



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 					snake_case			(UpperCamelCase		,   UpperCamelCase		,   UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:Optional[Any]     =       StableDiffusionInstructPixaPixPipeline
       lowerCAmelCase__	:Tuple     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
       lowerCAmelCase__	:str     =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       lowerCAmelCase__	:Dict     =       IMAGE_TO_IMAGE_IMAGE_PARAMS
       lowerCAmelCase__	:int     =       IMAGE_TO_IMAGE_IMAGE_PARAMS



       def 				_a   (					self   )	->				Tuple:
           torch.manual_seed(0   )
           lowercase__									=    UNetaDConditionModel(
               block_out_channels=(32, 64)  ,layers_per_block=2  ,sample_size=32  ,in_channels=8  ,out_channels=4  ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,cross_attention_dim=32  ,)
           lowercase__									=    PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
           torch.manual_seed(0   )
           lowercase__									=    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   )
           lowercase__									=    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  ,)
           lowercase__									=    CLIPTextModel(UpperCAmelCase_   )
           lowercase__									=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=0   )	->				int:
           lowercase__									=    floats_tensor((1, 3, 32, 32)  ,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
           lowercase__									=    image.cpu().permute(0  ,2  ,3  ,1   )[0]
           lowercase__									=    Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert("RGB"   )
           if str(UpperCAmelCase_   ).startswith("mps"   ):
               lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           else:
               lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    {
               "prompt": "A painting of a squirrel eating a burger",
               "image": image,
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "image_guidance_scale": 1,
               "output_type": "numpy",
           }
           return inputs



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    sd_pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1]
           assert image.shape == (1, 32, 32, 3)
           lowercase__									=    np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				str:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    "french fries"
           lowercase__									=    sd_pipe(**UpperCAmelCase_  ,negative_prompt=UpperCAmelCase_   )
           lowercase__									=    output.images
           lowercase__									=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase__									=    np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				List[Any]:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    [inputs["prompt"]] * 2

           lowercase__									=    np.array(inputs["image"]   ).astype(np.floataa   ) / 2_55.0
           lowercase__									=    torch.from_numpy(UpperCAmelCase_   ).unsqueeze(0   ).to(UpperCAmelCase_   )
           lowercase__									=    image / 2 + 0.5
           lowercase__									=    image.permute(0  ,3  ,1  ,2   )
           lowercase__									=    image.repeat(2  ,1  ,1  ,1   )

           lowercase__									=    sd_pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[-1, -3:, -3:, -1]

           assert image.shape == (2, 32, 32, 3)
           lowercase__									=    np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				List[Any]:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    EulerAncestralDiscreteScheduler(
               beta_start=0.0_00_85  ,beta_end=0.0_12  ,beta_schedule="scaled_linear"   )
           lowercase__									=    StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    sd_pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1]

           lowercase__									=    [round(UpperCAmelCase_  ,4   ) for x in image_slice.flatten().tolist()]
           print(",".join([str(UpperCAmelCase_   ) for x in slice]   )   )

           assert image.shape == (1, 32, 32, 3)
           lowercase__									=    np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				int:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_   )
           lowercase__									=    VaeImageProcessor(do_resize=UpperCAmelCase_  ,do_normalize=UpperCAmelCase_   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase_  ,input_image_type="pt"   )   )[0]

           lowercase__									=    components["vae"]
           lowercase__									=    self.get_dummy_inputs_by_type(UpperCAmelCase_  ,input_image_type="pt"   )

           for image_param in self.image_latents_params:
               if image_param in inputs.keys():
                   lowercase__									=    vae.encode(inputs[image_param]   ).latent_dist.mode()

           lowercase__									=    pipe(**UpperCAmelCase_   )[0]

           lowercase__									=    np.abs(out - out_latents_inputs   ).max()
           self.assertLess(UpperCAmelCase_  ,1E-4  ,"passing latents as image input generate different result from passing image"   )





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



       def 				_a   (					self   )	->				str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 				_a   (					self  ,UpperCAmelCase_=0   )	->				Optional[Any]:
           lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           lowercase__									=    load_image(
               "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"   )
           lowercase__									=    {
               "prompt": "turn him into a cyborg",
               "image": image,
               "generator": generator,
               "num_inference_steps": 3,
               "guidance_scale": 7.5,
               "image_guidance_scale": 1.0,
               "output_type": "numpy",
           }
           return inputs



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               "timbrooks/instruct-pix2pix"  ,safety_checker=UpperCAmelCase_   )
           pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing()

           lowercase__									=    self.get_inputs()
           lowercase__									=    pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowercase__									=    np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]   )

           assert np.abs(expected_slice - image_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				int:
           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               "timbrooks/instruct-pix2pix"  ,safety_checker=UpperCAmelCase_   )
           lowercase__									=    LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
           pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing()

           lowercase__									=    self.get_inputs()
           lowercase__									=    pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowercase__									=    np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]   )

           assert np.abs(expected_slice - image_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               "timbrooks/instruct-pix2pix"  ,safety_checker=UpperCAmelCase_   )
           lowercase__									=    DDIMScheduler.from_config(pipe.scheduler.config   )
           pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing()

           lowercase__									=    self.get_inputs()
           lowercase__									=    pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowercase__									=    np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]   )

           assert np.abs(expected_slice - image_slice   ).max() < 1E-3



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    0

           def callback_fn(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   ) -> None:
               lowercase__									=    True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                   lowercase__									=    latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   lowercase__									=    latents[0, -3:, -3:, -1]
                   lowercase__									=    np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]   )

                   assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
               elif step == 2:
                   lowercase__									=    latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   lowercase__									=    latents[0, -3:, -3:, -1]
                   lowercase__									=    np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]   )

                   assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

           lowercase__									=    False

           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               "timbrooks/instruct-pix2pix"  ,safety_checker=UpperCAmelCase_  ,torch_dtype=torch.floataa   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing()

           lowercase__									=    self.get_inputs()
           pipe(**UpperCAmelCase_  ,callback=UpperCAmelCase_  ,callback_steps=1   )
           assert callback_fn.has_been_called
           assert number_of_steps == 3



       def 				_a   (					self   )	->				Any:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               "timbrooks/instruct-pix2pix"  ,safety_checker=UpperCAmelCase_  ,torch_dtype=torch.floataa   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           lowercase__									=    self.get_inputs()
           lowercase__									=    pipe(**UpperCAmelCase_   )

           lowercase__									=    torch.cuda.max_memory_allocated()
           # make sure that less than 2.2 GB is allocated
           assert mem_bytes < 2.2 * 10**9







       def 				_a   (					self   )	->				int:
           lowercase__									=    self.get_inputs()
           # resize to resolution that is divisible by 8 but not 16 or 32
           lowercase__									=    inputs["image"].resize((504, 504)   )

           lowercase__									=    "timbrooks/instruct-pix2pix"
           lowercase__									=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
               UpperCAmelCase_  ,safety_checker=UpperCAmelCase_  ,)
           pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
           pipe.enable_attention_slicing()

           lowercase__									=    pipe(**UpperCAmelCase_   )
           lowercase__									=    output.images[0]

           lowercase__									=    image[255:258, 383:386, -1]

           assert image.shape == (504, 504, 3)
           lowercase__									=    np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




