






import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			UpperCamelCase_				(      snake_case_	, unittest.TestCase    ):



	'''simple docstring'''




	lowerCAmelCase													=			KandinskyVaaPriorEmbaEmbPipeline
	lowerCAmelCase													=			['''prompt''', '''image''']
	lowerCAmelCase													=			['''prompt''', '''image''']
	lowerCAmelCase													=			[
	    '''num_images_per_prompt''',
	    '''strength''',
	    '''generator''',
	    '''num_inference_steps''',
	    '''latents''',
	    '''negative_prompt''',
	    '''guidance_scale''',
	    '''output_type''',
	    '''return_dict''',
	]
	lowerCAmelCase													=			False




	@property
	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								return 32




	@property
	def 							_UpperCamelCase      (	self )	->   str:
								return 32




	@property
	def 							_UpperCamelCase      (	self )	->   List[str]:
								return self.time_input_dim




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								return self.time_input_dim * 4




	@property
	def 							_UpperCamelCase      (	self )	->   List[str]:
								return 1_00




	@property
	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
								return tokenizer




	@property
	def 							_UpperCamelCase      (	self )	->   Dict:
								torch.manual_seed(0 )
								snake_case_           =							CLIPTextConfig(
								    bos_token_id=0	,      eos_token_id=2	,      hidden_size=self.text_embedder_hidden_size	,      projection_dim=self.text_embedder_hidden_size	,      intermediate_size=37	,      layer_norm_eps=1E-05	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=10_00	,      )
								return CLIPTextModelWithProjection(a )




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								torch.manual_seed(0 )

								snake_case_           =							{
								    'num_attention_heads': 2,
								    'attention_head_dim': 12,
								    'embedding_dim': self.text_embedder_hidden_size,
								    'num_layers': 1,
								}

								snake_case_           =							PriorTransformer(**a )
								# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
								snake_case_           =							nn.Parameter(torch.ones(model.clip_std.shape ) )
								return model




	@property
	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								torch.manual_seed(0 )
								snake_case_           =							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size	,      image_size=2_24	,      projection_dim=self.text_embedder_hidden_size	,      intermediate_size=37	,      num_attention_heads=4	,      num_channels=3	,      num_hidden_layers=5	,      patch_size=14	,      )

								snake_case_           =							CLIPVisionModelWithProjection(a )
								return model




	@property
	def 							_UpperCamelCase      (	self )	->   str:
								snake_case_           =							CLIPImageProcessor(
								    crop_size=2_24	,      do_center_crop=a	,      do_normalize=a	,      do_resize=a	,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,      resample=3	,      size=2_24	,      )

								return image_processor




	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							self.dummy_prior
								snake_case_           =							self.dummy_image_encoder
								snake_case_           =							self.dummy_text_encoder
								snake_case_           =							self.dummy_tokenizer
								snake_case_           =							self.dummy_image_processor

								snake_case_           =							UnCLIPScheduler(
								    variance_type='fixed_small_log'	,      prediction_type='sample'	,      num_train_timesteps=10_00	,      clip_sample=a	,      clip_sample_range=10.0	,      )

								snake_case_           =							{
								    'prior': prior,
								    'image_encoder': image_encoder,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'scheduler': scheduler,
								    'image_processor': image_processor,
								}

								return components




	def 							_UpperCamelCase      (	self	,      a	,      a=0 )	->   str:
								if str(a ).startswith('mps' ):
															snake_case_           =							torch.manual_seed(a )
								else:
															snake_case_           =							torch.Generator(device=a ).manual_seed(a )

								snake_case_           =							floats_tensor((1, 3, 64, 64)	,      rng=random.Random(a ) ).to(a )
								snake_case_           =							image.cpu().permute(0	,      2	,      3	,      1 )[0]
								snake_case_           =							Image.fromarray(np.uinta(a ) ).convert('RGB' ).resize((2_56, 2_56) )

								snake_case_           =							{
								    'prompt': 'horse',
								    'image': init_image,
								    'strength': 0.5,
								    'generator': generator,
								    'guidance_scale': 4.0,
								    'num_inference_steps': 2,
								    'output_type': 'np',
								}
								return inputs




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							'cpu'

								snake_case_           =							self.get_dummy_components()

								snake_case_           =							self.pipeline_class(**a )
								snake_case_           =							pipe.to(a )

								pipe.set_progress_bar_config(disable=a )

								snake_case_           =							pipe(**self.get_dummy_inputs(a ) )
								snake_case_           =							output.image_embeds

								snake_case_           =							pipe(
								    **self.get_dummy_inputs(a )	,      return_dict=a	,      )[0]

								snake_case_           =							image[0, -10:]
								snake_case_           =							image_from_tuple[0, -10:]

								assert image.shape == (1, 32)

								snake_case_           =							np.array(
								    [
								        0.1_071_284,
								        1.3_330_271,
								        0.61_260_223,
								        -0.6_691_065,
								        -0.3_846_852,
								        -1.0_303_661,
								        0.22_716_111,
								        0.03_348_901,
								        0.30_040_675,
								        -0.24_805_029,
								    ] )

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




	@skip_mps
	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							torch_device == 'cpu'
								snake_case_           =							True
								snake_case_           =							False

								self._test_inference_batch_single_identical(
								    test_max_difference=a	,      relax_max_difference=a	,      test_mean_pixel_difference=a	,      )






	@skip_mps
	def 							_UpperCamelCase      (	self )	->   int:
								snake_case_           =							torch_device == 'cpu'
								snake_case_           =							False

								self._test_attention_slicing_forward_pass(
								    test_max_difference=a	,      test_mean_pixel_difference=a	,      )













from __future__ import annotations





def 						__UpperCAmelCase      (					a_):
							for r in range(1							,					a_):
														snake_case_           =							[]
														for x in range(n - 1):
																					snake_case_           =							pow(a_							,					a_							,					a_)
																					if val in li:
																												break
																					li.append(a_)
														else:
																					return r
							return None


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

		lowercase  							= pow(b_public, a_private, q)
		lowercase  							= 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)





