






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		str									=KandinskyVaaPipeline
       a    :		List[str]									=[
           '''image_embeds''',
           '''negative_image_embeds''',
       ]
       a    :		List[str]									=['''image_embeds''', '''negative_image_embeds''']
       a    :		Optional[int]									=[
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       a    :		Optional[Any]									=False





       @property
       def       _a   ( self							):
         return 3_2





       @property
       def       _a   ( self							):
         return 3_2





       @property
       def       _a   ( self							):
         return self.time_input_dim





       @property
       def       _a   ( self							):
         return self.time_input_dim * 4





       @property
       def       _a   ( self							):
         return 1_0_0





       @property
       def       _a   ( self							):
         torch.manual_seed(0							)

         UpperCamelCase_: Union[str, Any]   				=    {
             '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,
         }

         UpperCamelCase_: List[str]   				=    UNetaDConditionModel(**_lowerCamelCase							)
         return model





       @property
       def       _a   ( self							):
         return {
             "block_out_channels": [3_2, 6_4],
             "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": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }





       @property
       def       _a   ( self							):
         torch.manual_seed(0							)
         UpperCamelCase_: Tuple   				=    VQModel(**self.dummy_movq_kwargs							)
         return model





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.dummy_unet
         UpperCamelCase_: Tuple   				=    self.dummy_movq

         UpperCamelCase_: Union[str, Any]   				=    DDIMScheduler(
             num_train_timesteps=1_0_0_0  ,      beta_schedule='linear'  ,      beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      clip_sample=_lowerCamelCase  ,      set_alpha_to_one=_lowerCamelCase  ,      steps_offset=1  ,      prediction_type='epsilon'  ,      thresholding=_lowerCamelCase  ,      )

         UpperCamelCase_: int   				=    {
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=0							):
         UpperCamelCase_: int   				=    floats_tensor((1, self.text_embedder_hidden_size)  ,      rng=random.Random(_lowerCamelCase							)							).to(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    floats_tensor((1, self.text_embedder_hidden_size)  ,      rng=random.Random(seed + 1							)							).to(
             _lowerCamelCase							)
         if str(_lowerCamelCase							).startswith('mps'							):
           UpperCamelCase_: Any   				=    torch.manual_seed(_lowerCamelCase							)
         else:
           UpperCamelCase_: List[Any]   				=    torch.Generator(device=_lowerCamelCase							).manual_seed(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    {
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 6_4,
             'width': 6_4,
             'guidance_scale': 4.0,
             'num_inference_steps': 2,
             'output_type': 'np',
         }
         return inputs





       def       _a   ( self							):
         UpperCamelCase_: str   				=    'cpu'

         UpperCamelCase_: List[str]   				=    self.get_dummy_components()

         UpperCamelCase_: Dict   				=    self.pipeline_class(**_lowerCamelCase							)
         UpperCamelCase_: str   				=    pipe.to(_lowerCamelCase							)

         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Optional[Any]   				=    pipe(**self.get_dummy_inputs(_lowerCamelCase							)							)
         UpperCamelCase_: List[Any]   				=    output.images

         UpperCamelCase_: int   				=    pipe(
             **self.get_dummy_inputs(_lowerCamelCase							)  ,      return_dict=_lowerCamelCase  ,      )[0]

         UpperCamelCase_: List[Any]   				=    image[0, -3:, -3:, -1]
         UpperCamelCase_: int   				=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         UpperCamelCase_: Optional[int]   				=    np.array(
             [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







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





       def       _a   ( self							):
         UpperCamelCase_: str   				=    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'							)

         UpperCamelCase_: Union[str, Any]   				=    KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'  ,      torch_dtype=torch.floataa							)
         pipe_prior.to(_lowerCamelCase							)

         UpperCamelCase_: int   				=    KandinskyVaaPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-decoder'  ,      torch_dtype=torch.floataa							)
         UpperCamelCase_: Any   				=    pipeline.to(_lowerCamelCase							)
         pipeline.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Tuple   				=    'red cat, 4k photo'

         UpperCamelCase_: List[Any]   				=    torch.Generator(device='cuda'							).manual_seed(0							)
         UpperCamelCase_: Optional[Any]   				=    pipe_prior(
             _lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=5  ,      negative_prompt=''  ,      ).to_tuple()

         UpperCamelCase_: Tuple   				=    torch.Generator(device='cuda'							).manual_seed(0							)
         UpperCamelCase_: List[str]   				=    pipeline(
             image_embeds=_lowerCamelCase  ,      negative_image_embeds=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=1_0_0  ,      output_type='np'  ,      )

         UpperCamelCase_: Any   				=    output.images[0]

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

         assert_mean_pixel_difference(_lowerCamelCase  ,      _lowerCamelCase							)











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

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

def   snake_case ()	->    int:
  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()