"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				(PNDMScheduler,)
					snake_case__       					=				(("num_inference_steps", 50),)







					def 	__lowerCAmelCase		(       self     :			List[Any]					,**lowerCamelCase__     :			List[Any]    ):
							UpperCAmelCase__    =							{
							    'num_train_timesteps': 1_000,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							}

							config.update(**lowerCamelCase__    )
							return config







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			List[str]=0					,**lowerCamelCase__     :			str    ):
							UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
							UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
							UpperCAmelCase__    =							self.dummy_sample
							UpperCAmelCase__    =							0.1 * sample
							UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
									UpperCAmelCase__    =							self.get_scheduler_config(**lowerCamelCase__    )
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
									scheduler.set_timesteps(lowerCamelCase__    )
									# copy over dummy past residuals
									UpperCAmelCase__    =							dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase__    )
											UpperCAmelCase__    =							scheduler_class.from_pretrained(lowerCamelCase__    )
											new_scheduler.set_timesteps(lowerCamelCase__    )
											# copy over dummy past residuals
											UpperCAmelCase__    =							dummy_past_residuals[:]

									UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							new_scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

									UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							new_scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			str    ):
							pass







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			int=0					,**lowerCamelCase__     :			List[Any]    ):
							UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
							UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
							UpperCAmelCase__    =							self.dummy_sample
							UpperCAmelCase__    =							0.1 * sample
							UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
									UpperCAmelCase__    =							self.get_scheduler_config()
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
									scheduler.set_timesteps(lowerCamelCase__    )

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

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

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

									UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							new_scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

									UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							new_scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			str					,**lowerCamelCase__     :			Any    ):
							UpperCAmelCase__    =							self.scheduler_classes[0]
							UpperCAmelCase__    =							self.get_scheduler_config(**lowerCamelCase__    )
							UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

							UpperCAmelCase__    =							10
							UpperCAmelCase__    =							self.dummy_model()
							UpperCAmelCase__    =							self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase__    )

							for i, t in enumerate(scheduler.prk_timesteps    ):
									UpperCAmelCase__    =							model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    ).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps    ):
									UpperCAmelCase__    =							model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    ).prev_sample

							return sample







					def 	__lowerCAmelCase		(       self     :			str    ):
							UpperCAmelCase__    =							dict(self.forward_default_kwargs    )

							UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )

							for scheduler_class in self.scheduler_classes:
									UpperCAmelCase__    =							self.get_scheduler_config()
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample

									if num_inference_steps is not None and hasattr(lowerCamelCase__					,'set_timesteps'    ):
											scheduler.set_timesteps(lowerCamelCase__    )
									elif num_inference_steps is not None and not hasattr(lowerCamelCase__					,'set_timesteps'    ):
											UpperCAmelCase__    =							num_inference_steps

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

									UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,0					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,1					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

									UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,0					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
									UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,1					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
							for timesteps in [100, 1_000]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
							for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=lowerCamelCase__    )

							UpperCAmelCase__    =							self.scheduler_classes[0]
							UpperCAmelCase__    =							self.get_scheduler_config(steps_offset=1    )
							UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
							scheduler.set_timesteps(10    )
							assert torch.equal(
							    scheduler.timesteps					,torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]    )					,)







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
							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=lowerCamelCase__					,beta_end=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
							for t in [1, 5, 10]:
									self.check_over_forward(time_step=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
							for t, num_inference_steps in zip([1, 5, 10]					,[10, 50, 100]    ):
									self.check_over_forward(num_inference_steps=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							UpperCAmelCase__    =							27

							for scheduler_class in self.scheduler_classes:
									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample

									UpperCAmelCase__    =							self.get_scheduler_config()
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									scheduler.set_timesteps(lowerCamelCase__    )

									# 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__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    ).prev_sample







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
							with self.assertRaises(lowerCamelCase__    ):
									UpperCAmelCase__    =							self.scheduler_classes[0]
									UpperCAmelCase__    =							self.get_scheduler_config()
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Any    ):
							UpperCAmelCase__    =							self.full_loop()
							UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
							UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

							assert abs(result_sum.item() - 198.1_318    ) < 1e-2
							assert abs(result_mean.item() - 0.2_5_8_0    ) < 1e-3







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
							UpperCAmelCase__    =							self.full_loop(prediction_type='v_prediction'    )
							UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
							UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

							assert abs(result_sum.item() - 67.3_986    ) < 1e-2
							assert abs(result_mean.item() - 0.0_8_7_8    ) < 1e-3







					def 	__lowerCAmelCase		(       self     :			int    ):
							# We specify different beta, so that the first alpha is 0.99
							UpperCAmelCase__    =							self.full_loop(set_alpha_to_one=lowerCamelCase__					,beta_start=0.0_1    )
							UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
							UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

							assert abs(result_sum.item() - 230.0_399    ) < 1e-2
							assert abs(result_mean.item() - 0.2_9_9_5    ) < 1e-3







					def 	__lowerCAmelCase		(       self     :			Dict    ):
							# We specify different beta, so that the first alpha is 0.99
							UpperCAmelCase__    =							self.full_loop(set_alpha_to_one=lowerCamelCase__					,beta_start=0.0_1    )
							UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
							UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

							assert abs(result_sum.item() - 186.9_482    ) < 1e-2
							assert abs(result_mean.item() - 0.2_4_3_4    ) < 1e-3




"""simple docstring"""






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							""
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42

				for keychar, cipherchar in zip(cycle(lowerCamelCase )      ,    lowerCamelCase ):
								UpperCAmelCase__    =							cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(lowerCamelCase )

				return decoded



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for key in product(lowerCamelCase      ,    repeat=3 ):
								UpperCAmelCase__    =							try_key(lowerCamelCase      ,    lowerCamelCase )
								if encoded is not None:
												possibles.append(lowerCamelCase )
				return possibles



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							Path(lowerCamelCase ).parent.joinpath(lowerCamelCase ).read_text(encoding='utf-8' )

				UpperCAmelCase__    =							[int(lowerCamelCase ) for number in data.strip().split(',' )]

				UpperCAmelCase__    =							filter_valid_chars(lowerCamelCase )
				for common_word in COMMON_WORDS:
								UpperCAmelCase__    =							filter_common_word(lowerCamelCase      ,    lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												break

				UpperCAmelCase__    =							possibles[0]
				return sum(ord(lowerCamelCase ) for char in decoded_text )


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



