



"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							UnCLIPImageVariationPipeline
						__UpperCamelCase		    =							IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
						__UpperCamelCase		    =							IMAGE_VARIATION_BATCH_PARAMS

						__UpperCamelCase		    =							[
						    """generator""",
						    """return_dict""",
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]
						__UpperCamelCase		    =							False






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






						@property
						def      UpperCAmelCase__					( self			:List[str] )    ->      Dict:
												return 32






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												return self.time_input_dim






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
												return self.time_input_dim * 4






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






						@property
						def      UpperCAmelCase__					( self			:List[str] )    ->      Union[str, Any]:
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
												return tokenizer






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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(lowercase_ )






						@property
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Any:
												torch.manual_seed(0 )
												UpperCAmelCase					=			CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size       ,  projection_dim=self.text_embedder_hidden_size       ,  num_hidden_layers=5       ,  num_attention_heads=4       ,  image_size=32       ,  intermediate_size=37       ,  patch_size=1       ,  )
												return CLIPVisionModelWithProjection(lowercase_ )






						@property
						def      UpperCAmelCase__					( self			:str )    ->      str:
												torch.manual_seed(0 )

												UpperCAmelCase					=			{
												    'clip_embeddings_dim': self.text_embedder_hidden_size,
												    'time_embed_dim': self.time_embed_dim,
												    'cross_attention_dim': self.cross_attention_dim,
												}

												UpperCAmelCase					=			UnCLIPTextProjModel(**lowercase_ )
												return model






						@property
						def      UpperCAmelCase__					( self			:Any )    ->      Any:
												torch.manual_seed(0 )

												UpperCAmelCase					=			{
												    'sample_size': 32,
												    # RGB in channels
												    'in_channels': 3,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 6,
												    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
												    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
												    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
												    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    'layers_per_block': 1,
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': 'identity',
												}

												UpperCAmelCase					=			UNetaDConditionModel(**lowercase_ )
												return model






						@property
						def      UpperCAmelCase__					( self			:Dict )    ->      Optional[int]:
												return {
												    "sample_size": 64,
												    "layers_per_block": 1,
												    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
												    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "in_channels": 6,
												    "out_channels": 3,
												}






						@property
						def      UpperCAmelCase__					( self			:List[str] )    ->      Optional[Any]:
												torch.manual_seed(0 )

												UpperCAmelCase					=			UNetaDModel(**self.dummy_super_res_kwargs )
												return model






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Tuple:
												# seeded differently to get different unet than `self.dummy_super_res_first`
												torch.manual_seed(1 )

												UpperCAmelCase					=			UNetaDModel(**self.dummy_super_res_kwargs )
												return model






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[str]:
												UpperCAmelCase					=			self.dummy_decoder
												UpperCAmelCase					=			self.dummy_text_proj
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			self.dummy_tokenizer
												UpperCAmelCase					=			self.dummy_super_res_first
												UpperCAmelCase					=			self.dummy_super_res_last

												UpperCAmelCase					=			UnCLIPScheduler(
												    variance_type='learned_range'       ,  prediction_type='epsilon'       ,  num_train_timesteps=10_00       ,  )

												UpperCAmelCase					=			UnCLIPScheduler(
												    variance_type='fixed_small_log'       ,  prediction_type='epsilon'       ,  num_train_timesteps=10_00       ,  )

												UpperCAmelCase					=			CLIPImageProcessor(crop_size=32       ,  size=32 )

												UpperCAmelCase					=			self.dummy_image_encoder

												return {
												    "decoder": decoder,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "text_proj": text_proj,
												    "feature_extractor": feature_extractor,
												    "image_encoder": image_encoder,
												    "super_res_first": super_res_first,
												    "super_res_last": super_res_last,
												    "decoder_scheduler": decoder_scheduler,
												    "super_res_scheduler": super_res_scheduler,
												}






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Dict       ,  lowercase_			:Dict=0       ,  lowercase_			:Dict=True )    ->      str:
												UpperCAmelCase					=			floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(lowercase_ ) ).to(lowercase_ )
												if str(lowercase_ ).startswith('mps' ):
																		UpperCAmelCase					=			torch.manual_seed(lowercase_ )
												else:
																		UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

												if pil_image:
																		UpperCAmelCase					=			input_image * 0.5 + 0.5
																		UpperCAmelCase					=			input_image.clamp(0       ,  1 )
																		UpperCAmelCase					=			input_image.cpu().permute(0       ,  2       ,  3       ,  1 ).float().numpy()
																		UpperCAmelCase					=			DiffusionPipeline.numpy_to_pil(lowercase_ )[0]

												return {
												    "image": input_image,
												    "generator": generator,
												    "decoder_num_inference_steps": 2,
												    "super_res_num_inference_steps": 2,
												    "output_type": "np",
												}






						def      UpperCAmelCase__					( self			:List[Any] )    ->      str:
												UpperCAmelCase					=			'cpu'

												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )

												UpperCAmelCase					=			pipe(**lowercase_ )
												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )

												UpperCAmelCase					=			pipe(
												    **lowercase_       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												UpperCAmelCase					=			np.array(
												    [
												        0.9997,
												        0.0002,
												        0.9997,
												        0.9997,
												        0.9969,
												        0.0023,
												        0.9997,
												        0.9969,
												        0.9970,
												    ] )

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






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      int:
												UpperCAmelCase					=			'cpu'

												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )

												UpperCAmelCase					=			pipe(**lowercase_ )
												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )

												UpperCAmelCase					=			pipe(
												    **lowercase_       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												UpperCAmelCase					=			np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )

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






						def      UpperCAmelCase__					( self			:Dict )    ->      Union[str, Any]:
												UpperCAmelCase					=			'cpu'

												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )
												UpperCAmelCase					=			[
												    pipeline_inputs['image'],
												    pipeline_inputs['image'],
												]

												UpperCAmelCase					=			pipe(**lowercase_ )
												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )
												UpperCAmelCase					=			[
												    tuple_pipeline_inputs['image'],
												    tuple_pipeline_inputs['image'],
												]

												UpperCAmelCase					=			pipe(
												    **lowercase_       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (2, 64, 64, 3)

												UpperCAmelCase					=			np.array(
												    [
												        0.9997,
												        0.9989,
												        0.0008,
												        0.0021,
												        0.9960,
												        0.0018,
												        0.0014,
												        0.0002,
												        0.9933,
												    ] )

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






						def      UpperCAmelCase__					( self			:List[str] )    ->      Dict:
												UpperCAmelCase					=			torch.device('cpu' )







												class 				A_      :



																		"""simple docstring"""
																		__UpperCamelCase		    =							1


												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			pipe.decoder.dtype
												UpperCAmelCase					=			1

												UpperCAmelCase					=			(
												    batch_size,
												    pipe.decoder.config.in_channels,
												    pipe.decoder.config.sample_size,
												    pipe.decoder.config.sample_size,
												)
												UpperCAmelCase					=			pipe.prepare_latents(
												    lowercase_       ,  dtype=lowercase_       ,  device=lowercase_       ,  generator=lowercase_       ,  latents=lowercase_       ,  scheduler=DummyScheduler() )

												UpperCAmelCase					=			(
												    batch_size,
												    pipe.super_res_first.config.in_channels // 2,
												    pipe.super_res_first.config.sample_size,
												    pipe.super_res_first.config.sample_size,
												)
												UpperCAmelCase					=			pipe.prepare_latents(
												    lowercase_       ,  dtype=lowercase_       ,  device=lowercase_       ,  generator=lowercase_       ,  latents=lowercase_       ,  scheduler=DummyScheduler() )

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )

												UpperCAmelCase					=			pipe(
												    **lowercase_       ,  decoder_latents=lowercase_       ,  super_res_latents=lowercase_ ).images

												UpperCAmelCase					=			self.get_dummy_inputs(lowercase_       ,  pil_image=lowercase_ )
												# Don't pass image, instead pass embedding
												UpperCAmelCase					=			pipeline_inputs.pop('image' )
												UpperCAmelCase					=			pipe.image_encoder(lowercase_ ).image_embeds

												UpperCAmelCase					=			pipe(
												    **lowercase_       ,  decoder_latents=lowercase_       ,  super_res_latents=lowercase_       ,  image_embeddings=lowercase_       ,  ).images

												# make sure passing text embeddings manually is identical
												assert np.abs(img_out_a - img_out_a ).max() < 1E-4






						@skip_mps
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			torch_device == 'cpu'

												# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
												UpperCAmelCase					=			1E-2

												self._test_attention_slicing_forward_pass(
												    test_max_difference=lowercase_       ,  expected_max_diff=lowercase_ )






						@skip_mps
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Dict:
												UpperCAmelCase					=			torch_device == 'cpu'
												UpperCAmelCase					=			True
												UpperCAmelCase					=			[
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

												self._test_inference_batch_single_identical(
												    test_max_difference=lowercase_       ,  relax_max_difference=lowercase_       ,  additional_params_copy_to_batched_inputs=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      int:
												UpperCAmelCase					=			[
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

												if torch_device == "mps":
																		# TODO: MPS errors with larger batch sizes
																		UpperCAmelCase					=			[2, 3]
																		self._test_inference_batch_consistent(
																		    batch_sizes=lowercase_       ,  additional_params_copy_to_batched_inputs=lowercase_       ,  )
												else:
																		self._test_inference_batch_consistent(
																		    additional_params_copy_to_batched_inputs=lowercase_ )






						@skip_mps
						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def      UpperCAmelCase__					( self			:Any )    ->      Optional[int]:
												return super().test_save_load_local()






						@skip_mps
						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:int )    ->      Optional[int]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )

												UpperCAmelCase					=			UnCLIPImageVariationPipeline.from_pretrained(
												    'kakaobrain/karlo-v1-alpha-image-variations'       ,  torch_dtype=torch.floataa )
												UpperCAmelCase					=			pipeline.to(lowercase_ )
												pipeline.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			torch.Generator(device='cpu' ).manual_seed(0 )
												UpperCAmelCase					=			pipeline(
												    lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images[0]

												assert image.shape == (2_56, 2_56, 3)

												assert_mean_pixel_difference(lowercase_       ,  lowercase_       ,  15 )










"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_    					=	3

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating primitive root of p'						)
						while True:
												UpperCAmelCase					=			random.randrange(3		,     lowercase_						)
												if pow(lowercase_		,     2		,     lowercase_						) == 1:
																		continue
												if pow(lowercase_		,     lowercase_		,     lowercase_						) == 1:
																		continue
												return g

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating prime p...'						)
						UpperCAmelCase					=			rabin_miller.generate_large_prime(lowercase_						)  # select large prime number.
						UpperCAmelCase					=			primitive_root(lowercase_						)  # one primitive root on modulo p.
						UpperCAmelCase					=			random.randrange(3		,     lowercase_						)  # private_key -> have to be greater than 2 for safety.
						UpperCAmelCase					=			cryptomath.find_mod_inverse(pow(lowercase_		,     lowercase_		,     lowercase_						)		,     lowercase_						)

						UpperCAmelCase					=			(key_size, e_a, e_a, p)
						UpperCAmelCase					=			(key_size, d)

						return public_key, private_key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
												print('\nWARNING:'						)
												print(
												    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    'Use a different name or delete these files and re-run this program.'						)
												sys.exit()

						UpperCAmelCase				,   UpperCAmelCase					=			generate_key(lowercase_						)
						print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
						with open(F"""{name}_pubkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

						print(F"""Writing private key to file {name}_privkey.txt..."""						)
						with open(F"""{name}_privkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{private_key[0]},{private_key[1]}"""						)

def 			_lowerCAmelCase				(       ):
						print('Making key files...'						)
						make_key_files('elgamal'		,     2048						)
						print('Key files generation successful'						)


if __name__ == "__main__":
				main()





