
'''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 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						CycleDiffusionPipeline
     __magic_name__          =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''negative_prompt''',
         '''height''',
         '''width''',
         '''negative_prompt_embeds''',
     }
     __magic_name__          =						PipelineTesterMixin.required_optional_params - {'''latents'''}
     __magic_name__          =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}		)
     __magic_name__          =						IMAGE_TO_IMAGE_IMAGE_PARAMS
     __magic_name__          =						IMAGE_TO_IMAGE_IMAGE_PARAMS





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         torch.manual_seed(0      )
         A			:  int   =		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			:  List[Any]   =		DDIMScheduler(
             beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='''scaled_linear'''		,     num_train_timesteps=1000		,     clip_sample=SCREAMING_SNAKE_CASE		,     set_alpha_to_one=SCREAMING_SNAKE_CASE		,     )
         torch.manual_seed(0      )
         A			:  int   =		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			:  int   =		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		,     )
         A			:  Any   =		CLIPTextModel(SCREAMING_SNAKE_CASE      )
         A			:  int   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=0      )					->							List[str]:






         """simple docstring"""






         A			:  Dict   =		floats_tensor((1, 3, 32, 32)		,     rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
         A			:  int   =		image / 2 + 0.5
         if str(SCREAMING_SNAKE_CASE      ).startswith('''mps'''      ):
             A			:  Optional[int]   =		torch.manual_seed(SCREAMING_SNAKE_CASE      )
         else:
             A			:  Optional[int]   =		torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
         A			:  Union[str, 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      )					->							List[str]:






         """simple docstring"""






         A			:  List[Any]   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         A			:  int   =		self.get_dummy_components()
         A			:  Any   =		CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		pipe.to(SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

         A			:  Any   =		images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         A			:  int   =		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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






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

         A			:  Union[str, Any]   =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
         A			:  str   =		pipe(**SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		output.images

         A			:  List[Any]   =		images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         A			:  Optional[Any]   =		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 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return super().test_save_load_local()





     @unittest.skip('''non-deterministic pipeline'''      )
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         return super().test_inference_batch_single_identical()





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






         """simple docstring"""






         return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return super().test_save_load_optional_components()





     @skip_mps
     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         return super().test_attention_slicing_forward_pass()





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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






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

         A			:  Dict   =		'''CompVis/stable-diffusion-v1-4'''
         A			:  int   =		DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE		,     subfolder='''scheduler'''      )
         A			:  Tuple   =		CycleDiffusionPipeline.from_pretrained(
             SCREAMING_SNAKE_CASE		,     scheduler=SCREAMING_SNAKE_CASE		,     safety_checker=SCREAMING_SNAKE_CASE		,     torch_dtype=torch.floataa		,     revision='''fp16'''      )

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

         A			:  Optional[int]   =		'''A black colored car'''
         A			:  List[str]   =		'''A blue colored car'''

         A			:  str   =		torch.manual_seed(0      )
         A			:  Union[str, Any]   =		pipe(
             prompt=SCREAMING_SNAKE_CASE		,     source_prompt=SCREAMING_SNAKE_CASE		,     image=SCREAMING_SNAKE_CASE		,     num_inference_steps=100		,     eta=0.1		,     strength=0.85		,     guidance_scale=3		,     source_guidance_scale=1		,     generator=SCREAMING_SNAKE_CASE		,     output_type='''np'''		,     )
         A			:  Optional[int]   =		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      )					->							Optional[Any]:






         """simple docstring"""






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

         A			:  Dict   =		'''CompVis/stable-diffusion-v1-4'''
         A			:  Dict   =		DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE		,     subfolder='''scheduler'''      )
         A			:  List[Any]   =		CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE		,     scheduler=SCREAMING_SNAKE_CASE		,     safety_checker=SCREAMING_SNAKE_CASE      )

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

         A			:  Optional[int]   =		'''A black colored car'''
         A			:  Any   =		'''A blue colored car'''

         A			:  str   =		torch.manual_seed(0      )
         A			:  Any   =		pipe(
             prompt=SCREAMING_SNAKE_CASE		,     source_prompt=SCREAMING_SNAKE_CASE		,     image=SCREAMING_SNAKE_CASE		,     num_inference_steps=100		,     eta=0.1		,     strength=0.85		,     guidance_scale=3		,     source_guidance_scale=1		,     generator=SCREAMING_SNAKE_CASE		,     output_type='''np'''		,     )
         A			:  int   =		output.images

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






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




