


import string

import numpy



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    return b if a == 0 else greatest_common_divisor(b % a				,					lowerCamelCase_					)






class _lowerCamelCase:
       lowercase_					:      List[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)
       lowercase_					:      Union[str, Any]       					=     numpy.vectorize(lambda _a  : x % 36  )

       lowercase_					:      Optional[Any]       					=     numpy.vectorize(_a  )
       def __init__(   self, lowerCamelCase)			->  None:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.modulus(lowerCamelCase)  # mod36 calc's on the encrypt key
           self.check_determinant()  # validate the determinant of the encryption key
           _lowercase			:				Optional[Any] 						=						encrypt_key.shape[0]
       def      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

           return self.key_string.index(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           return self.key_string[round(lowerCamelCase)]
       def      UpperCamelCase	(   self)			->  None:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
               _lowercase			:				Dict 						=						det % len(self.key_string)

           _lowercase			:				str 						=						len(self.key_string)
           if greatest_common_divisor(lowerCamelCase, len(self.key_string)) != 1:
               _lowercase			:				Any 						=						(
                   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      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						[char for char in text.upper() if char in self.key_string]

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

           return "".join(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Dict 						=						self.process_text(text.upper())
           _lowercase			:				List[Any] 						=						''

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

           return encrypted
       def      UpperCamelCase	(   self)			->  numpy.ndarray:




           """simple docstring"""

           _lowercase			:				int 						=						round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
               _lowercase			:				Dict 						=						det % len(self.key_string)
           _lowercase			:				Dict 						=						None
           for i in range(len(self.key_string)):
               if (det * i) % len(self.key_string) == 1:
                   _lowercase			:				Dict 						=						i
                   break

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

           return self.to_int(self.modulus(lowerCamelCase))




       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Any 						=						self.make_decrypt_key()
           _lowercase			:				Optional[Any] 						=						self.process_text(text.upper())
           _lowercase			:				Optional[Any] 						=						''

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

           return decrypted



def 							UpperCamelCase_( )       ->		None:
    _lowercase			:				str 						=						int(input('Enter the order of the encryption key: '					)					)
    _lowercase			:				List[Any] 						=						[]

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

    _lowercase			:				str 						=						HillCipher(numpy.array(lowerCamelCase_					)					)

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









import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

		lowercase__   ,				lowercase__					:      Any      =		imgs[0].size
		lowercase__					:      int      =		Image.new('RGB'   ,    size=(cols * w, rows * h)			)
		lowercase__   ,				lowercase__					:      Any      =		grid.size

		for i, img in enumerate(_lowercase			):
				grid.paste(_lowercase   ,    box=(i % cols * w, i // cols * h)			)
		return grid


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__UpperCamelCase:	List[str] = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))


