
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        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		KandinskyVaaPipeline
  a__           =		[
      """image_embeds""",
      """negative_image_embeds""",
  ]
  a__           =		["""image_embeds""", """negative_image_embeds"""]
  a__           =		[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a__           =		False
  @property
  def 							_lowercase							( self			: Tuple    )			->	str:







    """simple docstring"""






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







    """simple docstring"""






    return 32
  @property
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    return self.time_input_dim
  @property
  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    return self.time_input_dim * 4
  @property
  def 							_lowercase							( self			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    return 100
  @property
  def 							_lowercase							( self			: Any    )			->	Dict:







    """simple docstring"""






    torch.manual_seed(0    )

    __magic_name__				     =       {
        """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,
    }

    __magic_name__				     =       UNetaDConditionModel(**UpperCamelCase__    )
    return model
  @property
  def 							_lowercase							( self			: List[Any]    )			->	Tuple:







    """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 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    torch.manual_seed(0    )
    __magic_name__				     =       VQModel(**self.dummy_movq_kwargs    )
    return model
  def 							_lowercase							( self			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       self.dummy_unet
    __magic_name__				     =       self.dummy_movq

    __magic_name__				     =       DDIMScheduler(
        num_train_timesteps=1000			,							beta_schedule="""linear"""			,							beta_start=0.00085			,							beta_end=0.012			,							clip_sample=UpperCamelCase__			,							set_alpha_to_one=UpperCamelCase__			,							steps_offset=1			,							prediction_type="""epsilon"""			,							thresholding=UpperCamelCase__			,							)

    __magic_name__				     =       {
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }
    return components
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Any=0    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       floats_tensor((1, self.text_embedder_hidden_size)			,							rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )
    __magic_name__				     =       floats_tensor((1, self.text_embedder_hidden_size)			,							rng=random.Random(seed + 1    )    ).to(
        UpperCamelCase__    )
    if str(UpperCamelCase__    ).startswith("""mps"""    ):
      __magic_name__				     =       torch.manual_seed(UpperCamelCase__    )
    else:
      __magic_name__				     =       torch.Generator(device=UpperCamelCase__    ).manual_seed(UpperCamelCase__    )
    __magic_name__				     =       {
        """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 							_lowercase							( self			: Optional[int]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """cpu"""

    __magic_name__				     =       self.get_dummy_components()

    __magic_name__				     =       self.pipeline_class(**UpperCamelCase__    )
    __magic_name__				     =       pipe.to(UpperCamelCase__    )

    pipe.set_progress_bar_config(disable=UpperCamelCase__    )

    __magic_name__				     =       pipe(**self.get_dummy_inputs(UpperCamelCase__    )    )
    __magic_name__				     =       output.images

    __magic_name__				     =       pipe(
        **self.get_dummy_inputs(UpperCamelCase__    )			,							return_dict=UpperCamelCase__			,							)[0]

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

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

    __magic_name__				     =       np.array(
        [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]    )

    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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Optional[int]:







    """simple docstring"""






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





  def 							_lowercase							( self			: Tuple    )			->	Any:







    """simple docstring"""






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

    __magic_name__				     =       KandinskyVaaPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior"""			,							torch_dtype=torch.floataa    )
    pipe_prior.to(UpperCamelCase__    )

    __magic_name__				     =       KandinskyVaaPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-decoder"""			,							torch_dtype=torch.floataa    )
    __magic_name__				     =       pipeline.to(UpperCamelCase__    )
    pipeline.set_progress_bar_config(disable=UpperCamelCase__    )

    __magic_name__				     =       """red cat, 4k photo"""

    __magic_name__				     =       torch.Generator(device="""cuda"""    ).manual_seed(0    )
    __magic_name__     ,    __magic_name__				     =       pipe_prior(
        UpperCamelCase__			,							generator=UpperCamelCase__			,							num_inference_steps=5			,							negative_prompt=""""""			,							).to_tuple()

    __magic_name__				     =       torch.Generator(device="""cuda"""    ).manual_seed(0    )
    __magic_name__				     =       pipeline(
        image_embeds=UpperCamelCase__			,							negative_image_embeds=UpperCamelCase__			,							generator=UpperCamelCase__			,							num_inference_steps=100			,							output_type="""np"""			,							)

    __magic_name__				     =       output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(UpperCamelCase__			,							UpperCamelCase__    )




"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




