

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






     def 	UpperCAmelCase_				(				self	)->     Dict:
         torch.manual_seed(0	)
         UpperCamelCase_			  =						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		,						)
         UpperCamelCase_			  =						DDIMScheduler(
             beta_start=0.00_085		,						beta_end=0.012		,						beta_schedule="scaled_linear"		,						num_train_timesteps=1_000		,						clip_sample=_lowercase		,						set_alpha_to_one=_lowercase		,						)
         torch.manual_seed(0	)
         UpperCamelCase_			  =						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	)
         UpperCamelCase_			  =						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=1_000		,						)
         UpperCamelCase_			  =						CLIPTextModel(_lowercase	)
         UpperCamelCase_			  =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

         UpperCamelCase_			  =						{
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components






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

         UpperCamelCase_			  =						self.get_dummy_components()
         UpperCamelCase_			  =						CycleDiffusionPipeline(**_lowercase	)
         UpperCamelCase_			  =						pipe.to(_lowercase	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs(_lowercase	)
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images

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

         assert images.shape == (1, 32, 32, 3)
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.get_dummy_components()
         for name, module in components.items():
             if hasattr(_lowercase		,						"half"	):
                 UpperCamelCase_			  =						module.half()
         UpperCamelCase_			  =						CycleDiffusionPipeline(**_lowercase	)
         UpperCamelCase_			  =						pipe.to(_lowercase	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs(_lowercase	)
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images

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

         assert images.shape == (1, 32, 32, 3)
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     List[Any]:
         return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"	)
     def 	UpperCAmelCase_				(				self	)->     Dict:
         return super().test_inference_batch_single_identical()






     @skip_mps
     def 	UpperCAmelCase_				(				self	)->     str:
         return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         return super().test_save_load_optional_components()






     @skip_mps
     def 	UpperCAmelCase_				(				self	)->     Dict:
         return super().test_attention_slicing_forward_pass()






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






     def 	UpperCAmelCase_				(				self	)->     Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






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

         UpperCamelCase_			  =						"CompVis/stable-diffusion-v1-4"
         UpperCamelCase_			  =						DDIMScheduler.from_pretrained(_lowercase		,						subfolder="scheduler"	)
         UpperCamelCase_			  =						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()

         UpperCamelCase_			  =						"A black colored car"
         UpperCamelCase_			  =						"A blue colored car"

         UpperCamelCase_			  =						torch.manual_seed(0	)
         UpperCamelCase_			  =						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"		,						)
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/cycle-diffusion/black_colored_car.png"	)
         UpperCamelCase_			  =						load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"	)
         UpperCamelCase_			  =						init_image.resize((512, 512)	)

         UpperCamelCase_			  =						"CompVis/stable-diffusion-v1-4"
         UpperCamelCase_			  =						DDIMScheduler.from_pretrained(_lowercase		,						subfolder="scheduler"	)
         UpperCamelCase_			  =						CycleDiffusionPipeline.from_pretrained(_lowercase		,						scheduler=_lowercase		,						safety_checker=_lowercase	)

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

         UpperCamelCase_			  =						"A black colored car"
         UpperCamelCase_			  =						"A blue colored car"

         UpperCamelCase_			  =						torch.manual_seed(0	)
         UpperCamelCase_			  =						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"		,						)
         UpperCamelCase_			  =						output.images

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



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
