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







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




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

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







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__=0						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   floats_tensor((1, 3, 32, 32)						,     rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   image / 2 + 0.5
       if str(UpperCamelCase__						).startswith("mps"						):
           snake_case   :				List[Any]				 =   torch.manual_seed(UpperCamelCase__						)
       else:
           snake_case   :				str				 =   torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)
       snake_case   :				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 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   "cpu"  # ensure determinism for the device-dependent torch.Generator

       snake_case   :				List[Any]				 =   self.get_dummy_components()
       snake_case   :				List[str]				 =   CycleDiffusionPipeline(**UpperCamelCase__						)
       snake_case   :				Tuple				 =   pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				str				 =   self.get_dummy_inputs(UpperCamelCase__						)
       snake_case   :				Tuple				 =   pipe(**UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   output.images

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

       assert images.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   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 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Any				 =   self.get_dummy_components()
       for name, module in components.items():
           if hasattr(UpperCamelCase__						,     "half"						):
               snake_case   :				Optional[int]				 =   module.half()
       snake_case   :				List[str]				 =   CycleDiffusionPipeline(**UpperCamelCase__						)
       snake_case   :				str				 =   pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   self.get_dummy_inputs(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   pipe(**UpperCamelCase__						)
       snake_case   :				Dict				 =   output.images

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

       assert images.shape == (1, 32, 32, 3)
       snake_case   :				Tuple				 =   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 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       return super().test_save_load_local()







   @unittest.skip("non-deterministic pipeline"						)
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       return super().test_inference_batch_single_identical()







   @skip_mps
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return super().test_dict_tuple_outputs_equivalent()







   @skip_mps
   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       return super().test_save_load_optional_components()







   @skip_mps
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       return super().test_attention_slicing_forward_pass()







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







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




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

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

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

       snake_case   :				Union[str, Any]				 =   "A black colored car"
       snake_case   :				List[Any]				 =   "A blue colored car"

       snake_case   :				str				 =   torch.manual_seed(0						)
       snake_case   :				int				 =   pipe(
           prompt=UpperCamelCase__						,     source_prompt=UpperCamelCase__						,     image=UpperCamelCase__						,     num_inference_steps=100						,     eta=0.1						,     strength=0.85						,     guidance_scale=3						,     source_guidance_scale=1						,     generator=UpperCamelCase__						,     output_type="np"						,     )
       snake_case   :				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 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				int				 =   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/cycle-diffusion/black_colored_car.png"						)
       snake_case   :				Tuple				 =   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"						)
       snake_case   :				Dict				 =   init_image.resize((512, 512)						)

       snake_case   :				Union[str, Any]				 =   "CompVis/stable-diffusion-v1-4"
       snake_case   :				int				 =   DDIMScheduler.from_pretrained(UpperCamelCase__						,     subfolder="scheduler"						)
       snake_case   :				Optional[int]				 =   CycleDiffusionPipeline.from_pretrained(UpperCamelCase__						,     scheduler=UpperCamelCase__						,     safety_checker=UpperCamelCase__						)

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

       snake_case   :				Optional[Any]				 =   "A black colored car"
       snake_case   :				Optional[Any]				 =   "A blue colored car"

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

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









"""simple docstring"""



from __future__ import annotations

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]





