



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class 	lowerCamelCase__      (							_A):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =42
		snake_case_     =None



class 	lowerCamelCase__      (							_A				,    _A):




		'''simple docstring'''



		snake_case_     =2



		@register_to_config
		def __init__(self		,__lowerCamelCase = 0.02		,__lowerCamelCase = 1_00		,__lowerCamelCase = 1.007		,__lowerCamelCase = 80		,__lowerCamelCase = 0.05		,__lowerCamelCase = 50		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					sigma_max

						# setable values
						lowerCAmelCase__					:    str												=					None
						lowerCAmelCase__					:    List[Any]												=					None
						lowerCAmelCase__					:    List[Any]												=					None  # sigma(t_i)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      str:

						"""simple docstring"""






						return sample



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					num_inference_steps
						lowerCAmelCase__					:    int												=					np.arange(0		,self.num_inference_steps			)[::-1].copy()
						lowerCAmelCase__					:    List[Any]												=					torch.from_numpy(__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			)
						lowerCAmelCase__					:    Union[str, Any]												=					[
						    (
						        self.config.sigma_max**2
						        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
						    )
						    for i in self.timesteps
						]
						lowerCAmelCase__					:    Any												=					torch.tensor(__SCREAMING_SNAKE_CASE		,dtype=torch.floataa		,device=__SCREAMING_SNAKE_CASE			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = None			)					->      Dict:

						"""simple docstring"""






						if self.config.s_min <= sigma <= self.config.s_max:
										lowerCAmelCase__					:    int												=					min(self.config.s_churn / self.num_inference_steps		,2**0.5 - 1			)
						else:
										lowerCAmelCase__					:    List[str]												=					0

						# sample eps ~ N(0, S_noise^2 * I)
						lowerCAmelCase__					:    Dict												=					self.config.s_noise * randn_tensor(sample.shape		,generator=__SCREAMING_SNAKE_CASE			).to(sample.device			)
						lowerCAmelCase__					:    str												=					sigma + gamma * sigma
						lowerCAmelCase__					:    Optional[int]												=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = True		,)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					sample_hat + sigma_hat * model_output
						lowerCAmelCase__					:    Tuple												=					(sample_hat - pred_original_sample) / sigma_hat
						lowerCAmelCase__					:    Optional[int]												=					sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
										return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=__SCREAMING_SNAKE_CASE		,derivative=__SCREAMING_SNAKE_CASE		,pred_original_sample=__SCREAMING_SNAKE_CASE			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = True		,)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					sample_prev + sigma_prev * model_output
						lowerCAmelCase__					:    Optional[int]												=					(sample_prev - pred_original_sample) / sigma_prev
						lowerCAmelCase__					:    Optional[int]												=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

						if not return_dict:
										return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=__SCREAMING_SNAKE_CASE		,derivative=__SCREAMING_SNAKE_CASE		,pred_original_sample=__SCREAMING_SNAKE_CASE			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						raise NotImplementedError()






import string

import numpy






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		int):





				'''simple docstring'''

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



class 	lowerCamelCase__      :




		'''simple docstring'''



		snake_case_     =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)
		snake_case_     =numpy.vectorize(lambda lowerCamelCase__: x % 36)

		snake_case_     =numpy.vectorize(lowerCamelCase__)



		def __init__(self		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.modulus(__lowerCamelCase			)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						lowerCAmelCase__					:    Optional[int]												=					encrypt_key.shape[0]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						return self.key_string.index(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						return self.key_string[round(__lowerCamelCase			)]



		def 		lowerCAmelCase__				(self			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

						if det < 0:
										lowerCAmelCase__					:    str												=					det % len(self.key_string			)

						lowerCAmelCase__					:    Optional[Any]												=					len(self.key_string			)
						if greatest_common_divisor(__lowerCamelCase		,len(self.key_string			)			) != 1:
										lowerCAmelCase__					:    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(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					[char for char in text.upper() if char in self.key_string]

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

						return "".join(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    str												=					''''''

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

						return encrypted



		def 		lowerCAmelCase__				(self			)					->      numpy.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

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

						lowerCAmelCase__					:    Optional[Any]												=					(
						    det_inv
						    * numpy.linalg.det(self.encrypt_key			)
						    * numpy.linalg.inv(self.encrypt_key			)
						)

						return self.to_int(self.modulus(__lowerCamelCase			)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.make_decrypt_key()
						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    Optional[Any]												=					''''''

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

						return decrypted






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					int(input('''Enter the order of the encryption key: '''))
				lowerCAmelCase__					:    Union[str, Any]												=					[]

				print('''Enter each row of the encryption key with space separated integers''')
				for _ in range(lowerCamelCase_):
								lowerCAmelCase__					:    int												=					[int(lowerCamelCase_) for x in input().split()]
								hill_matrix.append(lowerCamelCase_)

				lowerCAmelCase__					:    List[str]												=					HillCipher(numpy.array(lowerCamelCase_))

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()

