



"""simple docstring"""




# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			StableDiffusionControlNetImgaImgPipeline
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      __SCREAMING_SNAKE_CASE						:       List[str]								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      __SCREAMING_SNAKE_CASE						:       Any								=			IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}						)
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			IMAGE_TO_IMAGE_IMAGE_PARAMS



      def 						snake_case_ (					self					:     List[str]						):
           torch.manual_seed(0						)
           _UpperCAmelCase      :     List[str]        =   UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)					,     layers_per_block=2					,     sample_size=3_2					,     in_channels=4					,     out_channels=4					,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,     cross_attention_dim=3_2					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     int        =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,     layers_per_block=2					,     in_channels=4					,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,     cross_attention_dim=3_2					,     conditioning_embedding_out_channels=(1_6, 3_2)					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     Union[str, Any]        =   DDIMScheduler(
               beta_start=0.00_085					,     beta_end=0.012					,     beta_schedule="scaled_linear"					,     clip_sample=A					,     set_alpha_to_one=A					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     List[str]        =   AutoencoderKL(
               block_out_channels=[3_2, 6_4]					,     in_channels=3					,     out_channels=3					,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,     latent_channels=4					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     str        =   CLIPTextConfig(
               bos_token_id=0					,     eos_token_id=2					,     hidden_size=3_2					,     intermediate_size=3_7					,     layer_norm_eps=1e-05					,     num_attention_heads=4					,     num_hidden_layers=5					,     pad_token_id=1					,     vocab_size=1_0_0_0					,     )
           _UpperCAmelCase      :     Union[str, Any]        =   CLIPTextModel(A						)
           _UpperCAmelCase      :     Any        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

           _UpperCAmelCase      :     List[str]        =   {
               "unet": unet,
               "controlnet": controlnet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components



      def 						snake_case_ (					self					:     List[str]					,     A					:     int					,     A					:     List[Any]=0						):
           if str(A						).startswith("mps"						):
                _UpperCAmelCase      :     Dict        =   torch.manual_seed(A						)
           else:
                _UpperCAmelCase      :     Any        =   torch.Generator(device=A						).manual_seed(A						)

           _UpperCAmelCase      :     int        =   2
           _UpperCAmelCase      :     List[Any]        =   randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,     generator=A					,     device=torch.device(A						)					,     )
           _UpperCAmelCase      :     Optional[Any]        =   floats_tensor(control_image.shape					,     rng=random.Random(A						)						).to(A						)
           _UpperCAmelCase      :     List[Any]        =   image.cpu().permute(0					,     2					,     3					,     1						)[0]
           _UpperCAmelCase      :     Dict        =   Image.fromarray(np.uinta(A						)						).convert("RGB"						).resize((6_4, 6_4)						)
           _UpperCAmelCase      :     Any        =   {
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
               "image": image,
               "control_image": control_image,
           }

           return inputs



      def 						snake_case_ (					self					:     Optional[int]						):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)



      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()					,     reason="XFormers attention is only available with CUDA and `xformers` installed"					,     )
      def 						snake_case_ (					self					:     Union[str, Any]						):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)



      def 						snake_case_ (					self					:     List[str]						):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			StableDiffusionControlNetImgaImgPipeline
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      __SCREAMING_SNAKE_CASE						:       List[str]								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      __SCREAMING_SNAKE_CASE						:       int								=			frozenset([]						)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



      def 						snake_case_ (					self					:     str						):
           torch.manual_seed(0						)
           _UpperCAmelCase      :     Optional[Any]        =   UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)					,     layers_per_block=2					,     sample_size=3_2					,     in_channels=4					,     out_channels=4					,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,     cross_attention_dim=3_2					,     )
           torch.manual_seed(0						)

           def init_weights(A					:     int						):
                if isinstance(A					,     torch.nn.Convad						):
                     torch.nn.init.normal(m.weight						)
                     m.bias.data.fill_(1.0						)

           _UpperCAmelCase      :     Optional[int]        =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,     layers_per_block=2					,     in_channels=4					,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,     cross_attention_dim=3_2					,     conditioning_embedding_out_channels=(1_6, 3_2)					,     )
           controlneta.controlnet_down_blocks.apply(A						)

           torch.manual_seed(0						)
           _UpperCAmelCase      :     int        =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,     layers_per_block=2					,     in_channels=4					,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,     cross_attention_dim=3_2					,     conditioning_embedding_out_channels=(1_6, 3_2)					,     )
           controlneta.controlnet_down_blocks.apply(A						)

           torch.manual_seed(0						)
           _UpperCAmelCase      :     Any        =   DDIMScheduler(
               beta_start=0.00_085					,     beta_end=0.012					,     beta_schedule="scaled_linear"					,     clip_sample=A					,     set_alpha_to_one=A					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     Optional[Any]        =   AutoencoderKL(
               block_out_channels=[3_2, 6_4]					,     in_channels=3					,     out_channels=3					,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,     latent_channels=4					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     Tuple        =   CLIPTextConfig(
               bos_token_id=0					,     eos_token_id=2					,     hidden_size=3_2					,     intermediate_size=3_7					,     layer_norm_eps=1e-05					,     num_attention_heads=4					,     num_hidden_layers=5					,     pad_token_id=1					,     vocab_size=1_0_0_0					,     )
           _UpperCAmelCase      :     Tuple        =   CLIPTextModel(A						)
           _UpperCAmelCase      :     Optional[int]        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

           _UpperCAmelCase      :     Optional[Any]        =   MultiControlNetModel([controlneta, controlneta]						)

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



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     str					,     A					:     Optional[Any]=0						):
           if str(A						).startswith("mps"						):
                _UpperCAmelCase      :     Dict        =   torch.manual_seed(A						)
           else:
                _UpperCAmelCase      :     Any        =   torch.Generator(device=A						).manual_seed(A						)

           _UpperCAmelCase      :     Optional[int]        =   2

           _UpperCAmelCase      :     Any        =   [
               randn_tensor(
                   (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,     generator=A					,     device=torch.device(A						)					,     ),
               randn_tensor(
                   (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,     generator=A					,     device=torch.device(A						)					,     ),
           ]

           _UpperCAmelCase      :     int        =   floats_tensor(control_image[0].shape					,     rng=random.Random(A						)						).to(A						)
           _UpperCAmelCase      :     List[Any]        =   image.cpu().permute(0					,     2					,     3					,     1						)[0]
           _UpperCAmelCase      :     str        =   Image.fromarray(np.uinta(A						)						).convert("RGB"						).resize((6_4, 6_4)						)
           _UpperCAmelCase      :     Dict        =   {
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
               "image": image,
               "control_image": control_image,
           }

           return inputs



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Dict        =   self.get_dummy_components()
           _UpperCAmelCase      :     List[Any]        =   self.pipeline_class(**A						)
           pipe.to(A						)

           _UpperCAmelCase      :     Optional[int]        =   10.0
           _UpperCAmelCase      :     Union[str, Any]        =   4

           _UpperCAmelCase      :     List[str]        =   self.get_dummy_inputs(A						)
           _UpperCAmelCase      :     Dict        =   steps
           _UpperCAmelCase      :     Any        =   scale
           _UpperCAmelCase      :     int        =   pipe(**A						)[0]

           _UpperCAmelCase      :     Optional[int]        =   self.get_dummy_inputs(A						)
           _UpperCAmelCase      :     str        =   steps
           _UpperCAmelCase      :     int        =   scale
           _UpperCAmelCase      :     Dict        =   pipe(**A					,     control_guidance_start=0.1					,     control_guidance_end=0.2						)[0]

           _UpperCAmelCase      :     Tuple        =   self.get_dummy_inputs(A						)
           _UpperCAmelCase      :     int        =   steps
           _UpperCAmelCase      :     List[str]        =   scale
           _UpperCAmelCase      :     str        =   pipe(**A					,     control_guidance_start=[0.1, 0.3]					,     control_guidance_end=[0.2, 0.7]						)[0]

           _UpperCAmelCase      :     Optional[Any]        =   self.get_dummy_inputs(A						)
           _UpperCAmelCase      :     Dict        =   steps
           _UpperCAmelCase      :     Optional[int]        =   scale
           _UpperCAmelCase      :     Dict        =   pipe(**A					,     control_guidance_start=0.4					,     control_guidance_end=[0.5, 0.8]						)[0]

           # make sure that all outputs are different
           assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
           assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
           assert np.sum(np.abs(output_a - output_a						)						) > 1e-3



      def 						snake_case_ (					self					:     Dict						):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)



      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()					,     reason="XFormers attention is only available with CUDA and `xformers` installed"					,     )
      def 						snake_case_ (					self					:     str						):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Dict        =   self.get_dummy_components()
           _UpperCAmelCase      :     Tuple        =   self.pipeline_class(**A						)
           pipe.to(A						)
           pipe.set_progress_bar_config(disable=A						)
           with tempfile.TemporaryDirectory() as tmpdir:
                try:
                     # save_pretrained is not implemented for Multi-ControlNet
                     pipe.save_pretrained(A						)
                except NotImplementedError:
                     pass







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



      def 						snake_case_ (					self					:     Any						):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     str        =   ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"						)

           _UpperCAmelCase      :     str        =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"					,     safety_checker=A					,     controlnet=A						)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=A						)

           _UpperCAmelCase      :     Any        =   torch.Generator(device="cpu"						).manual_seed(0						)
           _UpperCAmelCase      :     Any        =   "evil space-punk bird"
           _UpperCAmelCase      :     Union[str, Any]        =   load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"						).resize((5_1_2, 5_1_2)						)
           _UpperCAmelCase      :     Any        =   load_image(
               "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"						).resize((5_1_2, 5_1_2)						)

           _UpperCAmelCase      :     Optional[Any]        =   pipe(
               A					,     A					,     control_image=A					,     generator=A					,     output_type="np"					,     num_inference_steps=5_0					,     strength=0.6					,     )

           _UpperCAmelCase      :     Optional[Any]        =   output.images[0]

           assert image.shape == (5_1_2, 5_1_2, 3)

           _UpperCAmelCase      :     Dict        =   load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"						)

           assert np.abs(expected_image - image						).max() < 9e-2





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list    )  ->       list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__    ) < 2:
          return collection

     def circle_sort_util(SCREAMING_SNAKE_CASE__     :						list						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int    ) -> bool:
          _UpperCAmelCase      :     Dict        =   False

          if low == high:
               return swapped

          _UpperCAmelCase      :     str        =   low
          _UpperCAmelCase      :     Dict        =   high

          while left < right:
               if collection[left] > collection[right]:
                    _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   (
                        collection[right],
                        collection[left],
                    )
                    _UpperCAmelCase      :     Optional[int]        =   True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
                   collection[right + 1],
                   collection[left],
               )

               _UpperCAmelCase      :     Union[str, Any]        =   True

          _UpperCAmelCase      :     Dict        =   low + int((high - low) / 2    )
          _UpperCAmelCase      :     Any        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Optional[Any]        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  mid + 1						,  SCREAMING_SNAKE_CASE__    )

          return swapped or left_swap or right_swap

     _UpperCAmelCase      :     Tuple        =   True

     while is_not_sorted is True:
          _UpperCAmelCase      :     Dict        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  0						,  len(SCREAMING_SNAKE_CASE__    ) - 1    )

     return collection


if __name__ == "__main__":
 _lowerCAmelCase    : Dict							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : Optional[Any]							=							[int(item) for item in user_input.split(",")]
 print(circle_sort(unsorted))
