






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	(PNDMScheduler,)
					a_			=	(("num_inference_steps", 5_0),)



					def  _lowercase  (      self  :						Dict							,	**__A  :						Tuple ):
						snake_case__				:   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(**__A )
						return config



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Tuple=0							,	**__A  :						Dict ):
						snake_case__				:   Optional[int]     =						dict(self.forward_default_kwargs )
						snake_case__				:   List[Any]     =						kwargs.pop("num_inference_steps"							,	__A )
						snake_case__				:   Optional[int]     =						self.dummy_sample
						snake_case__				:   List[Any]     =						0.1 * sample
						snake_case__				:   Dict     =						[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A )
								snake_case__				:   Tuple     =						scheduler_class.from_pretrained(__A )
								new_scheduler.set_timesteps(__A )
								# copy over dummy past residuals
								snake_case__				:   List[str]     =						dummy_past_residuals[:]

							snake_case__				:   int     =						scheduler.step_prk(__A							,	__A							,	__A							,	**__A ).prev_sample
							snake_case__				:   List[Any]     =						new_scheduler.step_prk(__A							,	__A							,	__A							,	**__A ).prev_sample

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

							snake_case__				:   Tuple     =						scheduler.step_plms(__A							,	__A							,	__A							,	**__A ).prev_sample
							snake_case__				:   int     =						new_scheduler.step_plms(__A							,	__A							,	__A							,	**__A ).prev_sample

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



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



					def  _lowercase  (      self  :						Any							,	__A  :						Dict=0							,	**__A  :						Union[str, Any] ):
						snake_case__				:   Optional[Any]     =						dict(self.forward_default_kwargs )
						snake_case__				:   Optional[int]     =						kwargs.pop("num_inference_steps"							,	__A )
						snake_case__				:   List[Any]     =						self.dummy_sample
						snake_case__				:   List[Any]     =						0.1 * sample
						snake_case__				:   List[str]     =						[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A )
								snake_case__				:   Tuple     =						scheduler_class.from_pretrained(__A )
								# copy over dummy past residuals
								new_scheduler.set_timesteps(__A )

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

							snake_case__				:   List[str]     =						scheduler.step_prk(__A							,	__A							,	__A							,	**__A ).prev_sample
							snake_case__				:   List[str]     =						new_scheduler.step_prk(__A							,	__A							,	__A							,	**__A ).prev_sample

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

							snake_case__				:   Tuple     =						scheduler.step_plms(__A							,	__A							,	__A							,	**__A ).prev_sample
							snake_case__				:   Union[str, Any]     =						new_scheduler.step_plms(__A							,	__A							,	__A							,	**__A ).prev_sample

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



					def  _lowercase  (      self  :						List[str]							,	**__A  :						int ):
						snake_case__				:   Any     =						self.scheduler_classes[0]
						snake_case__				:   List[str]     =						self.get_scheduler_config(**__A )
						snake_case__				:   List[Any]     =						scheduler_class(**__A )

						snake_case__				:   Optional[int]     =						1_0
						snake_case__				:   Dict     =						self.dummy_model()
						snake_case__				:   Any     =						self.dummy_sample_deter
						scheduler.set_timesteps(__A )

						for i, t in enumerate(scheduler.prk_timesteps ):
							snake_case__				:   List[str]     =						model(__A							,	__A )
							snake_case__				:   Union[str, Any]     =						scheduler.step_prk(__A							,	__A							,	__A ).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps ):
							snake_case__				:   List[Any]     =						model(__A							,	__A )
							snake_case__				:   List[Any]     =						scheduler.step_plms(__A							,	__A							,	__A ).prev_sample

						return sample



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Tuple     =						dict(self.forward_default_kwargs )

						snake_case__				:   List[Any]     =						kwargs.pop("num_inference_steps"							,	__A )

						for scheduler_class in self.scheduler_classes:
							snake_case__				:   int     =						self.get_scheduler_config()
							snake_case__				:   Any     =						scheduler_class(**__A )

							snake_case__				:   Optional[Any]     =						self.dummy_sample
							snake_case__				:   Optional[int]     =						0.1 * sample

							if num_inference_steps is not None and hasattr(__A							,	"set_timesteps" ):
								scheduler.set_timesteps(__A )
							elif num_inference_steps is not None and not hasattr(__A							,	"set_timesteps" ):
								snake_case__				:   Tuple     =						num_inference_steps

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

							snake_case__				:   Any     =						scheduler.step_prk(__A							,	0							,	__A							,	**__A ).prev_sample
							snake_case__				:   Dict     =						scheduler.step_prk(__A							,	1							,	__A							,	**__A ).prev_sample

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

							snake_case__				:   Dict     =						scheduler.step_plms(__A							,	0							,	__A							,	**__A ).prev_sample
							snake_case__				:   Optional[Any]     =						scheduler.step_plms(__A							,	1							,	__A							,	**__A ).prev_sample

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



					def  _lowercase  (      self  :						Optional[Any] ):
						for timesteps in [1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=__A )



					def  _lowercase  (      self  :						Any ):
						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__A )

						snake_case__				:   Optional[Any]     =						self.scheduler_classes[0]
						snake_case__				:   Optional[int]     =						self.get_scheduler_config(steps_offset=1 )
						snake_case__				:   str     =						scheduler_class(**__A )
						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  _lowercase  (      self  :						Tuple ):
						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=__A							,	beta_end=__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__A )



					def  _lowercase  (      self  :						Any ):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=__A )



					def  _lowercase  (      self  :						int ):
						for t in [1, 5, 1_0]:
							self.check_over_forward(time_step=__A )



					def  _lowercase  (      self  :						Dict ):
						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=__A )



					def  _lowercase  (      self  :						List[str] ):
						# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
						snake_case__				:   List[Any]     =						2_7

						for scheduler_class in self.scheduler_classes:
							snake_case__				:   List[Any]     =						self.dummy_sample
							snake_case__				:   Union[str, Any]     =						0.1 * sample

							snake_case__				:   Dict     =						self.get_scheduler_config()
							snake_case__				:   Dict     =						scheduler_class(**__A )

							scheduler.set_timesteps(__A )

							# 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] ):
								snake_case__				:   Optional[int]     =						scheduler.step_prk(__A							,	__A							,	__A ).prev_sample



					def  _lowercase  (      self  :						Union[str, Any] ):
						with self.assertRaises(__A ):
							snake_case__				:   Any     =						self.scheduler_classes[0]
							snake_case__				:   Optional[int]     =						self.get_scheduler_config()
							snake_case__				:   List[str]     =						scheduler_class(**__A )

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



					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   int     =						self.full_loop()
						snake_case__				:   Optional[Any]     =						torch.sum(torch.abs(__A ) )
						snake_case__				:   Any     =						torch.mean(torch.abs(__A ) )

						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  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						self.full_loop(prediction_type="v_prediction" )
						snake_case__				:   List[Any]     =						torch.sum(torch.abs(__A ) )
						snake_case__				:   Optional[Any]     =						torch.mean(torch.abs(__A ) )

						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  _lowercase  (      self  :						Dict ):
						# We specify different beta, so that the first alpha is 0.99
						snake_case__				:   List[str]     =						self.full_loop(set_alpha_to_one=__A							,	beta_start=0.0_1 )
						snake_case__				:   Dict     =						torch.sum(torch.abs(__A ) )
						snake_case__				:   str     =						torch.mean(torch.abs(__A ) )

						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  _lowercase  (      self  :						Tuple ):
						# We specify different beta, so that the first alpha is 0.99
						snake_case__				:   Union[str, Any]     =						self.full_loop(set_alpha_to_one=__A							,	beta_start=0.0_1 )
						snake_case__				:   int     =						torch.sum(torch.abs(__A ) )
						snake_case__				:   Dict     =						torch.mean(torch.abs(__A ) )

						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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   str     =						[True] * limit
	snake_case__				:   str     =						False
	snake_case__				:   str     =						False
	snake_case__				:   str     =						True

	for i in range(3  ,						int(limit**0.5 + 1     )  ,						2     ):
		snake_case__				:   Optional[Any]     =						i * 2
		while index < limit:
			snake_case__				:   Union[str, Any]     =						False
			snake_case__				:   Any     =						index + i

	snake_case__				:   Optional[Any]     =						[2]

	for i in range(3  ,						snake_case_  ,						2     ):
		if is_prime[i]:
			primes.append(snake_case_     )

	return primes







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int = 1000000     ):
	snake_case__				:   Optional[int]     =						prime_sieve(snake_case_     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						0

	for i in range(len(snake_case_     )     ):
		for j in range(i + length  ,						len(snake_case_     )     ):
			snake_case__				:   Dict     =						sum(primes[i:j]     )
			if sol >= ceiling:
				break

			if sol in primes:
				snake_case__				:   Tuple     =						j - i
				snake_case__				:   str     =						sol

	return largest


if __name__ == "__main__":
	print(f"{solution() = }")



