"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 	UpperCamelCase    (					lowercase		):
    UpperCAmelCase       :   Union[str, Any]					=	(UniPCMultistepScheduler,)
    UpperCAmelCase       :   Any					=	(("""num_inference_steps""", 25),)



    def       _lowercase  (self       :   Any			,						**_A       :   Optional[int])					->			int:
     __snake_case    :							int   	=				{
         'num_train_timesteps': 10_00,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'solver_order': 2,
         'solver_type': 'bh2',
     }

     config.update(**_A)
     return config



    def       _lowercase  (self       :   Optional[Any]			,						_A       :   Optional[Any]=0			,						**_A       :   Optional[int])					->			List[str]:
     __snake_case    :							Optional[Any]   	=				dict(self.forward_default_kwargs)
     __snake_case    :							int   	=				kwargs.pop('num_inference_steps'			,						_A)
     __snake_case    :							str   	=				self.dummy_sample
     __snake_case    :							int   	=				0.1 * sample
     __snake_case    :							List[str]   	=				[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
      __snake_case    :							Optional[int]   	=				self.get_scheduler_config(**_A)
      __snake_case    :							Union[str, Any]   	=				scheduler_class(**_A)
      scheduler.set_timesteps(_A)
      # copy over dummy past residuals
      __snake_case    :							Any   	=				dummy_past_residuals[: scheduler.config.solver_order]

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

      __snake_case    :							str   	=				sample, sample
      for t in range(_A			,						time_step + scheduler.config.solver_order + 1):
       __snake_case    :							int   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
       __snake_case    :							List[str]   	=				new_scheduler.step(_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[Any]			,						_A       :   Any=0			,						**_A       :   Any)					->			List[str]:
     __snake_case    :							List[str]   	=				dict(self.forward_default_kwargs)
     __snake_case    :							Union[str, Any]   	=				kwargs.pop('num_inference_steps'			,						_A)
     __snake_case    :							List[str]   	=				self.dummy_sample
     __snake_case    :							Optional[int]   	=				0.1 * sample
     __snake_case    :							Optional[Any]   	=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

      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    :							int   	=				dummy_past_residuals[: new_scheduler.config.solver_order]

      __snake_case    :							Any   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
      __snake_case    :							Tuple   	=				new_scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample

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



    def       _lowercase  (self       :   Optional[Any]			,						_A       :   str=None			,						**_A       :   Any)					->			Union[str, Any]:
     if scheduler is None:
      __snake_case    :							Tuple   	=				self.scheduler_classes[0]
      __snake_case    :							Tuple   	=				self.get_scheduler_config(**_A)
      __snake_case    :							str   	=				scheduler_class(**_A)

     __snake_case    :							Dict   	=				self.scheduler_classes[0]
     __snake_case    :							Optional[Any]   	=				self.get_scheduler_config(**_A)
     __snake_case    :							List[str]   	=				scheduler_class(**_A)

     __snake_case    :							Any   	=				10
     __snake_case    :							int   	=				self.dummy_model()
     __snake_case    :							int   	=				self.dummy_sample_deter
     scheduler.set_timesteps(_A)

     for i, t in enumerate(scheduler.timesteps):
      __snake_case    :							Dict   	=				model(_A			,						_A)
      __snake_case    :							Optional[Any]   	=				scheduler.step(_A			,						_A			,						_A).prev_sample

     return sample



    def       _lowercase  (self       :   Optional[Any])					->			Tuple:
     __snake_case    :							Dict   	=				dict(self.forward_default_kwargs)

     __snake_case    :							Any   	=				kwargs.pop('num_inference_steps'			,						_A)

     for scheduler_class in self.scheduler_classes:
      __snake_case    :							Any   	=				self.get_scheduler_config()
      __snake_case    :							str   	=				scheduler_class(**_A)

      __snake_case    :							Union[str, Any]   	=				self.dummy_sample
      __snake_case    :							str   	=				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    :							str   	=				num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      __snake_case    :							str   	=				[residual + 0.2, residual + 0.15, residual + 0.10]
      __snake_case    :							Optional[int]   	=				dummy_past_residuals[: scheduler.config.solver_order]

      __snake_case    :							Dict   	=				scheduler.timesteps[5]
      __snake_case    :							Tuple   	=				scheduler.timesteps[6]

      __snake_case    :							str   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
      __snake_case    :							Any   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample

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



    def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     __snake_case    :							Optional[Any]   	=				UniPCMultistepScheduler(**self.get_scheduler_config())
     __snake_case    :							int   	=				self.full_loop(scheduler=_A)
     __snake_case    :							Union[str, Any]   	=				torch.mean(torch.abs(_A))

     assert abs(result_mean.item() - 0.2_464) < 1E-3

     __snake_case    :							Union[str, Any]   	=				DPMSolverSinglestepScheduler.from_config(scheduler.config)
     __snake_case    :							List[Any]   	=				DEISMultistepScheduler.from_config(scheduler.config)
     __snake_case    :							str   	=				DPMSolverMultistepScheduler.from_config(scheduler.config)
     __snake_case    :							Optional[int]   	=				UniPCMultistepScheduler.from_config(scheduler.config)

     __snake_case    :							Optional[int]   	=				self.full_loop(scheduler=_A)
     __snake_case    :							Optional[Any]   	=				torch.mean(torch.abs(_A))

     assert abs(result_mean.item() - 0.2_464) < 1E-3



    def       _lowercase  (self       :   Optional[Any])					->			Dict:
     for timesteps in [25, 50, 1_00, 9_99, 10_00]:
      self.check_over_configs(num_train_timesteps=_A)



    def       _lowercase  (self       :   Any)					->			List[str]:
     self.check_over_configs(thresholding=_A)
     for order in [1, 2, 3]:
      for solver_type in ["bh1", "bh2"]:
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(
             thresholding=_A			,						prediction_type=_A			,						sample_max_value=_A			,						solver_order=_A			,						solver_type=_A			,						)



    def       _lowercase  (self       :   Optional[Any])					->			str:
     for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=_A)



    def       _lowercase  (self       :   List[str])					->			int:
     for solver_type in ["bh1", "bh2"]:
      for order in [1, 2, 3]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            solver_order=_A			,						solver_type=_A			,						prediction_type=_A			,						)
        __snake_case    :							Optional[int]   	=				self.full_loop(
            solver_order=_A			,						solver_type=_A			,						prediction_type=_A			,						)
        assert not torch.isnan(_A).any(), "Samples have nan numbers"



    def       _lowercase  (self       :   str)					->			Union[str, Any]:
     self.check_over_configs(lower_order_final=_A)
     self.check_over_configs(lower_order_final=_A)



    def       _lowercase  (self       :   Optional[Any])					->			List[Any]:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
      self.check_over_forward(num_inference_steps=_A			,						time_step=0)



    def       _lowercase  (self       :   Tuple)					->			Any:
     __snake_case    :							List[Any]   	=				self.full_loop()
     __snake_case    :							Union[str, Any]   	=				torch.mean(torch.abs(_A))

     assert abs(result_mean.item() - 0.2_464) < 1E-3



    def       _lowercase  (self       :   str)					->			Optional[Any]:
     __snake_case    :							Optional[Any]   	=				self.full_loop(prediction_type='v_prediction')
     __snake_case    :							int   	=				torch.mean(torch.abs(_A))

     assert abs(result_mean.item() - 0.1_014) < 1E-3



    def       _lowercase  (self       :   str)					->			Optional[Any]:
     __snake_case    :							List[str]   	=				self.scheduler_classes[0]
     __snake_case    :							List[str]   	=				self.get_scheduler_config(thresholding=_A			,						dynamic_thresholding_ratio=0)
     __snake_case    :							List[Any]   	=				scheduler_class(**_A)

     __snake_case    :							int   	=				10
     __snake_case    :							str   	=				self.dummy_model()
     __snake_case    :							str   	=				self.dummy_sample_deter.half()
     scheduler.set_timesteps(_A)

     for i, t in enumerate(scheduler.timesteps):
      __snake_case    :							List[str]   	=				model(_A			,						_A)
      __snake_case    :							int   	=				scheduler.step(_A			,						_A			,						_A).prev_sample

     assert sample.dtype == torch.floataa






    def       _lowercase  (self       :   List[Any]			,						**_A       :   Union[str, Any])					->			Dict:
     for scheduler_class in self.scheduler_classes:
      __snake_case    :							str   	=				self.get_scheduler_config(**_A)
      __snake_case    :							Optional[Any]   	=				scheduler_class(**_A)

      scheduler.set_timesteps(scheduler.config.num_train_timesteps)
      assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps






"""simple docstring"""







import string

import numpy
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	return b if a == 0 else greatest_common_divisor(b % a    ,							UpperCAmelCase_						)

class 	UpperCamelCase    :
				UpperCAmelCase       :   Any					=	string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				UpperCAmelCase       :   List[Any]					=	numpy.vectorize(lambda lowercase		: x % 36		)

				UpperCAmelCase       :   Dict					=	numpy.vectorize(lowercase		)



				def __init__(self       :   str			,						_A       :   numpy.ndarray)					->			None:
					__snake_case    :							str   	=				self.modulus(_A)  # mod36 calc's on the encrypt key
					self.check_determinant()  # validate the determinant of the encryption key
					__snake_case    :							Optional[Any]   	=				encrypt_key.shape[0]



				def       _lowercase  (self       :   Any			,						_A       :   str)					->			int:
					return self.key_string.index(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   int)					->			str:
					return self.key_string[round(_A)]



				def       _lowercase  (self       :   Optional[int])					->			None:
					__snake_case    :							Any   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							Any   	=				det % len(self.key_string)

					__snake_case    :							Tuple   	=				len(self.key_string)
					if greatest_common_divisor(_A			,						len(self.key_string)) != 1:
						__snake_case    :							List[str]   	=				(
						    f"determinant modular {req_l} of encryption key({det}) "
						    f"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(_A)



				def       _lowercase  (self       :   Dict			,						_A       :   str)					->			str:
					__snake_case    :							str   	=				[char for char in text.upper() if char in self.key_string]

					__snake_case    :							int   	=				chars[-1]
					while len(_A) % self.break_key != 0:
						chars.append(_A)

					return "".join(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			str:
					__snake_case    :							Any   	=				self.process_text(text.upper())
					__snake_case    :							Dict   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Dict   	=				text[i : i + self.break_key]
						__snake_case    :							List[str]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							str   	=				numpy.array([vec]).T
						__snake_case    :							List[Any]   	=				self.modulus(self.encrypt_key.dot(_A)).T.tolist()[
						    0
						]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_encrypted)
						encrypted += encrypted_batch

					return encrypted



				def       _lowercase  (self       :   Optional[int])					->			numpy.ndarray:
					__snake_case    :							List[Any]   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							int   	=				det % len(self.key_string)
					__snake_case    :							Optional[Any]   	=				None
					for i in range(len(self.key_string)):
						if (det * i) % len(self.key_string) == 1:
							__snake_case    :							Dict   	=				i
							break

					__snake_case    :							List[str]   	=				(
					    det_inv
					    * numpy.linalg.det(self.encrypt_key)
					    * numpy.linalg.inv(self.encrypt_key)
					)

					return self.to_int(self.modulus(_A))






				def       _lowercase  (self       :   int			,						_A       :   str)					->			str:
					__snake_case    :							int   	=				self.make_decrypt_key()
					__snake_case    :							List[str]   	=				self.process_text(text.upper())
					__snake_case    :							str   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Optional[Any]   	=				text[i : i + self.break_key]
						__snake_case    :							Union[str, Any]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							Tuple   	=				numpy.array([vec]).T
						__snake_case    :							List[str]   	=				self.modulus(decrypt_key.dot(_A)).T.tolist()[0]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_decrypted)
						decrypted += decrypted_batch

					return decrypted







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				int(input('Enter the order of the encryption key: '						)						)
	__snake_case    :							str   	=				[]

	print('Enter each row of the encryption key with space separated integers'						)
	for _ in range(UpperCAmelCase_						):
		__snake_case    :							Union[str, Any]   	=				[int(UpperCAmelCase_						) for x in input().split()]
		hill_matrix.append(UpperCAmelCase_						)

	__snake_case    :							Dict   	=				HillCipher(numpy.array(UpperCAmelCase_						)						)

	print('Would you like to encrypt or decrypt some text? (1 or 2)'						)
	__snake_case    :							Optional[Any]   	=				input('\n1. Encrypt\n2. Decrypt\n'						)
	if option == "1":
		__snake_case    :							int   	=				input('What text would you like to encrypt?: '						)
		print('Your encrypted text is:'						)
		print(hc.encrypt(UpperCAmelCase_						)						)
	elif option == "2":
		__snake_case    :							Tuple   	=				input('What text would you like to decrypt?: '						)
		print('Your decrypted text is:'						)
		print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	main()





