




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class     a__ (      UpperCamelCase__     ,      UpperCamelCase__     ,      unittest.TestCase   ):
     a	:    Optional[Any]	      =			IFImgaImgPipeline
     a	:    List[str]	      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
     a	:    Optional[Any]	      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     a	:    Tuple	      =			PipelineTesterMixin.required_optional_params - {"""latents"""}







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          return self._get_dummy_components()







     def     lowerCAmelCase_       (			self						,	A						,	A=0				)						->					Dict:



          '''simple docstring'''



          if str(A				).startswith("mps"				):
               a											=   torch.manual_seed(A				)
          else:
               a											=   torch.Generator(device=A				).manual_seed(A				)

          a											=   floats_tensor((1, 3, 32, 32)						,	rng=random.Random(A				)				).to(A				)

          a											=   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          self._test_save_load_optional_components()







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()						,	reason="XFormers attention is only available with CUDA and `xformers` installed"						,	)
     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)







     @unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"				)
     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          super().test_save_load_floataa(expected_max_diff=1e-1				)







     @unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"				)
     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          super().test_floataa_inference(expected_max_diff=1e-1				)







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          self._test_save_load_local()







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2						,	)







from __future__ import annotations

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  int | None:
     for r in range(1	,      __UpperCamelCase):
          a											=   []
          for x in range(n - 1):
               a											=   pow(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
               if val in li:
                    break
               li.append(__UpperCamelCase)
          else:
               return r
     return None


if __name__ == "__main__":
   lowercase__   :    Tuple 					=      int(input("Enter a prime number q: "))
   lowercase__   :    List[Any] 					=      find_primitive(q)
   if a is None:
      print(F'Cannot find the primitive for the value: {a!r}')
   else:
      lowercase__   :    Any 					=      int(input("Enter private key of A: "))
      lowercase__   :    Optional[int] 					=      pow(a, a_private, q)
      lowercase__   :    Optional[Any] 					=      int(input("Enter private key of B: "))
      lowercase__   :    Any 					=      pow(a, b_private, q)

      lowercase__   :    Optional[Any] 					=      pow(b_public, a_private, q)
      lowercase__   :    List[str] 					=      pow(a_public, b_private, q)

      print("The key value generated by A is: ", a_secret)
      print("The key value generated by B is: ", b_secret)

