




"""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__	(     lowercase,	lowercase,	unittest.TestCase						):






			'''simple docstring'''



			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 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			Dict    =    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  ,      )
							_lowerCamelCase     :			Optional[Any]    =    DDIMScheduler(
							    beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      beta_schedule='scaled_linear'  ,      num_train_timesteps=1000  ,      clip_sample=lowercase  ,      set_alpha_to_one=lowercase  ,      )
							torch.manual_seed(0  )
							_lowerCamelCase     :			Optional[Any]    =    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  )
							_lowerCamelCase     :			Optional[Any]    =    CLIPTextConfig(
							    bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=32  ,      intermediate_size=37  ,      layer_norm_eps=1E-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1000  ,      )
							_lowerCamelCase     :			Any    =    CLIPTextModel(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

							_lowerCamelCase     :			int    =    {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							_lowerCamelCase     :			Union[str, Any]    =    floats_tensor((1, 3, 32, 32)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    image / 2 + 0.5
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Optional[int]    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			Union[str, Any]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			List[Any]    =    {
							    '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 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'  # ensure determinism for the device-dependent torch.Generator

							_lowerCamelCase     :			str    =    self.get_dummy_components()
							_lowerCamelCase     :			Tuple    =    CycleDiffusionPipeline(**lowercase  )
							_lowerCamelCase     :			int    =    pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[Any]    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			List[Any]    =    pipe(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    output.images

							_lowerCamelCase     :			Optional[Any]    =    images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[str]    =    np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
			@unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.get_dummy_components()
							for name, module in components.items():
											if hasattr(lowercase  ,      'half'  ):
															_lowerCamelCase     :			str    =    module.half()
							_lowerCamelCase     :			List[str]    =    CycleDiffusionPipeline(**lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[Any]    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe(**lowercase  )
							_lowerCamelCase     :			Tuple    =    output.images

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

							assert images.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			str    =    np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_local()
			@unittest.skip('non-deterministic pipeline'  )
			def 					A_    (  self  ):
							return super().test_inference_batch_single_identical()
			@skip_mps
			def 					A_    (  self  ):
							return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_optional_components()






			@skip_mps
			def 					A_    (  self  ):
							return super().test_attention_slicing_forward_pass()






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






			'''simple docstring'''



			def 					A_    (  self  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/cycle-diffusion/black_colored_car.png'  )
							_lowerCamelCase     :			Any    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'  )
							_lowerCamelCase     :			Optional[Any]    =    init_image.resize((512, 512)  )

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

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

							_lowerCamelCase     :			Tuple    =    'A black colored car'
							_lowerCamelCase     :			Optional[Any]    =    'A blue colored car'

							_lowerCamelCase     :			List[Any]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    pipe(
							    prompt=lowercase  ,      source_prompt=lowercase  ,      image=lowercase  ,      num_inference_steps=100  ,      eta=0.1  ,      strength=0.85  ,      guidance_scale=3  ,      source_guidance_scale=1  ,      generator=lowercase  ,      output_type='np'  ,      )
							_lowerCamelCase     :			Optional[Any]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/cycle-diffusion/black_colored_car.png'  )
							_lowerCamelCase     :			str    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'  )
							_lowerCamelCase     :			Tuple    =    init_image.resize((512, 512)  )

							_lowerCamelCase     :			List[Any]    =    'CompVis/stable-diffusion-v1-4'
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  ,      subfolder='scheduler'  )
							_lowerCamelCase     :			str    =    CycleDiffusionPipeline.from_pretrained(lowercase  ,      scheduler=lowercase  ,      safety_checker=lowercase  )

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

							_lowerCamelCase     :			List[str]    =    'A black colored car'
							_lowerCamelCase     :			Tuple    =    'A blue colored car'

							_lowerCamelCase     :			Dict    =    torch.manual_seed(0  )
							_lowerCamelCase     :			Dict    =    pipe(
							    prompt=lowercase  ,      source_prompt=lowercase  ,      image=lowercase  ,      num_inference_steps=100  ,      eta=0.1  ,      strength=0.85  ,      guidance_scale=3  ,      source_guidance_scale=1  ,      generator=lowercase  ,      output_type='np'  ,      )
							_lowerCamelCase     :			str    =    output.images

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






'''simple docstring'''




import math






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5 ,							int(math.sqrt(__snake_case			) + 1			) ,							6			):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def        _lowerCAmelCase			(  __snake_case  : float = 0.1			)     ->     int:
     __A						: Tuple      =       3
     __A						: Optional[int]      =       3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1 ,							(j + 2) * (j + 2) ,							j + 1			):
               primes += is_prime(__snake_case			)
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()