


from functools import lru_cache



@lru_cache
def     __lowerCAmelCase						(							snake_case	:    int )       ->							int:
   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()






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					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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:				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=1000    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

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

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	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  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[str]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_save_load_local()





     @unittest.skip("""non-deterministic pipeline"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return super().test_inference_batch_single_identical()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        return super().test_save_load_optional_components()







     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_attention_slicing_forward_pass()



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





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





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

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	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()

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

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

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	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()

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

        __lowerCamelCase:				str     =	torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Any     =	output.images

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


