





'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					KandinskyVaaControlnetImgaImgPipeline
     _UpperCAmelCase										=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     _UpperCAmelCase										=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     _UpperCAmelCase										=					[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     _UpperCAmelCase										=					False







     @property
     def     UpperCamelCase			( self    ) ->	Any:
      return 32







     @property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return 32







     @property
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      return self.time_input_dim







     @property
     def     UpperCamelCase			( self    ) ->	List[Any]:
      return self.time_input_dim * 4







     @property
     def     UpperCamelCase			( self    ) ->	List[str]:
      return 1_00







     @property
     def     UpperCamelCase			( self    ) ->	int:
      torch.manual_seed(0    )

      snake_case				=			{
          '''in_channels''': 8,
          # Out channels is double in channels because predicts mean and variance
          '''out_channels''': 8,
          '''addition_embed_type''': '''image_hint''',
          '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
          '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
          '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
          '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
          '''layers_per_block''': 1,
          '''encoder_hid_dim''': self.text_embedder_hidden_size,
          '''encoder_hid_dim_type''': '''image_proj''',
          '''cross_attention_dim''': self.cross_attention_dim,
          '''attention_head_dim''': 4,
          '''resnet_time_scale_shift''': '''scale_shift''',
          '''class_embed_type''': None,
      }

      snake_case				=			UNetaDConditionModel(**A__    )
      return model







     @property
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }







     @property
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      torch.manual_seed(0    )
      snake_case				=			VQModel(**self.dummy_movq_kwargs    )
      return model







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.dummy_unet
      snake_case				=			self.dummy_movq

      snake_case				=			{
          '''num_train_timesteps''': 10_00,
          '''beta_schedule''': '''linear''',
          '''beta_start''': 0.0_0_0_8_5,
          '''beta_end''': 0.0_1_2,
          '''clip_sample''': False,
          '''set_alpha_to_one''': False,
          '''steps_offset''': 0,
          '''prediction_type''': '''epsilon''',
          '''thresholding''': False,
      }

      snake_case				=			DDIMScheduler(**A__    )

      snake_case				=			{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Tuple:
      snake_case				=			floats_tensor((1, self.text_embedder_hidden_size)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, self.text_embedder_hidden_size)    ,   rng=random.Random(seed + 1    )    ).to(
          A__    )
      # create init_image
      snake_case				=			floats_tensor((1, 3, 64, 64)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			image.cpu().permute(0    ,   2    ,   3    ,   1    )[0]
      snake_case				=			Image.fromarray(np.uinta(A__    )    ).convert('''RGB'''    ).resize((2_56, 2_56)    )
      # create hint
      snake_case				=			floats_tensor((1, 3, 64, 64)    ,   rng=random.Random(A__    )    ).to(A__    )

      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )
      snake_case				=			{
          '''image''': init_image,
          '''image_embeds''': image_embeds,
          '''negative_image_embeds''': negative_image_embeds,
          '''hint''': hint,
          '''generator''': generator,
          '''height''': 64,
          '''width''': 64,
          '''num_inference_steps''': 10,
          '''guidance_scale''': 7.0,
          '''strength''': 0.2,
          '''output_type''': '''np''',
      }
      return inputs







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			'''cpu'''

      snake_case				=			self.get_dummy_components()

      snake_case				=			self.pipeline_class(**A__    )
      snake_case				=			pipe.to(A__    )

      pipe.set_progress_bar_config(disable=A__    )

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

      snake_case				=			pipe(
          **self.get_dummy_inputs(A__    )    ,   return_dict=A__    ,   )[0]

      snake_case				=			image[0, -3:, -3:, -1]
      snake_case				=			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      snake_case				=			np.array(
          [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]    )
      assert (
          np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
      ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
      ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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







     def     UpperCamelCase			( self    ) ->	Tuple:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''    )

      snake_case				=			load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
      snake_case				=			init_image.resize((5_12, 5_12)    )

      snake_case				=			load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/hint_image_cat.png'''    )
      snake_case				=			torch.from_numpy(np.array(A__    )    ).float() / 2_55.0
      snake_case				=			hint.permute(2    ,   0    ,   1    ).unsqueeze(0    )

      snake_case				=			'''A robot, 4k photo'''

      snake_case				=			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-prior'''    ,   torch_dtype=torch.floataa    )
      pipe_prior.to(A__    )

      snake_case				=			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-controlnet-depth'''    ,   torch_dtype=torch.floataa    )
      snake_case				=			pipeline.to(A__    )

      pipeline.set_progress_bar_config(disable=A__    )

      snake_case				=			torch.Generator(device='''cpu'''    ).manual_seed(0    )

      snake_case				,		snake_case				=			pipe_prior(
          A__    ,   image=A__    ,   strength=0.8_5    ,   generator=A__    ,   negative_prompt=''''''    ,   ).to_tuple()

      snake_case				=			pipeline(
          image=A__    ,   image_embeds=A__    ,   negative_image_embeds=A__    ,   hint=A__    ,   generator=A__    ,   num_inference_steps=1_00    ,   height=5_12    ,   width=5_12    ,   strength=0.5    ,   output_type='''np'''    ,   )

      snake_case				=			output.images[0]

      assert image.shape == (5_12, 5_12, 3)

      assert_mean_pixel_difference(A__    ,   A__    )








'''simple docstring'''







def    __UpperCamelCase	(	a    : int					,   a    : int     ) ->int:
 while b:
  snake_case				,		snake_case				=			b, a % b
 return a

def    __UpperCamelCase	(	a    : int					,   a    : int     ) ->int:
 return a if b == 0 else euclidean_gcd_recursive(a					,   a % b     )

def    __UpperCamelCase	(	) ->Optional[Any]:
 print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3					,   5     )}"""     )
 print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5					,   3     )}"""     )
 print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1					,   3     )}"""     )
 print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3					,   6     )}"""     )
 print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6					,   3     )}"""     )

 print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,   5     )}"""     )
 print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,   3     )}"""     )
 print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,   3     )}"""     )
 print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,   6     )}"""     )
 print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,   3     )}"""     )


if __name__ == "__main__":
   main()

