCamelCase__					:	Optional[int]         =       torch.manual_seed(0     )

														for t in reversed(range(UpperCamelCase__     )     ):
																					# 1. predict noise residual
																					lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							UpperCamelCase__     )

																					# 2. predict previous mean of sample x_t-1
																					lowerCamelCase__					:	Any         =       scheduler.step(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							generator=UpperCamelCase__     ).prev_sample

																					lowerCamelCase__					:	List[str]         =       pred_prev_sample

														lowerCamelCase__					:	List[Any]         =       torch.sum(torch.abs(UpperCamelCase__     )     )
														lowerCamelCase__					:	Optional[int]         =       torch.mean(torch.abs(UpperCamelCase__     )     )

														assert abs(result_sum.item() - 258.9_606     ) < 1e-2
														assert abs(result_mean.item() - 0.3_372     ) < 1e-3




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Any         =       self.get_scheduler_config(prediction_type="""v_prediction"""     )
														lowerCamelCase__					:	Any         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	int         =       len(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       self.dummy_model()
														lowerCamelCase__					:	List[Any]         =       self.dummy_sample_deter
														lowerCamelCase__					:	List[str]         =       torch.manual_seed(0     )

														for t in reversed(range(UpperCamelCase__     )     ):
																					# 1. predict noise residual
																					lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase__			,							UpperCamelCase__     )

																					# 2. predict previous mean of sample x_t-1
																					lowerCamelCase__					:	List[str]         =       scheduler.step(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							generator=UpperCamelCase__     ).prev_sample

																					lowerCamelCase__					:	List[Any]         =       pred_prev_sample

														lowerCamelCase__					:	Union[str, Any]         =       torch.sum(torch.abs(UpperCamelCase__     )     )
														lowerCamelCase__					:	List[str]         =       torch.mean(torch.abs(UpperCamelCase__     )     )

														assert abs(result_sum.item() - 202.0_296     ) < 1e-2
														assert abs(result_mean.item() - 0.2_631     ) < 1e-3




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Dict         =       self.scheduler_classes[0]
														lowerCamelCase__					:	List[str]         =       self.get_scheduler_config()
														lowerCamelCase__					:	Optional[int]         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       [100, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       scheduler.timesteps

														for i, timestep in enumerate(UpperCamelCase__     ):
																					if i == len(UpperCamelCase__     ) - 1:
																												lowerCamelCase__					:	List[str]         =       -1
																					else:
																												lowerCamelCase__					:	int         =       timesteps[i + 1]

																					lowerCamelCase__					:	List[Any]         =       scheduler.previous_timestep(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       prev_t.item()

																					self.assertEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Union[str, Any]         =       self.get_scheduler_config()
														lowerCamelCase__					:	Any         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       [100, 87, 50, 51, 0]

														with self.assertRaises(UpperCamelCase__			,							msg="""`custom_timesteps` must be in descending order."""     ):
																					scheduler.set_timesteps(timesteps=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Tuple         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Dict         =       self.get_scheduler_config()
														lowerCamelCase__					:	str         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [100, 87, 50, 1, 0]
														lowerCamelCase__					:	List[str]         =       len(UpperCamelCase__     )

														with self.assertRaises(UpperCamelCase__			,							msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""     ):
																					scheduler.set_timesteps(num_inference_steps=UpperCamelCase__			,							timesteps=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.scheduler_classes[0]
														lowerCamelCase__					:	Tuple         =       self.get_scheduler_config()
														lowerCamelCase__					:	List[Any]         =       scheduler_class(**UpperCamelCase__     )

														lowerCamelCase__					:	Union[str, Any]         =       [scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    UpperCamelCase__			,							msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,							):
																					scheduler.set_timesteps(timesteps=UpperCamelCase__     )









'''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''']))

