


'''simple docstring'''




# flake8: noqa
# Lint as: python3
_a   :   Tuple												=   [
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental












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 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					KandinskyVaaPipeline
  lowercase_						:			Union[str, Any]						=					[
      '''image_embeds''',
      '''negative_image_embeds''',
  ]
  lowercase_						:			int						=					['''image_embeds''', '''negative_image_embeds''']
  lowercase_						:			str						=					[
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowercase_						:			str						=					False

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim * 4

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return 1_00

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     torch.manual_seed(0			)

     __UpperCamelCase	:			int					=					{
         """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	:			Any					=					UNetaDConditionModel(**lowerCamelCase__			)
     return model

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     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						:	int			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Tuple					=					VQModel(**self.dummy_movq_kwargs			)
     return model

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.dummy_unet
     __UpperCamelCase	:			Optional[int]					=					self.dummy_movq

     __UpperCamelCase	:			List[str]					=					DDIMScheduler(
         num_train_timesteps=10_00   ,				beta_schedule="""linear"""   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				steps_offset=1   ,				prediction_type="""epsilon"""   ,				thresholding=lowerCamelCase__   ,				)

     __UpperCamelCase	:			str					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1			)			).to(
         lowerCamelCase__			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Optional[int]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			int					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					{
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""

     __UpperCamelCase	:			Dict					=					self.get_dummy_components()

     __UpperCamelCase	:			Union[str, Any]					=					self.pipeline_class(**lowerCamelCase__			)
     __UpperCamelCase	:			int					=					pipe.to(lowerCamelCase__			)

     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[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[str]					=					image[0, -3:, -3:, -1]
     __UpperCamelCase	:			Tuple					=					image_from_tuple[0, -3:, -3:, -1]

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

     __UpperCamelCase	:			int					=					np.array(
         [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]			)

     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 ):

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

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

  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""			)

     __UpperCamelCase	:			List[str]					=					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	:			Optional[int]					=					pipeline.to(lowerCamelCase__			)
     pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					"""red cat, 4k photo"""

     __UpperCamelCase	:			Tuple					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					pipe_prior(
         lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=5   ,				negative_prompt=""""""   ,				).to_tuple()

     __UpperCamelCase	:			Any					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					pipeline(
         image_embeds=lowerCamelCase__   ,				negative_image_embeds=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=1_00   ,				output_type="""np"""   ,				)

     __UpperCamelCase	:			Any					=					output.images[0]

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

     assert_mean_pixel_difference(lowerCamelCase__   ,				lowerCamelCase__			)



