


'''simple docstring'''







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  lowerCAmelCase_						(       _lowercase		,    _lowercase		,    _lowercase		,    unittest.TestCase      ):





		"""simple docstring"""



		a_       :Dict												=StableDiffusionControlNetImgaImgPipeline
		a_       :Optional[Any]												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		a_       :List[str]												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		a_       :Optional[int]												=IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}      )
		a_       :Optional[Any]												=IMAGE_TO_IMAGE_IMAGE_PARAMS

		def __a						(					self     :							Dict						):





							'''simple docstring'''

							torch.manual_seed(0						)
							__a       =   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						)
							__a       =   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						)
							__a       =   DDIMScheduler(
							    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule="""scaled_linear"""				,							clip_sample=A_				,							set_alpha_to_one=A_				,							)
							torch.manual_seed(0						)
							__a       =   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						)
							__a       =   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				,							)
							__a       =   CLIPTextModel(A_						)
							__a       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

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

		def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=0						):





							'''simple docstring'''

							if str(A_						).startswith("""mps"""						):
												__a       =   torch.manual_seed(A_						)
							else:
												__a       =   torch.Generator(device=A_						).manual_seed(A_						)

							__a       =   2
							__a       =   randn_tensor(
							    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)				,							generator=A_				,							device=torch.device(A_						)				,							)
							__a       =   floats_tensor(control_image.shape				,							rng=random.Random(A_						)						).to(A_						)
							__a       =   image.cpu().permute(0				,							2				,							3				,							1						)[0]
							__a       =   Image.fromarray(np.uinta(A_						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
							__a       =   {
							    """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 __a						(					self     :							List[str]						):





							'''simple docstring'''

							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 __a						(					self     :							Optional[Any]						):





							'''simple docstring'''

							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)


		def __a						(					self     :							Tuple						):





							'''simple docstring'''

							self._test_inference_batch_single_identical(expected_max_diff=2E-3						)


class  lowerCAmelCase_						(       _lowercase		,    _lowercase		,    unittest.TestCase      ):





		"""simple docstring"""



		a_       :Tuple												=StableDiffusionControlNetImgaImgPipeline
		a_       :Dict												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		a_       :int												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		a_       :Optional[int]												=frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

		def __a						(					self     :							Any						):





							'''simple docstring'''

							torch.manual_seed(0						)
							__a       =   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(SCREAMING_SNAKE_CASE__     :							Tuple						):
												if isinstance(A_				,							torch.nn.Convad						):
																	torch.nn.init.normal(m.weight						)
																	m.bias.data.fill_(1.0						)

							__a       =   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						)
							__a       =   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						)
							__a       =   DDIMScheduler(
							    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule="""scaled_linear"""				,							clip_sample=A_				,							set_alpha_to_one=A_				,							)
							torch.manual_seed(0						)
							__a       =   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						)
							__a       =   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				,							)
							__a       =   CLIPTextModel(A_						)
							__a       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							__a       =   MultiControlNetModel([controlneta, controlneta]						)

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

		def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0						):





							'''simple docstring'''

							if str(A_						).startswith("""mps"""						):
												__a       =   torch.manual_seed(A_						)
							else:
												__a       =   torch.Generator(device=A_						).manual_seed(A_						)

							__a       =   2

							__a       =   [
							    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_						)				,							),
							]

							__a       =   floats_tensor(control_image[0].shape				,							rng=random.Random(A_						)						).to(A_						)
							__a       =   image.cpu().permute(0				,							2				,							3				,							1						)[0]
							__a       =   Image.fromarray(np.uinta(A_						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
							__a       =   {
							    """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 __a						(					self     :							List[Any]						):





							'''simple docstring'''

							__a       =   self.get_dummy_components()
							__a       =   self.pipeline_class(**A_						)
							pipe.to(A_						)

							__a       =   1_0.0
							__a       =   4

							__a       =   self.get_dummy_inputs(A_						)
							__a       =   steps
							__a       =   scale
							__a       =   pipe(**A_						)[0]

							__a       =   self.get_dummy_inputs(A_						)
							__a       =   steps
							__a       =   scale
							__a       =   pipe(**A_				,							control_guidance_start=0.1				,							control_guidance_end=0.2						)[0]

							__a       =   self.get_dummy_inputs(A_						)
							__a       =   steps
							__a       =   scale
							__a       =   pipe(**A_				,							control_guidance_start=[0.1, 0.3]				,							control_guidance_end=[0.2, 0.7]						)[0]

							__a       =   self.get_dummy_inputs(A_						)
							__a       =   steps
							__a       =   scale
							__a       =   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 __a						(					self     :							Union[str, Any]						):





							'''simple docstring'''

							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 __a						(					self     :							Optional[int]						):





							'''simple docstring'''

							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)

		def __a						(					self     :							Union[str, Any]						):





							'''simple docstring'''

							self._test_inference_batch_single_identical(expected_max_diff=2E-3						)


		def __a						(					self     :							Union[str, Any]						):





							'''simple docstring'''

							__a       =   self.get_dummy_components()
							__a       =   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  lowerCAmelCase_						(       unittest.TestCase      ):





		"""simple docstring"""



		def __a						(					self     :							List[Any]						):





							'''simple docstring'''

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


		def __a						(					self     :							List[str]						):





							'''simple docstring'''

							__a       =   ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""						)

							__a       =   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_						)

							__a       =   torch.Generator(device="""cpu"""						).manual_seed(0						)
							__a       =   """evil space-punk bird"""
							__a       =   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)						)
							__a       =   load_image(
							    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""						).resize((5_1_2, 5_1_2)						)

							__a       =   pipe(
							    A_				,							A_				,							control_image=A_				,							generator=A_				,							output_type="""np"""				,							num_inference_steps=5_0				,							strength=0.6				,							)

							__a       =   output.images[0]

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

							__a       =   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'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






