





"""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 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



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

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


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						Union[str, Any]       =						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						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Dict       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						int       =						DDIMScheduler(
            beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='''scaled_linear'''						,				clip_sample=__lowercase						,				set_alpha_to_one=__lowercase						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Dict       =						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    :						Dict       =						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=1_000						,				)
        __UpperCAmelCase    :						List[Any]       =						CLIPTextModel(__lowercase )
        __UpperCAmelCase    :						Dict       =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        __UpperCAmelCase    :						List[Any]       =						{
            '''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       :      Union[str, Any]						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Tuple=0 ):


        '''simple docstring'''




        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						List[str]       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						List[str]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )

        __UpperCAmelCase    :						Dict       =						2
        __UpperCAmelCase    :						int       =						randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				)
        __UpperCAmelCase    :						Union[str, Any]       =						floats_tensor(control_image.shape						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        __UpperCAmelCase    :						Dict       =						image.cpu().permute(0						,				2						,				3						,				1 )[0]
        __UpperCAmelCase    :						Tuple       =						Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((64, 64) )
        __UpperCAmelCase    :						Optional[int]       =						{
            '''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       :      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       :      List[str] ):


        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self._test_inference_batch_single_identical(expected_max_diff=2e-3 )




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



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

      def       A_					(							self       :      int ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						Tuple       =						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						,				)
        torch.manual_seed(0 )

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

        __UpperCAmelCase    :						str       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        controlneta.controlnet_down_blocks.apply(__lowercase )

        torch.manual_seed(0 )
        __UpperCAmelCase    :						str       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        controlneta.controlnet_down_blocks.apply(__lowercase )

        torch.manual_seed(0 )
        __UpperCAmelCase    :						List[Any]       =						DDIMScheduler(
            beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='''scaled_linear'''						,				clip_sample=__lowercase						,				set_alpha_to_one=__lowercase						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Optional[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 )
        __UpperCAmelCase    :						Union[str, Any]       =						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=1_000						,				)
        __UpperCAmelCase    :						int       =						CLIPTextModel(__lowercase )
        __UpperCAmelCase    :						int       =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        __UpperCAmelCase    :						List[Any]       =						MultiControlNetModel([controlneta, controlneta] )

        __UpperCAmelCase    :						str       =						{
            '''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       :      Any						,				__lowercase       :      int						,				__lowercase       :      Optional[int]=0 ):


        '''simple docstring'''




        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						Any       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						Union[str, Any]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						2

        __UpperCAmelCase    :						List[Any]       =						[
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				),
        ]

        __UpperCAmelCase    :						Optional[Any]       =						floats_tensor(control_image[0].shape						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        __UpperCAmelCase    :						List[str]       =						image.cpu().permute(0						,				2						,				3						,				1 )[0]
        __UpperCAmelCase    :						Tuple       =						Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((64, 64) )
        __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       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_dummy_components()
        __UpperCAmelCase    :						Any       =						self.pipeline_class(**__lowercase )
        pipe.to(__lowercase )

        __UpperCAmelCase    :						Dict       =						1_0.0
        __UpperCAmelCase    :						int       =						4

        __UpperCAmelCase    :						Dict       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						steps
        __UpperCAmelCase    :						str       =						scale
        __UpperCAmelCase    :						List[Any]       =						pipe(**__lowercase )[0]

        __UpperCAmelCase    :						Optional[Any]       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						steps
        __UpperCAmelCase    :						Any       =						scale
        __UpperCAmelCase    :						int       =						pipe(**__lowercase						,				control_guidance_start=0.1						,				control_guidance_end=0.2 )[0]

        __UpperCAmelCase    :						Optional[int]       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						steps
        __UpperCAmelCase    :						str       =						scale
        __UpperCAmelCase    :						Optional[int]       =						pipe(**__lowercase						,				control_guidance_start=[0.1, 0.3]						,				control_guidance_end=[0.2, 0.7] )[0]

        __UpperCAmelCase    :						int       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						Dict       =						steps
        __UpperCAmelCase    :						Tuple       =						scale
        __UpperCAmelCase    :						Any       =						pipe(**__lowercase						,				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       :      int ):


        '''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 )







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						self.get_dummy_components()
        __UpperCAmelCase    :						Tuple       =						self.pipeline_class(**__lowercase )
        pipe.to(__lowercase )
        pipe.set_progress_bar_config(disable=__lowercase )
        with tempfile.TemporaryDirectory() as tmpdir:
          try:
            # save_pretrained is not implemented for Multi-ControlNet
            pipe.save_pretrained(__lowercase )
          except NotImplementedError:
            pass




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

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




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







      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''' )

        __UpperCAmelCase    :						Tuple       =						StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''						,				safety_checker=__lowercase						,				controlnet=__lowercase )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.Generator(device='''cpu''' ).manual_seed(0 )
        __UpperCAmelCase    :						str       =						'''evil space-punk bird'''
        __UpperCAmelCase    :						int       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''' ).resize((512, 512) )
        __UpperCAmelCase    :						Any       =						load_image(
            '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png''' ).resize((512, 512) )

        __UpperCAmelCase    :						Optional[int]       =						pipe(
            __lowercase						,				__lowercase						,				control_image=__lowercase						,				generator=__lowercase						,				output_type='''np'''						,				num_inference_steps=50						,				strength=0.6						,				)

        __UpperCAmelCase    :						List[str]       =						output.images[0]

        assert image.shape == (512, 512, 3)

        __UpperCAmelCase    :						int       =						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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


  return sorted(UpperCAmelCase_  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			UpperCAmelCase_  ):
    for j in range(max(0  ,			i - 6  )  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						Dict       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(UpperCAmelCase_  ,			UpperCAmelCase_  )

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))