
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			lowerCAmelCase__       (  __UpperCamelCase   ,						__UpperCamelCase   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					CycleDiffusionPipeline
					lowerCAmelCase_					       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    '''negative_prompt''',
					    '''height''',
					    '''width''',
					    '''negative_prompt_embeds''',
					}
					lowerCAmelCase_					       =					PipelineTesterMixin.required_optional_params - {'''latents'''}
					lowerCAmelCase_					       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}					)
					lowerCAmelCase_					       =					IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowerCAmelCase_					       =					IMAGE_TO_IMAGE_IMAGE_PARAMS






					def      _snake_case   (       self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_     :					List[str]													=	UNetaDConditionModel(
											    block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,							cross_attention_dim=32    ,							)
											lowercase_     :					Any													=	DDIMScheduler(
											    beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							num_train_timesteps=10_00    ,							clip_sample=__SCREAMING_SNAKE_CASE    ,							set_alpha_to_one=__SCREAMING_SNAKE_CASE    ,							)
											torch.manual_seed(0		)
											lowercase_     :					List[str]													=	AutoencoderKL(
											    block_out_channels=[32, 64]    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,							latent_channels=4    ,							)
											torch.manual_seed(0		)
											lowercase_     :					str													=	CLIPTextConfig(
											    bos_token_id=0    ,							eos_token_id=2    ,							hidden_size=32    ,							intermediate_size=37    ,							layer_norm_eps=1E-0_5    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							pad_token_id=1    ,							vocab_size=10_00    ,							)
											lowercase_     :					List[str]													=	CLIPTextModel(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

											lowercase_     :					Optional[int]													=	{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											lowercase_     :					Dict													=	floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Any													=	image / 2 + 0.5
											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_     :					Dict													=	torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_     :					List[str]													=	torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	{
											    'prompt': 'An astronaut riding an elephant',
											    'source_prompt': 'An astronaut riding a horse',
											    'image': image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'eta': 0.1,
											    'strength': 0.8,
											    'guidance_scale': 3,
											    'source_guidance_scale': 1,
											    'output_type': 'numpy',
											}
											return inputs






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	'cpu'  # ensure determinism for the device-dependent torch.Generator

											lowercase_     :					List[Any]													=	self.get_dummy_components()
											lowercase_     :					List[str]													=	CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[Any]													=	pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	pipe(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	output.images

											lowercase_     :					Dict													=	images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											lowercase_     :					List[Any]													=	np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]		)

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






					@unittest.skipIf(torch_device != '''cuda'''    ,							'''This test requires a GPU'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.get_dummy_components()
											for name, module in components.items():
																	if hasattr(__SCREAMING_SNAKE_CASE    ,							'''half'''		):
																							lowercase_     :					str													=	module.half()
											lowercase_     :					int													=	CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Any													=	pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[Any]													=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	pipe(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	output.images

											lowercase_     :					int													=	images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											lowercase_     :					Optional[int]													=	np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]		)

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






					@skip_mps
					def      _snake_case   (       self		):


											"""simple docstring"""





											return super().test_save_load_local()






					@unittest.skip('''non-deterministic pipeline'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											return super().test_inference_batch_single_identical()






					@skip_mps
					def      _snake_case   (       self		):


											"""simple docstring"""





											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def      _snake_case   (       self		):


											"""simple docstring"""





											return super().test_save_load_optional_components()






					@skip_mps
					def      _snake_case   (       self		):


											"""simple docstring"""





											return super().test_attention_slicing_forward_pass()







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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''		)
											lowercase_     :					Tuple													=	load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''		)
											lowercase_     :					Dict													=	init_image.resize((5_12, 5_12)		)

											lowercase_     :					Optional[int]													=	'CompVis/stable-diffusion-v1-4'
											lowercase_     :					Optional[int]													=	DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE    ,							subfolder='''scheduler'''		)
											lowercase_     :					Optional[int]													=	CycleDiffusionPipeline.from_pretrained(
											    __SCREAMING_SNAKE_CASE    ,							scheduler=__SCREAMING_SNAKE_CASE    ,							safety_checker=__SCREAMING_SNAKE_CASE    ,							torch_dtype=torch.floataa    ,							revision='''fp16'''		)

											pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
											pipe.enable_attention_slicing()

											lowercase_     :					Any													=	'A black colored car'
											lowercase_     :					List[str]													=	'A blue colored car'

											lowercase_     :					Optional[Any]													=	torch.manual_seed(0		)
											lowercase_     :					str													=	pipe(
											    prompt=__SCREAMING_SNAKE_CASE    ,							source_prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=1_00    ,							eta=0.1    ,							strength=0.85    ,							guidance_scale=3    ,							source_guidance_scale=1    ,							generator=__SCREAMING_SNAKE_CASE    ,							output_type='''np'''    ,							)
											lowercase_     :					int													=	output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image		).max() < 5E-1






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''		)
											lowercase_     :					Optional[int]													=	load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''		)
											lowercase_     :					List[Any]													=	init_image.resize((5_12, 5_12)		)

											lowercase_     :					Optional[Any]													=	'CompVis/stable-diffusion-v1-4'
											lowercase_     :					Union[str, Any]													=	DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE    ,							subfolder='''scheduler'''		)
											lowercase_     :					str													=	CycleDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE    ,							scheduler=__SCREAMING_SNAKE_CASE    ,							safety_checker=__SCREAMING_SNAKE_CASE		)

											pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
											pipe.enable_attention_slicing()

											lowercase_     :					Union[str, Any]													=	'A black colored car'
											lowercase_     :					Optional[Any]													=	'A blue colored car'

											lowercase_     :					Tuple													=	torch.manual_seed(0		)
											lowercase_     :					Optional[Any]													=	pipe(
											    prompt=__SCREAMING_SNAKE_CASE    ,							source_prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=1_00    ,							eta=0.1    ,							strength=0.85    ,							guidance_scale=3    ,							source_guidance_scale=1    ,							generator=__SCREAMING_SNAKE_CASE    ,							output_type='''np'''    ,							)
											lowercase_     :					Any													=	output.images

											assert np.abs(image - expected_image		).max() < 2E-2





'''simple docstring'''







from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	prime_factors(__SCREAMING_SNAKE_CASE							)
						if is_square_free(__SCREAMING_SNAKE_CASE							):
												return -1 if len(__SCREAMING_SNAKE_CASE							) % 2 else 1
						return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()



