
from functools import lru_cache


@lru_cache
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]    ):

       if num < 0:
              raise ValueError("Number should not be negative."    )

       return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''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 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									torch.manual_seed(0					)
									snake_case_									=				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       ,				)
									snake_case_									=				DDIMScheduler(
									    beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									torch.manual_seed(0					)
									snake_case_									=				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					)
									snake_case_									=				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       ,				)
									snake_case_									=				CLIPTextModel(lowercase_					)
									snake_case_									=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

									snake_case_									=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''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    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







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


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				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()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				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'''       ,				)
									snake_case_									=				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    :					List[str]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_					)

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

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				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'''       ,				)
									snake_case_									=				output.images

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

