


# 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 A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  List[str]      =StableDiffusionControlNetImgaImgPipeline
      UpperCamelCase__							:  Optional[Any]      =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      UpperCamelCase__							:  Any      =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase__							:  Optional[int]      =IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}     )
      UpperCamelCase__							:  Any      =IMAGE_TO_IMAGE_IMAGE_PARAMS






      def        lowerCamelCase       (					self						:     Tuple )					->						List[Any]:
             """simple docstring"""




             torch.manual_seed(0 )
             _lowerCamelCase       :		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 )
             _lowerCamelCase       :		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)			,   )
             torch.manual_seed(0 )
             _lowerCamelCase       :		Optional[int]		=DDIMScheduler(
                 beta_start=0.00085			,   beta_end=0.012			,   beta_schedule='scaled_linear'			,   clip_sample=lowercase_			,   set_alpha_to_one=lowercase_			,   )
             torch.manual_seed(0 )
             _lowerCamelCase       :		Any		=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 )
             _lowerCamelCase       :		Optional[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=1000			,   )
             _lowerCamelCase       :		str		=CLIPTextModel(lowercase_ )
             _lowerCamelCase       :		Any		=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

             _lowerCamelCase       :		Tuple		={
                 'unet': unet,
                 'controlnet': controlnet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }
             return components






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     List[Any]			,   lowercase_						:     Optional[Any]=0 )					->						Tuple:
             """simple docstring"""




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

             _lowerCamelCase       :		Tuple		=2
             _lowerCamelCase       :		Optional[Any]		=randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,   generator=lowercase_			,   device=torch.device(lowercase_ )			,   )
             _lowerCamelCase       :		Union[str, Any]		=floats_tensor(control_image.shape			,   rng=random.Random(lowercase_ ) ).to(lowercase_ )
             _lowerCamelCase       :		Optional[int]		=image.cpu().permute(0			,   2			,   3			,   1 )[0]
             _lowerCamelCase       :		Any		=Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )
             _lowerCamelCase       :		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        lowerCamelCase       (					self						:     Optional[int] )					->						List[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        lowerCamelCase       (					self						:     Optional[int] )					->						Optional[int]:
             """simple docstring"""




             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             self._test_inference_batch_single_identical(expected_max_diff=2E-3 )




class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Tuple      =StableDiffusionControlNetImgaImgPipeline
      UpperCamelCase__							:  List[str]      =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      UpperCamelCase__							:  Optional[Any]      =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase__							:  Union[str, Any]      =frozenset([]     )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             torch.manual_seed(0 )
             _lowerCamelCase       :		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_						:     Optional[int] ):
                    if isinstance(lowercase_			,   torch.nn.Convad ):
                           torch.nn.init.normal(m.weight )
                           m.bias.data.fill_(1.0 )

             _lowerCamelCase       :		Optional[Any]		=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 )
             _lowerCamelCase       :		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)			,   )
             controlneta.controlnet_down_blocks.apply(lowercase_ )

             torch.manual_seed(0 )
             _lowerCamelCase       :		str		=DDIMScheduler(
                 beta_start=0.00085			,   beta_end=0.012			,   beta_schedule='scaled_linear'			,   clip_sample=lowercase_			,   set_alpha_to_one=lowercase_			,   )
             torch.manual_seed(0 )
             _lowerCamelCase       :		Optional[Any]		=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 )
             _lowerCamelCase       :		List[str]		=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			,   )
             _lowerCamelCase       :		int		=CLIPTextModel(lowercase_ )
             _lowerCamelCase       :		Tuple		=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

             _lowerCamelCase       :		str		=MultiControlNetModel([controlneta, controlneta] )

             _lowerCamelCase       :		Union[str, Any]		={
                 'unet': unet,
                 'controlnet': controlnet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }
             return components






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     Tuple			,   lowercase_						:     Dict=0 )					->						Tuple:
             """simple docstring"""




             if str(lowercase_ ).startswith('mps' ):
                    _lowerCamelCase       :		Optional[Any]		=torch.manual_seed(lowercase_ )
             else:
                    _lowerCamelCase       :		str		=torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

             _lowerCamelCase       :		Optional[int]		=2

             _lowerCamelCase       :		Optional[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_ )			,   ),
             ]

             _lowerCamelCase       :		List[str]		=floats_tensor(control_image[0].shape			,   rng=random.Random(lowercase_ ) ).to(lowercase_ )
             _lowerCamelCase       :		List[Any]		=image.cpu().permute(0			,   2			,   3			,   1 )[0]
             _lowerCamelCase       :		int		=Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )
             _lowerCamelCase       :		Union[str, 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        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=self.get_dummy_components()
             _lowerCamelCase       :		Optional[int]		=self.pipeline_class(**lowercase_ )
             pipe.to(lowercase_ )

             _lowerCamelCase       :		Any		=10.0
             _lowerCamelCase       :		Any		=4

             _lowerCamelCase       :		List[str]		=self.get_dummy_inputs(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=steps
             _lowerCamelCase       :		Union[str, Any]		=scale
             _lowerCamelCase       :		Optional[int]		=pipe(**lowercase_ )[0]

             _lowerCamelCase       :		List[Any]		=self.get_dummy_inputs(lowercase_ )
             _lowerCamelCase       :		Any		=steps
             _lowerCamelCase       :		int		=scale
             _lowerCamelCase       :		Dict		=pipe(**lowercase_			,   control_guidance_start=0.1			,   control_guidance_end=0.2 )[0]

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

             _lowerCamelCase       :		int		=self.get_dummy_inputs(lowercase_ )
             _lowerCamelCase       :		List[Any]		=steps
             _lowerCamelCase       :		Tuple		=scale
             _lowerCamelCase       :		Dict		=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        lowerCamelCase       (					self						:     int )					->						Tuple:
             """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        lowerCamelCase       (					self						:     Dict )					->						str:
             """simple docstring"""




             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             self._test_inference_batch_single_identical(expected_max_diff=2E-3 )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.get_dummy_components()
             _lowerCamelCase       :		List[str]		=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 A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Tuple )					->						List[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny' )

             _lowerCamelCase       :		List[str]		=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_ )

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

             _lowerCamelCase       :		Dict		=pipe(
                 lowercase_			,   lowercase_			,   control_image=lowercase_			,   generator=lowercase_			,   output_type='np'			,   num_inference_steps=50			,   strength=0.6			,   )

             _lowerCamelCase       :		Dict		=output.images[0]

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

             _lowerCamelCase       :		Optional[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






def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


