




from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      if b == 0:
            return (1, 0)
      ((_A)    ,     (_A))							:  Optional[Any]    =		extended_euclid(snake_case_,a % b  )
      _A							:  Optional[Any]    =		a // b
      return (y, x - k * y)







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_  ):
      ((_A)    ,     (_A))							:  Tuple    =		extended_euclid(snake_case_,snake_case_  )
      _A							:  str    =		na * na
      _A							:  Tuple    =		ra * x * na + ra * y * na
      return (n % m + m) % m







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      ((_A)    ,     (_A))							:  Tuple    =		extended_euclid(snake_case_,snake_case_  )
      if b < 0:
            _A							:  List[str]    =		(b % n + n) % n
      return b







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A    ,     _A							:  List[str]    =		invert_modulo(snake_case_,snake_case_  ), invert_modulo(snake_case_,snake_case_  )
      _A							:  Union[str, Any]    =		na * na
      _A							:  Optional[int]    =		ra * x * na + ra * y * na
      return (n % m + m) % m


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="chinese_remainder_theorem", verbose=True)
  testmod(name="chinese_remainder_theorem2", verbose=True)
  testmod(name="invert_modulo", verbose=True)
  testmod(name="extended_euclid", verbose=True)







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		KandinskyVaaImgaImgPipeline
     _a											=		["image_embeds", "negative_image_embeds", "image"]
     _a											=		[
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _a											=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _a											=		False

     @property
     def       a__			(						self       )		->							int:
           return 32

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return 32

     @property
     def       a__			(						self       )		->							List[str]:
           return self.time_input_dim

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return self.time_input_dim * 4

     @property
     def       a__			(						self       )		->							str:
           return 100

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )

           _A							:  str    =		{
               """in_channels""": 4,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 8,
               """addition_embed_type""": """image""",
               """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,
           }

           _A							:  Union[str, Any]    =		UNetaDConditionModel(**_a       )
           return model

     @property
     def       a__			(						self       )		->							int:
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["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",
               ],
               "vq_embed_dim": 4,
           }

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  Dict    =		VQModel(**self.dummy_movq_kwargs       )
           return model

     def       a__			(						self       )		->							int:
           _A							:  Any    =		self.dummy_unet
           _A							:  List[Any]    =		self.dummy_movq

           _A							:  str    =		{
               """num_train_timesteps""": 1000,
               """beta_schedule""": """linear""",
               """beta_start""": 0.00085,
               """beta_end""": 0.012,
               """clip_sample""": False,
               """set_alpha_to_one""": False,
               """steps_offset""": 0,
               """prediction_type""": """epsilon""",
               """thresholding""": False,
           }

           _A							:  int    =		DDIMScheduler(**_a       )

           _A							:  Tuple    =		{
               """unet""": unet,
               """scheduler""": scheduler,
               """movq""": movq,
           }

           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							str:
           _A							:  Optional[int]    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Dict    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1       )       ).to(
               _a       )
           # create init_image
           _A							:  Optional[Any]    =		floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Optional[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       )[0]
           _A							:  Optional[Any]    =		Image.fromarray(np.uinta(_a       )       ).convert("""RGB"""       ).resize((256, 256)       )

           if str(_a       ).startswith("""mps"""       ):
                 _A							:  Tuple    =		torch.manual_seed(_a       )
           else:
                 _A							:  str    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Optional[Any]    =		{
               """image""": init_image,
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """num_inference_steps""": 10,
               """guidance_scale""": 7.0,
               """strength""": 0.2,
               """output_type""": """np""",
           }
           return inputs



     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		"""cpu"""

           _A							:  int    =		self.get_dummy_components()

           _A							:  Optional[int]    =		self.pipeline_class(**_a       )
           _A							:  Any    =		pipe.to(_a       )

           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[Any]    =		pipe(**self.get_dummy_inputs(_a       )       )
           _A							:  Dict    =		output.images

           _A							:  List[str]    =		pipe(
               **self.get_dummy_inputs(_a       )	,  return_dict=_a	,  )[0]

           _A							:  Dict    =		image[0, -3:, -3:, -1]
           _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

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

           _A							:  Optional[int]    =		np.array(
               [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]       )
           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       a__			(						self       )		->							List[Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def       a__			(						self       )		->							List[str]:
           _A							:  Any    =		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_img2img_frog.npy"""       )

           _A							:  Union[str, Any]    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
           _A							:  Dict    =		"""A red cartoon frog, 4k"""

           _A							:  Dict    =		KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""	,  torch_dtype=torch.floataa       )
           pipe_prior.to(_a       )

           _A							:  int    =		KandinskyVaaImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder"""	,  torch_dtype=torch.floataa       )
           _A							:  Dict    =		pipeline.to(_a       )

           pipeline.set_progress_bar_config(disable=_a       )

           _A							:  Tuple    =		torch.Generator(device="""cpu"""       ).manual_seed(0       )
           _A    ,     _A							:  List[str]    =		pipe_prior(
               _a	,  generator=_a	,  num_inference_steps=5	,  negative_prompt=""""""	,  ).to_tuple()

           _A							:  int    =		pipeline(
               image=_a	,  image_embeds=_a	,  negative_image_embeds=_a	,  generator=_a	,  num_inference_steps=100	,  height=768	,  width=768	,  strength=0.2	,  output_type="""np"""	,  )

           _A							:  Optional[int]    =		output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(_a	,  _a       )

