

"""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						(       _lowerCamelCase,	_lowerCamelCase,	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: Union[str, Any]       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  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__										=  DDIMScheduler(
             beta_start=0.00_085    ,						beta_end=0.012    ,						beta_schedule="""scaled_linear"""    ,						num_train_timesteps=10_00    ,						clip_sample=UpperCamelCase    ,						set_alpha_to_one=UpperCamelCase    ,						)
         torch.manual_seed(0       )
         A__										=  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__										=  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=10_00    ,						)
         A__										=  CLIPTextModel(UpperCamelCase       )
         A__										=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

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

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Dict=0       ):






         """simple docstring"""


         A__										=  floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(UpperCamelCase       )       ).to(UpperCamelCase       )
         A__										=  image / 2 + 0.5
         if str(UpperCamelCase       ).startswith("""mps"""       ):
              A__										=  torch.manual_seed(UpperCamelCase       )
         else:
              A__										=  torch.Generator(device=UpperCamelCase       ).manual_seed(UpperCamelCase       )
         A__										=  {
             """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: Dict       ):






         """simple docstring"""


         A__										=  """cpu"""  # ensure determinism for the device-dependent torch.Generator

         A__										=  self.get_dummy_components()
         A__										=  CycleDiffusionPipeline(**UpperCamelCase       )
         A__										=  pipe.to(UpperCamelCase       )
         pipe.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  self.get_dummy_inputs(UpperCamelCase       )
         A__										=  pipe(**UpperCamelCase       )
         A__										=  output.images

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

         assert images.shape == (1, 32, 32, 3)
         A__										=  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 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  self.get_dummy_components()
         for name, module in components.items():
              if hasattr(UpperCamelCase    ,						"""half"""       ):
                   A__										=  module.half()
         A__										=  CycleDiffusionPipeline(**UpperCamelCase       )
         A__										=  pipe.to(UpperCamelCase       )
         pipe.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  self.get_dummy_inputs(UpperCamelCase       )
         A__										=  pipe(**UpperCamelCase       )
         A__										=  output.images

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

         assert images.shape == (1, 32, 32, 3)
         A__										=  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 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         return super().test_save_load_local()

    @unittest.skip("""non-deterministic pipeline"""       )
    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         return super().test_inference_batch_single_identical()

    @skip_mps
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()

    @skip_mps
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         return super().test_save_load_optional_components()





    @skip_mps
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()



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



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/cycle-diffusion/black_colored_car.png"""       )
         A__										=  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""       )
         A__										=  init_image.resize((5_12, 5_12)       )

         A__										=  """CompVis/stable-diffusion-v1-4"""
         A__										=  DDIMScheduler.from_pretrained(UpperCamelCase    ,						subfolder="""scheduler"""       )
         A__										=  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()

         A__										=  """A black colored car"""
         A__										=  """A blue colored car"""

         A__										=  torch.manual_seed(0       )
         A__										=  pipe(
             prompt=UpperCamelCase    ,						source_prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						num_inference_steps=1_00    ,						eta=0.1    ,						strength=0.85    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=UpperCamelCase    ,						output_type="""np"""    ,						)
         A__										=  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: Dict       ):






         """simple docstring"""


         A__										=  load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/cycle-diffusion/black_colored_car.png"""       )
         A__										=  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""       )
         A__										=  init_image.resize((5_12, 5_12)       )

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

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

         A__										=  """A black colored car"""
         A__										=  """A blue colored car"""

         A__										=  torch.manual_seed(0       )
         A__										=  pipe(
             prompt=UpperCamelCase    ,						source_prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						num_inference_steps=1_00    ,						eta=0.1    ,						strength=0.85    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=UpperCamelCase    ,						output_type="""np"""    ,						)
         A__										=  output.images

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








"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					list			):
     if len(UpperCAmelCase_			) <= 1:
          return lst

     A__										=  1

     while i < len(UpperCAmelCase_			):
          if lst[i - 1] <= lst[i]:
               i += 1
          else:
               A__			,				A__										=  lst[i], lst[i - 1]
               i -= 1
               if i == 0:
                    A__										=  1

     return lst


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						input('Enter numbers separated by a comma:\n').strip()
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[int(item) for item in user_input.split(',')]
 print(gnome_sort(unsorted))






