
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        UpperCAmelCase_			( __lowerCamelCase  ,       unittest.TestCase     ):


  '''simple docstring'''







  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 							_lowercase							( self			: str    )			->	Any:







    """simple docstring"""






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







    """simple docstring"""






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







    """simple docstring"""






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







    """simple docstring"""






    return self.time_input_dim * 4
  @property
  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    return 100
  @property
  def 							_lowercase							( self			: Optional[Any]    )			->	Any:







    """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			: Any    )			->	List[str]:







    """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			: int    )			->	Optional[int]:







    """simple docstring"""






    torch.manual_seed(0    )
    __magic_name__				     =       VQModel(**self.dummy_movq_kwargs    )
    return model
  def 							_lowercase							( self			: Optional[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       self.dummy_unet
    __magic_name__				     =       self.dummy_movq

    __magic_name__				     =       {
        """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,
    }

    __magic_name__				     =       DDIMScheduler(**UpperCamelCase_    )

    __magic_name__				     =       {
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[int]=0    )			->	int:







    """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_    )
    # create init_image
    __magic_name__				     =       floats_tensor((1, 3, 64, 64)			,							rng=random.Random(UpperCamelCase_    )    ).to(UpperCamelCase_    )
    __magic_name__				     =       image.cpu().permute(0			,							2			,							3			,							1    )[0]
    __magic_name__				     =       Image.fromarray(np.uinta(UpperCamelCase_    )    ).convert("""RGB"""    ).resize((256, 256)    )

    if str(UpperCamelCase_    ).startswith("""mps"""    ):
      __magic_name__				     =       torch.manual_seed(UpperCamelCase_    )
    else:
      __magic_name__				     =       torch.Generator(device=UpperCamelCase_    ).manual_seed(UpperCamelCase_    )
    __magic_name__				     =       {
        """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 							_lowercase							( self			: Dict    )			->	Optional[int]:







    """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(
        [0.6199778, 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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Dict    )			->	Union[str, Any]:







    """simple docstring"""






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





  def 							_lowercase							( self			: Union[str, Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_img2img_frog.npy"""    )

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

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

    __magic_name__				     =       KandinskyVaaImgaImgPipeline.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__				     =       torch.Generator(device="""cpu"""    ).manual_seed(0    )
    __magic_name__     ,    __magic_name__				     =       pipe_prior(
        UpperCamelCase_			,							generator=UpperCamelCase_			,							num_inference_steps=5			,							negative_prompt=""""""			,							).to_tuple()

    __magic_name__				     =       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"""			,							)

    __magic_name__				     =       output.images[0]

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

    assert_mean_pixel_difference(UpperCamelCase_			,							UpperCamelCase_    )







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""   )

             if do_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

             # All transformations expect numpy arrays.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

             if do_rescale:
                    __lowerCamelCase   =						[self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_   ) for image in images]

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

