


'''simple docstring'''







from __future__ import annotations

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

A__:       Any          =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A__:       List[str]          =			[ord(letter) for letter in string.ascii_lowercase]
A__:       Union[str, Any]          =			{ord(char) for char in VALID_CHARS}

A__:       Optional[Any]          =			['''the''', '''be''', '''to''', '''of''', '''and''', '''in''', '''that''', '''have''']


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      list[int]       ,_UpperCAmelCase      :      tuple[int, ...]    )    ->     List[str]:
       _a       :					int	   =""""""
       _a       :					int	   =42
       _a       :					List[str]	   =42
       _a       :					Dict	   =42

       for keychar, cipherchar in zip(cycle(SCREAMING_SNAKE_CASE_    )       ,SCREAMING_SNAKE_CASE_    ):
              _a       :					List[Any]	   =cipherchar ^ keychar
              if decodedchar not in VALID_INTS:
                     return None
              decoded += chr(SCREAMING_SNAKE_CASE_    )

       return decoded


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      list[int]    )    ->     Any:
       _a       :					Any	   =[]
       for key in product(SCREAMING_SNAKE_CASE_       ,repeat=3    ):
              _a       :					Dict	   =try_key(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_    )
              if encoded is not None:
                     possibles.append(SCREAMING_SNAKE_CASE_    )
       return possibles


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      list[str]       ,_UpperCAmelCase      :      str    )    ->     int:
       return [possible for possible in possibles if common_word in possible.lower()]


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      str = "p059_cipher.txt"    )    ->     Optional[Any]:
       _a       :					str	   =42
       _a       :					Any	   =42
       _a       :					Dict	   =42
       _a       :					Union[str, Any]	   =42
       _a       :					List[str]	   =Path(SCREAMING_SNAKE_CASE_    ).parent.joinpath(SCREAMING_SNAKE_CASE_    ).read_text(encoding="""utf-8"""    )

       _a       :					Union[str, Any]	   =[int(SCREAMING_SNAKE_CASE_    ) for number in data.strip().split(""","""    )]

       _a       :					Dict	   =filter_valid_chars(SCREAMING_SNAKE_CASE_    )
       for common_word in COMMON_WORDS:
              _a       :					Tuple	   =filter_common_word(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_    )
              if len(SCREAMING_SNAKE_CASE_    ) == 1:
                     break

       _a       :					int	   =possibles[0]
       return sum(ord(SCREAMING_SNAKE_CASE_    ) for char in decoded_text    )


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









'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       (DPMSolverSinglestepScheduler,)
 __lowerCamelCase    :				int								=       (("num_inference_steps", 25),)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
         "prediction_type": "epsilon",
         "thresholding": False,
         "sample_max_value": 1.0,
         "algorithm_type": "dpmsolver++",
         "solver_type": "midpoint",
         "lambda_min_clipped": -float("inf"		),
         "variance_type": None,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

         _lowerCAmelCase      ,      _lowerCAmelCase						      =   sample, sample
         for t in range(_lowerCAmelCase      ,  time_step + scheduler.config.solver_order + 1		):
             _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
             _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self		)     ->				int:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

         # copy over dummy past residuals (must be after setting timesteps)
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   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)
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
         _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Tuple:
     if scheduler is None:
         _lowerCAmelCase						      =   self.scheduler_classes[0]
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     return sample




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   50
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2574		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3

     _lowerCAmelCase						      =   DEISMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   UniPCMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for order in [1, 2, 3]:
         for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  algorithm_type="dpmsolver++"      ,  solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     _lowerCAmelCase						      =   self.full_loop(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(variance_type=_lowerCAmelCase		)
     self.check_over_configs(variance_type="learned_range"		)




 def 				_snake_case (						self		)     ->				int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=_lowerCAmelCase      ,  time_step=0		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop()
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2248		) < 1E-3




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.1453		) < 1E-3




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"      ,  use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.0649		) < 1E-3





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(thresholding=_lowerCAmelCase      ,  dynamic_thresholding_ratio=0		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     assert sample.dtype == torch.floataa


