






import random
import sys

from . import cryptomath_module as cryptomath

_UpperCAmelCase					:						str         =     (
    R' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    R'abcdefghijklmnopqrstuvwxyz{|}~'
)






def  UpperCAmelCase__						(    lowerCamelCase	:			int			,lowerCamelCase	:			int			,lowerCamelCase	:			str    ):


   if mode == "encrypt":
      if key_a == 1:
         sys.exit(
             """The affine cipher becomes weak when key """
             """A is set to 1. Choose different key"""    )
      if key_b == 0:
         sys.exit(
             """The affine cipher becomes weak when key """
             """B is set to 0. Choose different key"""    )
   if key_a < 0 or key_b < 0 or key_b > len(__UpperCamelCase    ) - 1:
      sys.exit(
          """Key A must be greater than 0 and key B must """
          f'''be between 0 and {len(__UpperCamelCase    ) - 1}.'''    )
   if cryptomath.gcd(__UpperCamelCase			,len(__UpperCamelCase    )    ) != 1:
      sys.exit(
          f'''Key A {key_a} and the symbol set size {len(__UpperCamelCase    )} '''
          """are not relatively prime. Choose a different key."""    )






def  UpperCAmelCase__						(    lowerCamelCase	:			int			,lowerCamelCase	:			str    ):
   lowerCamelCase				,       lowerCamelCase									=divmod(__UpperCamelCase			,len(__UpperCamelCase    )    )
   check_keys(__UpperCamelCase			,__UpperCamelCase			,"""encrypt"""    )
   lowerCamelCase									=""""""
   for symbol in message:
      if symbol in SYMBOLS:
         lowerCamelCase									=SYMBOLS.find(__UpperCamelCase    )
         cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(__UpperCamelCase    )]
      else:
         cipher_text += symbol
   return cipher_text






def  UpperCAmelCase__						(    lowerCamelCase	:			int			,lowerCamelCase	:			str    ):
   lowerCamelCase				,       lowerCamelCase									=divmod(__UpperCamelCase			,len(__UpperCamelCase    )    )
   check_keys(__UpperCamelCase			,__UpperCamelCase			,"""decrypt"""    )
   lowerCamelCase									=""""""
   lowerCamelCase									=cryptomath.find_mod_inverse(__UpperCamelCase			,len(__UpperCamelCase    )    )
   for symbol in message:
      if symbol in SYMBOLS:
         lowerCamelCase									=SYMBOLS.find(__UpperCamelCase    )
         plain_text += SYMBOLS[
             (sym_index - key_b) * mod_inverse_of_key_a % len(__UpperCamelCase    )
         ]
      else:
         plain_text += symbol
   return plain_text






def  UpperCAmelCase__						(    ):


   while True:
      lowerCamelCase									=random.randint(2			,len(__UpperCamelCase    )    )
      lowerCamelCase									=random.randint(2			,len(__UpperCamelCase    )    )
      if cryptomath.gcd(__UpperCamelCase			,len(__UpperCamelCase    )    ) == 1 and key_b % len(__UpperCamelCase    ) != 0:
         return key_b * len(__UpperCamelCase    ) + key_b






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=input("""Enter message: """    ).strip()
   lowerCamelCase									=int(input("""Enter key [2000 - 9000]: """    ).strip()    )
   lowerCamelCase									=input("""Encrypt/Decrypt [E/D]: """    ).strip().lower()

   if mode.startswith("""e"""    ):
      lowerCamelCase									="""encrypt"""
      lowerCamelCase									=encrypt_message(__UpperCamelCase			,__UpperCamelCase    )
   elif mode.startswith("""d"""    ):
      lowerCamelCase									="""decrypt"""
      lowerCamelCase									=decrypt_message(__UpperCamelCase			,__UpperCamelCase    )
   print(f'''\n{mode.title()}ed text: \n{translated}'''    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # main()
"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






@nightly
@require_torch_gpu
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2



