


"""simple docstring"""







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 						_A  (  __UpperCamelCase   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Dict     =	KandinskyVaaImgaImgPipeline
    UpperCAmelCase	:							Union[str, Any]     =	["""image_embeds""", """negative_image_embeds""", """image"""]
    UpperCAmelCase	:							str     =	[
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    UpperCAmelCase	:							int     =	[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase	:							Tuple     =	False






    @property
    def      __snake_case							( self    :   Any):
      return 32






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      return 32






    @property
    def      __snake_case							( self    :   int):
      return self.time_input_dim






    @property
    def      __snake_case							( self    :   List[str]):
      return self.time_input_dim * 4






    @property
    def      __snake_case							( self    :   Dict):
      return 100






    @property
    def      __snake_case							( self    :   Optional[Any]):
      torch.manual_seed(0)

      a						:		Dict											=      {
          '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						:		Any											=      UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def      __snake_case							( self    :   Any):
      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      __snake_case							( self    :   str):
      torch.manual_seed(0)
      a						:		Tuple											=      VQModel(**self.dummy_movq_kwargs)
      return model






    def      __snake_case							( self    :   str):
      a						:		Union[str, Any]											=      self.dummy_unet
      a						:		Union[str, Any]											=      self.dummy_movq

      a						:		Optional[int]											=      {
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      a						:		Tuple											=      DDIMScheduler(**__UpperCAmelCase)

      a						:		Any											=      {
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






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

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]											=      torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		str											=      torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Optional[int]											=      {
          '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      __snake_case							( self    :   List[str]):
      a						:		List[Any]											=      'cpu'

      a						:		Optional[int]											=      self.get_dummy_components()

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

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]											=      pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Dict											=      output.images

      a						:		List[Any]											=      pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)    ,			return_dict=__UpperCAmelCase    ,			)[0]

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

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

      a						:		str											=      np.array(
          [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263])
      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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




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






    def      __snake_case							( self    :   Any):
      a						:		int											=      load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_img2img_frog.npy")

      a						:		int											=      load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
      a						:		Dict											=      'A red cartoon frog, 4k'

      a						:		List[Any]											=      KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"    ,			torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		Any											=      KandinskyVaaImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-decoder"    ,			torch_dtype=torch.floataa)
      a						:		Optional[Any]											=      pipeline.to(__UpperCAmelCase)

      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]											=      torch.Generator(device="cpu").manual_seed(0)
      a						:		Tuple											=      pipe_prior(
          __UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			num_inference_steps=5    ,			negative_prompt=""    ,			).to_tuple()

      a						:		Dict											=      pipeline(
          image=__UpperCAmelCase    ,			image_embeds=__UpperCAmelCase    ,			negative_image_embeds=__UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			num_inference_steps=100    ,			height=768    ,			width=768    ,			strength=0.2    ,			output_type="np"    ,			)

      a						:		int											=      output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase    ,			__UpperCAmelCase)










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






