


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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	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						,    )
						__a      :	Dict    =  DDIMScheduler(
						    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='scaled_linear'						,    num_train_timesteps=1000						,    clip_sample=__a						,    set_alpha_to_one=__a						,    )
						torch.manual_seed(0							)
						__a      :	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							)
						__a      :	Tuple    =  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						,    )
						__a      :	int    =  CLIPTextModel(__a							)
						__a      :	Union[str, Any]    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__a							)							).to(__a							)
						__a      :	Optional[int]    =  image / 2 + 0.5
						if str(__a							).startswith('mps'							):
											__a      :	Dict    =  torch.manual_seed(__a							)
						else:
											__a      :	Dict    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a      :	Optional[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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  'cpu'  # ensure determinism for the device-dependent torch.Generator

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

						__a      :	int    =  self.get_dummy_inputs(__a							)
						__a      :	Any    =  pipe(**__a							)
						__a      :	List[str]    =  output.images

						__a      :	List[str]    =  images[0, -3:, -3:, -1]

						assert images.shape == (1, 32, 32, 3)
						__a      :	Optional[Any]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Dict    =  self.get_dummy_inputs(__a							)
						__a      :	Any    =  pipe(**__a							)
						__a      :	int    =  output.images

						__a      :	Tuple    =  images[0, -3:, -3:, -1]

						assert images.shape == (1, 32, 32, 3)
						__a      :	Any    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_save_load_local()




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

						'''simple docstring'''





						return super().test_inference_batch_single_identical()




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_save_load_optional_components()





	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_attention_slicing_forward_pass()





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

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

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

						__a      :	Union[str, Any]    =  'A black colored car'
						__a      :	int    =  'A blue colored car'

						__a      :	int    =  torch.manual_seed(0							)
						__a      :	List[Any]    =  pipe(
						    prompt=__a						,    source_prompt=__a						,    image=__a						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__a						,    output_type='np'						,    )
						__a      :	List[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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/cycle-diffusion/black_colored_car.png'							)
						__a      :	Tuple    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'							)
						__a      :	Optional[Any]    =  init_image.resize((512, 512)							)

						__a      :	int    =  'CompVis/stable-diffusion-v1-4'
						__a      :	Tuple    =  DDIMScheduler.from_pretrained(__a						,    subfolder='scheduler'							)
						__a      :	Tuple    =  CycleDiffusionPipeline.from_pretrained(__a						,    scheduler=__a						,    safety_checker=__a							)

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

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

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

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







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()



