





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     snake_case_		(   a,		a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      CycleDiffusionPipeline
 __UpperCamelCase       =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 __UpperCamelCase       =      PipelineTesterMixin.required_optional_params - {'latents'}
 __UpperCamelCase       =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 __UpperCamelCase       =      IMAGE_TO_IMAGE_IMAGE_PARAMS
 __UpperCamelCase       =      IMAGE_TO_IMAGE_IMAGE_PARAMS

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    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=0.0_00_85,		beta_end=0.0_12,		beta_schedule="scaled_linear",		num_train_timesteps=1000,		clip_sample=A_,		set_alpha_to_one=A_,		)
    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-05,		num_attention_heads=4,		num_hidden_layers=5,		pad_token_id=1,		vocab_size=1000,		)
    UpperCAmelCase__						=CLIPTextModel(A_		)
    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,		A_,		A_=0		)	->       str:
    UpperCAmelCase__						=floats_tensor((1, 3, 32, 32),		rng=random.Random(A_		)		).to(A_		)
    UpperCAmelCase__						=image / 2 + 0.5
    if str(A_		).startswith("mps"		):
       UpperCAmelCase__						=torch.manual_seed(A_		)
    else:
       UpperCAmelCase__						=torch.Generator(device=A_		).manual_seed(A_		)
    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		)	->       Optional[Any]:
    UpperCAmelCase__						="cpu"  # ensure determinism for the device-dependent torch.Generator

    UpperCAmelCase__						=self.get_dummy_components()
    UpperCAmelCase__						=CycleDiffusionPipeline(**A_		)
    UpperCAmelCase__						=pipe.to(A_		)
    pipe.set_progress_bar_config(disable=A_		)

    UpperCAmelCase__						=self.get_dummy_inputs(A_		)
    UpperCAmelCase__						=pipe(**A_		)
    UpperCAmelCase__						=output.images

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

    assert images.shape == (1, 32, 32, 3)
    UpperCAmelCase__						=np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]		)

    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[Any]:
    UpperCAmelCase__						=self.get_dummy_components()
    for name, module in components.items():
       if hasattr(A_,		"half"		):
          UpperCAmelCase__						=module.half()
    UpperCAmelCase__						=CycleDiffusionPipeline(**A_		)
    UpperCAmelCase__						=pipe.to(A_		)
    pipe.set_progress_bar_config(disable=A_		)

    UpperCAmelCase__						=self.get_dummy_inputs(A_		)
    UpperCAmelCase__						=pipe(**A_		)
    UpperCAmelCase__						=output.images

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

    assert images.shape == (1, 32, 32, 3)
    UpperCAmelCase__						=np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]		)

    assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

 @skip_mps
 def  __UpperCAmelCase						(  self		)	->       str:
    return super().test_save_load_local()

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

 @skip_mps
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def  __UpperCAmelCase						(  self		)	->       int:
    return super().test_save_load_optional_components()






 @skip_mps
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    return super().test_attention_slicing_forward_pass()

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






 '''simple docstring'''





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

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    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(A_,		subfolder="scheduler"		)
    UpperCAmelCase__						=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()

    UpperCAmelCase__						="A black colored car"
    UpperCAmelCase__						="A blue colored car"

    UpperCAmelCase__						=torch.manual_seed(0		)
    UpperCAmelCase__						=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",		)
    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		)	->       Optional[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.npy"		)
    UpperCAmelCase__						=init_image.resize((512, 512)		)

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

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

    UpperCAmelCase__						="A black colored car"
    UpperCAmelCase__						="A blue colored car"

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

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










def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   for i in range(len(A     ) - 1				,							0				,							-1     ):
      UpperCAmelCase__						=False

      for j in range(A				,							0				,							-1     ):
         if unsorted[j] < unsorted[j - 1]:
            UpperCAmelCase__			,							UpperCAmelCase__						=unsorted[j - 1], unsorted[j]
            UpperCAmelCase__						=True

      for j in range(A     ):
         if unsorted[j] > unsorted[j + 1]:
            UpperCAmelCase__			,							UpperCAmelCase__						=unsorted[j + 1], unsorted[j]
            UpperCAmelCase__						=True

      if not swapped:
         break
   return unsorted


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCamelCase_												=	input('Enter numbers separated by a comma:\n').strip()
    UpperCamelCase_												=	[int(item) for item in user_input.split(',')]
    print(f"""{cocktail_shaker_sort(unsorted) = }""")



